Llama-3 8b Unsloth finetuning.ipynb

{"cells":[{"cell_type":"markdown","source":["To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n","<div class=\"align-center\">\n","  <a href=\"https://github.com/unslothai/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n","  <a href=\"https://discord.gg/u54VK8m8tk\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n","  <a href=\"https://ko-fi.com/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Kofi button.png\" width=\"145\"></a></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n","</div>\n","\n","To install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n","\n","You will learn how to do [data prep](#Data), how to [train](#Train), how to [run the model](#Inference), & [how to save it](#Save) (eg for Llama.cpp).\n","\n","**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**\n","\n","Use our [Llama-3 8b Instruct](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) notebook for conversational style finetunes."],"metadata":{"id":"IqM-T1RTzY6C"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"2eSvM9zX_2d3","executionInfo":{"status":"ok","timestamp":1717604602858,"user_tz":420,"elapsed":40045,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}}},"outputs":[],"source":["%%capture\n","# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n","!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n","!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes"]},{"cell_type":"markdown","source":["* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n","* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n","* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n","* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n","* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)"],"metadata":{"id":"r2v_X2fA0Df5"}},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":338,"referenced_widgets":["814f3b6a4f0a4be19b2a1ffdfce2f871","ab59d056cb744495ab9a8fe84258d54b","209010a2b3ef4f658dd828c2613b42b6","110035baa8a349fe9661445a9ae548e0","8c984c2c3b4e42a6bc267c7d4fb9e20c","7e64216c4d114912bddc513201e5a602","531a2339019c437395b54926ef04a0ec","e83fbeffb9fa4587b3b387ae3e9e779e","42e35d529df646b89c4ccbb650dbd976","d115e3c150934e45b37ce9f300cf9067","0df38029e6e1445ea5ecf04b191e80cf","e0ba11c171024e75bf9706164179f1db","9b6d2448519642af84b59909dbf8d770","9f64fbf9f3004cf698963fb8a6940fe9","ffe700302d634e46bf6f12b54d6aec60","4591fdcd75f945ff872099f43a7fb481","344d85f1f2104c58a8bc530d59802256","407d830ad1d24091934bc620233d0b35","8fada255d7b942f0b80fc90040e40e0f","282d968223a540ff9e79a69a9a86709c","c4d6573075154d36a1f2e4286120d54b","94a8ad81476c40a890f761fc57427e54","8b6ea3b7819a4473855c1b1930a77c87","b3ec3f3ecd14434dbd00d3c69e905156","a1f1a169bcdd4d9d8fd4c07851bf7ce7","5f49c2061dbf4bb8b6b757d284b560c8","a32c023dd8e341ef88b9606423000d65","17241d0872cf46d8967924d556e91a13","3a7966deac964203b31f30c36b8b3018","ed423af8d49e40328d24a9a012605ce0","a56d0ce92c3a46fdaf6b3189377a388d","89e2256eeefa47629e316600ce3d0913","dd3013af89d64e4e997154ac8b6aaab2","5a0e629bcd98425ab442cd1659ee2d11","015962727bf94bbe8248a2e4ce5904f9","a14d5848950c48ae8e3924bb505ef64b","2b5d281e56954c65a0fecf06fc219f42","51c0611c9e0749de823b6192575e4c59","0f6a5320447b4e1ab73fb2c07f11db6b","f51549cd7985475cae49a7fe0b332ab4","594748f5f64d4cd3a561ec12559f632c","b587481d85c641fe82917ab6e9b857eb","fed6ce8603f64f93a6b1b364bb629bc6","cd1ba8f9cece4452afa0c09b4a855dbb","66f833b67a2a477ab232ea07f8222173","8d520dc3bed74e74bb5fb6af0d8f2863","6bf39b13f5f142a880a7883ea43aec25","44bb18fe04754582a84056e95b6814b1","a715644e44be4d008feed7c9ad5398f1","33c00a4887094727b1b1b0d3fc50c50f","60dc4e77f9bd4a649b0f28ca692360fe","2a46a6a5e85d4a9295f6c941537d3580","f936a13bf58041d5be78c5cb90073a61","8951f8a176cb4abe9c33e568af39ae49","87568bf422e749dca7cccad914ef216c","82a2cf932f0e43aea1f803825dd8f1cf","955f724c9a454aeeb346995b3ad3e256","43e3b3b8e36345848a78fe455c2e40d7","e03a2e11d7824b46a3a877909f3d32fe","0a8f0f18a48e466cb48e7f033870dcfe","30c56dc2acba46a18f143426370c0f34","eeff50398df84340a4a239139448c732","84d6d9d3beae4472ba0d5935c5976098","7ae52d6cad7f439089f47a9446167297","793ad71acdef4752a54aebfce5544b2c","b2bbe32ecd974b7585ae361b3601171c"]},"id":"QmUBVEnvCDJv","executionInfo":{"status":"ok","timestamp":1717604647203,"user_tz":420,"elapsed":38894,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"665005ae-c038-4e43-d7f7-f3ac5acead5c"},"outputs":[{"output_type":"stream","name":"stdout","text":["🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n"]},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"814f3b6a4f0a4be19b2a1ffdfce2f871"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["==((====))==  Unsloth: Fast Llama patching release 2024.5\n","   \\\\   /|    GPU: NVIDIA L4. Max memory: 22.168 GB. Platform = Linux.\n","O^O/ \\_/ \\    Pytorch: 2.3.0+cu121. CUDA = 8.9. CUDA Toolkit = 12.1.\n","\\        /    Bfloat16 = TRUE. Xformers = 0.0.26.post1. FA = False.\n"," \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/5.70G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e0ba11c171024e75bf9706164179f1db"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/172 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8b6ea3b7819a4473855c1b1930a77c87"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/50.6k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a0e629bcd98425ab442cd1659ee2d11"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"66f833b67a2a477ab232ea07f8222173"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/464 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82a2cf932f0e43aea1f803825dd8f1cf"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"]}],"source":["from unsloth import FastLanguageModel\n","import torch\n","max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n","dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n","load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n","\n","# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n","fourbit_models = [\n","    \"unsloth/mistral-7b-v0.3-bnb-4bit\",      # New Mistral v3 2x faster!\n","    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n","    \"unsloth/llama-3-8b-bnb-4bit\",           # Llama-3 15 trillion tokens model 2x faster!\n","    \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n","    \"unsloth/llama-3-70b-bnb-4bit\",\n","    \"unsloth/Phi-3-mini-4k-instruct\",        # Phi-3 2x faster!\n","    \"unsloth/Phi-3-medium-4k-instruct\",\n","    \"unsloth/mistral-7b-bnb-4bit\",\n","    \"unsloth/gemma-7b-bnb-4bit\",             # Gemma 2.2x faster!\n","] # More models at https://huggingface.co/unsloth\n","\n","model, tokenizer = FastLanguageModel.from_pretrained(\n","    model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n","    max_seq_length = max_seq_length,\n","    dtype = dtype,\n","    load_in_4bit = load_in_4bit,\n","    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",")"]},{"cell_type":"markdown","source":["We now add LoRA adapters so we only need to update 1 to 10% of all parameters!"],"metadata":{"id":"SXd9bTZd1aaL"}},{"cell_type":"code","execution_count":3,"metadata":{"id":"6bZsfBuZDeCL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717604652015,"user_tz":420,"elapsed":4819,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"bff9e182-1bf7-45e7-dcbc-7ef634fc9ab5"},"outputs":[{"output_type":"stream","name":"stderr","text":["Unsloth 2024.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n"]}],"source":["model = FastLanguageModel.get_peft_model(\n","    model,\n","    r = 16, # Choose any number > 0 ! Suggested 8, 16, 32, 64, 128\n","    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n","                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n","    lora_alpha = 16,\n","    lora_dropout = 0, # Supports any, but = 0 is optimized\n","    bias = \"none\",    # Supports any, but = \"none\" is optimized\n","    # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n","    use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n","    random_state = 3407,\n","    use_rslora = False,  # We support rank stabilized LoRA\n","    loftq_config = None, # And LoftQ\n",")"]},{"cell_type":"markdown","source":["<a name=\"Data\"></a>\n","### Data Prep\n","We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n","\n","**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n","\n","**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n","\n","If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n","\n","For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)."],"metadata":{"id":"vITh0KVJ10qX"}},{"cell_type":"code","execution_count":4,"metadata":{"id":"LjY75GoYUCB8","colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["95e8938ced1747918028cb2a6d3cd208","9c098beaad104b668e310efb2a5e3412","396ab7fe491e43f58d40d2b61cf574a0","ddf02bd0c7aa45768a3ce1487ff60cf1","e12ae05be172427691cc97546917fa02","57e034a9b2474624b98196f11a2858e1","385aec3609344c8994519ac2c5964cc2","b9451f952e8a49c9b3c30003a73d33a6","ea81c16acb884dc1bfce08f67b660239","a8a0c8b5714b40e4a6d6d9730586a858","deeb92f5d8664a6faf1e9e5785f32bd0","c3580090bc2942ec8608199fa2dfb8e4","2d00a852cb444980a45868d7ede5c3c4","bc3262757b7442a188fca8f79950c1fc","ca211a1346d24741a2379da51f555d6b","e13b97535c1c4c838834a4b647184ffc","5f79c543236d4db38e163cf8d82e367f","7e78fd5f672249d4ac967cdf7a200419","20f5f8a843a94e158c0f2253b3809285","1f07f702fbd447e8a55dcd32d3e7dbc6","dc0df4a379554b3c93a14b3819b8d9e9","9fb0cfe57edc4d7d8081b076b694d921","a0557c4ba0cb44e782c61c37b3700f90","adea633ee1a84616b3d5684f02f654a8","0e580f3e488941bd9b3ada89f2a0076d","768f1a3321d448fa852b7e2a1679d354","3a068fc011414d87a943b17f04b89013","7610696ce6a543b6a0db0963832a611e","227d7f47212e4f61ac3ec58c1e0a2800","0b806c19532c4cc3905213f851b22a6c","5761469fa962431fb3ead639363a678c","e7f7941505c04d589886f5344aab6f23","38cf99cae5c4445ca39ecd847d6d9d67","62b79ef9b90740fcad8521dad232df9a","ab5277e141314251ad0ffd947cbf090e","8a44256111244586ab803d3ad987072b","aa1e9939816744fbb7366f47844f7169","c0461d897a2544fa90dd1b5028b23861","603cddd141624eaf9edce1999113eb33","1d4118c01b51422fa5bafd30d5d8320e","6de39750886841b2b0c076ee04e68d09","b421928fdf974b9e8604c7f159773d21","a7495ccfaf5d4623bb055e82efa71543","7844302ad44a48c5beb619cf23ee99f4"]},"executionInfo":{"status":"ok","timestamp":1717604658843,"user_tz":420,"elapsed":6835,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"7b83c502-43f0-4b4b-ae65-6e6a4fd94a50"},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/11.6k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"95e8938ced1747918028cb2a6d3cd208"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/44.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c3580090bc2942ec8608199fa2dfb8e4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/51760 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a0557c4ba0cb44e782c61c37b3700f90"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/51760 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"62b79ef9b90740fcad8521dad232df9a"}},"metadata":{}}],"source":["alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n","\n","### Instruction:\n","{}\n","\n","### Input:\n","{}\n","\n","### Response:\n","{}\"\"\"\n","\n","EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n","def formatting_prompts_func(examples):\n","    instructions = examples[\"instruction\"]\n","    inputs       = examples[\"input\"]\n","    outputs      = examples[\"output\"]\n","    texts = []\n","    for instruction, input, output in zip(instructions, inputs, outputs):\n","        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n","        text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n","        texts.append(text)\n","    return { \"text\" : texts, }\n","pass\n","\n","from datasets import load_dataset, Dataset\n","dataset = load_dataset(\"yahma/alpaca-cleaned\", split = \"train\")\n","dataset = dataset.map(formatting_prompts_func, batched = True,)"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"EsobCrCXCQ1j","executionInfo":{"status":"ok","timestamp":1717604710384,"user_tz":420,"elapsed":51545,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"9f85c59b-a893-42e2-db80-5ad682e30784"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["%load_ext autoreload\n","%autoreload 2"],"metadata":{"id":"glzTzXdHCVyh","executionInfo":{"status":"ok","timestamp":1717604710386,"user_tz":420,"elapsed":7,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["spc_train = load_dataset('json', data_files='/content/drive/MyDrive/CS 263/Synthetic-Persona-Chat_train3.jsonl', split='train')\n","spc_eval = load_dataset('json', data_files='/content/drive/MyDrive/CS 263/Synthetic-Persona-Chat_valid.jsonl', split='train[:50]')\n","spc_test = load_dataset('json', data_files='/content/drive/MyDrive/CS 263/Synthetic-Persona-Chat_valid.jsonl', split='train[50:100]')"],"metadata":{"id":"wulP0Cp_CY9l","colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["a9612f61590c41d1be3fef6d26a0ec69","f6508492e04e4a099f8e145dcace5d08","9d268e546441405f986f78ca7c132d10","0240738b95024890a51e89f52eb1f268","c2bc5770ce3d4f05a394fc3c90e85c88","0c76d1f9ae8b411384ac8b0e31c8e943","1850b98b0fb643fe8f47149bcde70312","3e2503a433bd479d8731e21c360099b2","35c8426f1d6e41888836fc7be6785fb2","53e37bba0db645bbbfb4c8bc7bec1b4a","6b780c1f4a2245f2bd702b3866ba974f","de43c5c4905d4919aedb495d9aedaff7","c12659ab02ff4ec3950a064f06cb4a98","9f2b88443f5c4229b82dc8a5ef890ac4","6aa3600b48c74cad8564dabfbf0edd64","f63c4482eb0f43a98e96b5ee98a5b2f0","b18cba10d047470e8715a71987018c61","b346bb06e4134824aa8341b7fb77e793","0e734612e27c4a8a8cced59db25ea168","0be3bac73c7b42c88fb386d0e6efeaef","3cba2e7bb838475a9a7b480c8e5b44e0","e8ff0db4ddfb491d8d450ae84c2a8674"]},"executionInfo":{"status":"ok","timestamp":1717604723019,"user_tz":420,"elapsed":12640,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"24027c43-8870-4e5f-b155-b95bf12c3fb7"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a9612f61590c41d1be3fef6d26a0ec69"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de43c5c4905d4919aedb495d9aedaff7"}},"metadata":{}}]},{"cell_type":"code","source":["spc_prompt = \"\"\"{}\n","\n","{}\n","\n","Here is the expected response:\n","{}\"\"\"\n","\n","EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n","def spc_formatting_prompts_func(messages, istest=False):\n","    systems = [messages['messages'][i][0]['content'] for i in range(len(messages['messages']))]\n","    users = [messages['messages'][i][1]['content'] for i in range(len(messages['messages']))]\n","    assistants = [messages['messages'][i][2]['content'] if not istest else \"\" for i in range(len(messages['messages']))]\n","    texts = []\n","    for system, user, assistant in zip(systems, users, assistants):\n","        # Must add EOS_TOKEN, otherwise your generation will go on forever!\n","        if istest:\n","          text = spc_prompt.format(system, user, assistant)\n","        else:\n","          text = spc_prompt.format(system, user, assistant) + EOS_TOKEN\n","        texts.append(text)\n","    return { \"text\" : texts, }\n","pass\n","\n","train_dataset = spc_train.map(spc_formatting_prompts_func, batched = True,)\n","eval_dataset = spc_eval.map(spc_formatting_prompts_func, batched = True,)\n","test_dataset = spc_test.map(lambda x: spc_formatting_prompts_func(x, True), batched = True,)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["53ec4685be4f4fa8b267f7048167d970","97626cdcd7694245b206e3cd16de0644","7935a60252de48f69b72ca52fddb52bb","db40adc5bd754a81acf0dad5e02f0530","b5780fc2b4764179b7c8d1c871757e02","58b9e835ace1488893a57e5bd4825d03","dbbfdb95226a442b9dc0eaada30d36ea","8c7e3405c40041ada7874990c6a95908","ea19b792ac6a45dca433a81e7436540f","2627c9ef2cf9422b968c88ffe79ab5da","8bdf72bb6bb147f78c639054ef8caa39","f163ea76730648cebfa45140e673bedf","b3433998aa494b12a96b10fe8f482b3b","51c88441408a4e46aeefaafbbeb010b1","67136dbc62204909afcb30cce0f226a3","fc78b17e93c549878ac3647095501622","a0e4137c75d64392adafedbb5dd16076","7d8000293f034adbaffc5fd810e5dd74","27e53232445142aba127278bb8f93632","1406a5d5e2a9497fbd6a6a9ae07d8e1f","179291a12b844743b06f2a2052e7f85b","22675758712349f7ac721a2f8137dfe7","6ea017ee986a42d6b6cf1cdcc2ea0518","57ef7861e63b45ad846712025e2d23c7","ecaf4e0ce70c4a7c8c9788c55a39a802","50ea21de1de244f5ba5549dc389da8e1","a0505e54b3ec4bfcbe0cbce75cfcfaa8","e18cc54b724d431c8c76544f79b9802d","d0466af6c2d94a65a972c0d6d607b8fb","c4a9cc9aa0c548758c04d5eba9be7b00","1d37e76a59084314994447dcb4f05048","2c12109d0d354c9a813540578920ced6","96f569e1f1674515b13bdc7c75008e77"]},"id":"RAWSH8O3Cfww","executionInfo":{"status":"ok","timestamp":1717604723020,"user_tz":420,"elapsed":14,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"4c67f1aa-cf54-48b2-a3d3-f943ab00a625"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/8589 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53ec4685be4f4fa8b267f7048167d970"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/50 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f163ea76730648cebfa45140e673bedf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/50 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6ea017ee986a42d6b6cf1cdcc2ea0518"}},"metadata":{}}]},{"cell_type":"code","source":["print([assistant['content'] for system, user, assistant in spc_test['messages']])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GMp3q0_uSObn","executionInfo":{"status":"ok","timestamp":1717603995801,"user_tz":420,"elapsed":5,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"705d6003-30aa-49eb-e0a0-a71a74e1e49c"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["[\"User 2: It's a lot of fun, but it's also a lot of hard work.\", 'User 2: Oh, that sounds so fun! I hope you have a great time.', \"User 2: Me too! What's your favorite piece of classical music?\", \"User 2: You definitely should! It's a must-try.\", \"User 2: I love helping people find what they're looking for. It's so satisfying to see someone's face light up when they find a book they've been looking for.\", \"User 2: It's a beautiful country. I'm sure you'd love it.\", 'User 2: It is. I love helping people.', \"User 2: I totally agree! And it's a great way to see new places.\", 'User 2: I like country music.', 'User 2: Me too! I read a lot of history and science books.', \"User 2: Oh, that's cool! I like to read science fiction and fantasy.\", 'User 2: So, what do you like to do for fun?', \"User 2: That's awesome! I've always wanted to learn how to play an instrument.\", \"User 2: I like the camaraderie. It's really nice to be around people who understand what you're going through.\", 'User 2: I like to read, grow roses in my garden, and spend time with my friends and family.', 'User 2: I like those genres too! I just finished reading a really good book called The Martian by Andy Weir.', \"User 2: That sounds like a lot of fun! I'm sure you'll be a great dog.\", \"User 2: That's interesting! I've never heard of dog agility classes.\", \"User 2: Me too. It's so beautiful.\", 'User 2: So, what else do you like to do for fun?', 'User 2: Meditation can help with that. It can teach you how to relax and focus on the present moment.', \"User 2: That sounds like a great job. I'm sure you help a lot of people.\", 'User 2: I might just do that. Thanks for the encouragement!', \"User 2: I think it's important to experience different places and cultures.\", 'User 2: Not much, just relaxing and watching TV.', \"User 2: That's cool. I'm not very good at it, but I still have a lot of fun.\", 'User 2: I like to play soccer, hang out with my friends, and play video games.', 'User 2: I like rock music too. What are some of your favorite bands?', 'User 2: I like my social studies class.', \"User 2: That sounds lovely. I'm sure it will be a beautiful day.\", 'User 2: Nice to meet you too, Cloud.', \"User 2: I'm a car salesman.\", \"User 2: But it's a good job, and I make a good living.\", \"User 2: Folk music? What's that?\", \"User 2: I've heard good things about that game.\", \"User 2: My favorite part is watching the bees work together to create honey. It's amazing to see how they can all work together so efficiently.\", \"User 2: It's a lot of work, but it's also a lot of fun.\", \"User 2: I'd be happy to help you out if you need it.\", \"User 2: I don't really play with dolls anymore. I'm more into sports.\", \"User 2: Oh, that sounds interesting. I'm not really sure what I want to do yet.\", \"User 2: You should definitely try it! It's a great way to get some exercise and see some beautiful scenery.\", 'User 2: Thanks! I like to think I look young for my age.', \"User 2: Me too! It's so much fun.\", \"User 2: That's cool.\", 'User 2: Really? What made you stop?', \"User 2: I'm a lawyer.\", \"User 2: That's great! I'm glad you're safe now.\", 'User 2: I have a golden retriever named Bailey, a poodle named Charlie, and a chihuahua named Toby.', \"User 2: Me neither! It's always nice to meet someone who shares my love of horses.\", \"User 2: You should try it! It's a lot of fun.\"]\n"]}]},{"cell_type":"markdown","source":["<a name=\"Train\"></a>\n","### Train the model\n","Now let's use Huggingface TRL's `SFTTrainer`! More docs here: [TRL SFT docs](https://huggingface.co/docs/trl/sft_trainer). We do 60 steps to speed things up, but you can set `num_train_epochs=1` for a full run, and turn off `max_steps=None`. We also support TRL's `DPOTrainer`!"],"metadata":{"id":"idAEIeSQ3xdS"}},{"cell_type":"code","execution_count":9,"metadata":{"id":"95_Nn-89DhsL","colab":{"base_uri":"https://localhost:8080/","height":193,"referenced_widgets":["c1c2b45cd4734368819f8a6238fa2a02","4a8f7c63bbf34bf4b34c50fa805525c4","885a42e7048044be8ca64193c35d8674","2ff7da6142da408bb558a1dfbe0883ca","52ef3f3f1b3c462188e498c092e4c102","5b0eccc31f1448baaffa98aabdf57305","f6dfcf3313924be4aa119d4a597191f2","f36c80d49ea4439b9459cd1687ed9506","47370ac6eda6401b94ec3374a63fd70d","21a8c9bf450e4abe9f858a76f17b644a","38c2dd48c5384d829dcc6b7c2e052d69","ccf71527048346ecb8425963c6c23282","51a480dafee94d0c9242bc4087468b4c","d3ece5a106eb4b86b6b3c3e3d9412214","95975cd9fb314c06b0b3ac9b6628a4f0","ce97c61f83244c0194488f09c8ba9151","90ab4e8fe0b34925bd912c20bcbc546e","5c658f8a83064548b7b6d48b90986c91","0d9088961b4240389f4f58e3b6b78d89","b8b403590dc94f86856168dd49db4beb","3dd7313a6e1c4464b083318cb221fdc9","6e56ddc1ecd34c69a230e1106e91c3d4"]},"executionInfo":{"status":"ok","timestamp":1717604743067,"user_tz":420,"elapsed":3679,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"cd9e8a45-5cca-41ee-cb50-84c5f6e0f612"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n","  warnings.warn(\n","/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n","  self.pid = os.fork()\n"]},{"output_type":"display_data","data":{"text/plain":["Map (num_proc=2):   0%|          | 0/8589 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c1c2b45cd4734368819f8a6238fa2a02"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map (num_proc=2):   0%|          | 0/50 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ccf71527048346ecb8425963c6c23282"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["max_steps is given, it will override any value given in num_train_epochs\n"]}],"source":["from trl import SFTTrainer\n","from transformers import TrainingArguments\n","from unsloth import is_bfloat16_supported\n","\n","trainer = SFTTrainer(\n","    model = model,\n","    tokenizer = tokenizer,\n","    train_dataset = train_dataset,\n","    eval_dataset = eval_dataset,\n","    dataset_text_field = \"text\",\n","    max_seq_length = max_seq_length,\n","    dataset_num_proc = 2,\n","    packing = False,\n","    args = TrainingArguments(\n","        per_device_train_batch_size = 2,\n","        gradient_accumulation_steps = 4,\n","        warmup_steps = 0,\n","        max_steps = 20,\n","        learning_rate = 2e-4,\n","        fp16 = not is_bfloat16_supported(),\n","        bf16 = is_bfloat16_supported(),\n","        logging_steps = 1,\n","        optim = \"adamw_8bit\",\n","        weight_decay = 0.01,\n","        lr_scheduler_type = \"linear\",\n","        seed = 3407,\n","        output_dir = \"outputs\",\n","        evaluation_strategy = \"steps\",\n","        eval_steps = 20\n","    ),\n",")"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"2ejIt2xSNKKp","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717604769167,"user_tz":420,"elapsed":1340,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"3075de11-5258-46ce-c4e9-2a6340902a51","cellView":"form"},"outputs":[{"output_type":"stream","name":"stdout","text":["GPU = NVIDIA L4. Max memory = 22.168 GB.\n","5.594 GB of memory reserved.\n"]}],"source":["#@title Show current memory stats\n","gpu_stats = torch.cuda.get_device_properties(0)\n","start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n","max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n","print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n","print(f\"{start_gpu_memory} GB of memory reserved.\")"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"yqxqAZ7KJ4oL","colab":{"base_uri":"https://localhost:8080/","height":199},"outputId":"8a5b8916-3783-4bd6-bd21-e37269b3ffb8","executionInfo":{"status":"ok","timestamp":1717604872898,"user_tz":420,"elapsed":100119,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["==((====))==  Unsloth - 2x faster free finetuning | Num GPUs = 1\n","   \\\\   /|    Num examples = 8,589 | Num Epochs = 1\n","O^O/ \\_/ \\    Batch size per device = 2 | Gradient Accumulation steps = 4\n","\\        /    Total batch size = 8 | Total steps = 20\n"," \"-____-\"     Number of trainable parameters = 41,943,040\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='20' max='20' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [20/20 01:27, Epoch 0/1]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>20</td>\n","      <td>0.524500</td>\n","      <td>0.505450</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}}],"source":["trainer_stats = trainer.train()"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"pCqnaKmlO1U9","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717604877095,"user_tz":420,"elapsed":464,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"c1e5d1c4-fbd4-4fff-a37e-9ca2f457b7e0","cellView":"form"},"outputs":[{"output_type":"stream","name":"stdout","text":["97.8827 seconds used for training.\n","1.63 minutes used for training.\n","Peak reserved memory = 9.703 GB.\n","Peak reserved memory for training = 4.109 GB.\n","Peak reserved memory % of max memory = 43.77 %.\n","Peak reserved memory for training % of max memory = 18.536 %.\n"]}],"source":["#@title Show final memory and time stats\n","used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n","used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n","used_percentage = round(used_memory         /max_memory*100, 3)\n","lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n","print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n","print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n","print(f\"Peak reserved memory = {used_memory} GB.\")\n","print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n","print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n","print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")"]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","\n","# Assuming `trainer` is your Trainer object.\n","train_losses = [log['loss'] for log in trainer.state.log_history if 'loss' in log]\n","train_steps = [log['step'] for log in trainer.state.log_history if 'loss' in log]\n","\n","val_losses = [log['eval_loss'] for log in trainer.state.log_history if 'eval_loss' in log]\n","val_steps = [log['step'] for log in trainer.state.log_history if 'eval_loss' in log]\n","\n","# Create a line plot for the training and validation losses.\n","plt.figure(figsize=(10, 5))\n","plt.plot(train_steps, train_losses, label='Training Loss')\n","plt.plot(val_steps, val_losses, label='Validation Loss', marker='o')  # Added markers for clarity\n","\n","plt.title('Training and Validation Losses')\n","plt.xlabel('Training Steps')\n","plt.ylabel('Loss')\n","plt.legend()\n","plt.grid(True)\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"pHDbNphg-gtr","executionInfo":{"status":"ok","timestamp":1717584014357,"user_tz":420,"elapsed":609,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"d940f39c-0e78-4798-b1f3-a3e20574e6d2"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"markdown","source":["<a name=\"Inference\"></a>\n","### Inference\n","Let's run the model! You can change the instruction and input - leave the output blank!"],"metadata":{"id":"ekOmTR1hSNcr"}},{"cell_type":"code","source":["import re\n","results = []\n","# spc_prompt = Copied from above\n","model.generation_config.pad_token_id = tokenizer.pad_token_id\n","FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n","for i in range(len(spc_test)):\n","  inputs = tokenizer(\n","  [\n","      spc_prompt.format(\n","          spc_test['messages'][i][0]['content'], # instruction\n","          spc_test['messages'][i][1]['content'], # input\n","          \"\", # output - leave this blank for generation!\n","      )\n","  ], return_tensors = \"pt\").to(\"cuda\")\n","\n","  outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n","  s = tokenizer.batch_decode(outputs)[0]\n","  results.append(re.search(r'Here is the expected response:\\n([^<]+)(<|$)', s).group(1))"],"metadata":{"id":"kR3gIAX-SM2q","executionInfo":{"status":"ok","timestamp":1717605147540,"user_tz":420,"elapsed":74062,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}}},"execution_count":15,"outputs":[]},{"cell_type":"code","source":["print(*results, sep='\\n\\n\\n')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fxmHjGFAPrP_","executionInfo":{"status":"ok","timestamp":1717605170790,"user_tz":420,"elapsed":525,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"4ea4d435-d008-4b3f-ef82-29d46592ce11"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["User 2: It's not that hard. You just need to practice a lot.\n","\n","\n","User 2: That's great! I've been to Disney World a few times. It's a lot of fun.\n","\n","\n","User 2: Really? I'm a huge fan of Beethoven.\n","\n","\n","User 2: I'm sure you'll love it!\n","\n","\n","User 2: I love helping people find the information they need. It's so satisfying to see them leave with a smile on their face.\n","\n","\n","User 2: It's a beautiful country with a lot of history and culture. I highly recommend it!\n","\n","\n","User 2: It is. I love helping people find the information they need.\n","\n","\n","User 2: I agree! It's a great workout and a lot of fun.\n","\n","\n","User 2: I love classical music.\n","\n","\n","User 2: I've been to Spain a few times. I love the food and the culture.\n","\n","\n","User 2: Oh, that's great! I love reading about history too. What's your favorite historical period to read about?\n","\n","\n","User 2: I'm sure you'll be great at it!\n","\n","\n","User 2: That's awesome! I bet you're really good at it.\n","\n","\n","User 2: I love the sense of camaraderie and the feeling of serving my country.\n","\n","\n","User 2: I like to read, garden, and go to the library.\n","\n","\n","User 2: Oh, cool! I love science fiction and fantasy too. I've read all of the Harry Potter books and I'm a big fan of the Lord of the Rings trilogy.\n","\n","\n","User 2: That's great! I'm sure you'll be a great dog.\n","\n","\n","User 2: Oh, that sounds like a lot of fun! I've never been to a dog agility class before.\n","\n","\n","User 2: Thank you! I love the way he sings that song.\n","\n","\n","User 2: What do you like to do for fun?\n","\n","\n","User 2: I know what you mean. That's why I practice Buddhism - it helps me to stay in the present and not worry about the future.\n","\n","\n","User 2: That's cool! I've always been interested in life coaching. I've been thinking about starting my own coaching business.\n","\n","\n","User 2: I'll think about it.\n","\n","\n","User 2: That's interesting. I've never met anyone who's lived in the same place for their whole life.\n","\n","\n","User 2: I'm just taking a break from work. I'm watching some TV.\n","\n","\n","User 2: That's cool. I'm not very good at it yet, but I'm getting better.\n","\n","\n","User 2: I like to play soccer and hang out with my friends.\n","\n","\n","User 2: I like rock too. What's your favorite band?\n","\n","\n","User 2: I like the people I meet there.\n","\n","\n","User 2: That sounds like a lot of fun. I'm sure it will be a beautiful day.\n","\n","\n","User 2: Nice to meet you too, Cloud.\n","\n","\n","User 2: I'm a car salesman.\n","\n","\n","User 2: It's not just the customers that are difficult, it's my coworkers too.\n","User 1: Really? Why is that?\n","User 2: Because they're all so competitive.\n","User 1: I can't imagine that.\n","User 2: It's true. Everyone is always trying to\n","\n","\n","User 2: I've always been interested in folk music.\n","\n","\n","User 2: Oh, that's a great game! I've heard a lot of good things about it.\n","\n","\n","User 2: I love seeing the bees in their natural habitat and being able to help them thrive.\n","\n","\n","User 2: I know! It's a pretty unique job.\n","\n","\n","User 2: That's great! I'm sure you'll do a great job.\n","\n","\n","User 2: I like to play with my dog.\n","\n","\n","User 2: That's cool. I'm not sure what I want to do after high school.\n","\n","\n","User 2: It's a great way to get some exercise and see some beautiful scenery.\n","\n","\n","User 2: Thanks! I have a pet fish.\n","\n","\n","User 2: It's a great game. I've been playing it for years.\n","\n","\n","User 2: I go to the gym regularly.\n","\n","\n","User 2: Really?\n","\n","\n","User 2: I'm a personal trainer.\n","\n","\n","User 2: That's amazing! I'm glad you're here now.\n","\n","\n","User 2: My dogs are named Luna, Bella, and Max.\n","\n","\n","User 2: Me neither! I'm glad I found you.\n","\n","\n","User 2: It's not for everyone, but it works for me.\n"]}]},{"cell_type":"code","source":["from unsloth import FastLanguageModel\n","import torch\n","max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n","dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n","load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n","\n","model, tokenizer = FastLanguageModel.from_pretrained(\n","    model_name = \"darthsid2000/lora_model\",\n","    max_seq_length = max_seq_length,\n","    dtype = dtype,\n","    load_in_4bit = load_in_4bit,\n",")\n","FastLanguageModel.for_inference(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":401},"id":"dWw_CfFrAEIP","executionInfo":{"status":"error","timestamp":1717588295326,"user_tz":420,"elapsed":12,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"6af99e42-d745-482f-b467-6f040b97edf2"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ModuleNotFoundError","evalue":"No module named 'unsloth'","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)","\u001b[0;32m<ipython-input-1-ac77550cb8a8>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0munsloth\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mFastLanguageModel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m2048\u001b[0m \u001b[0;31m# Choose any! We auto support RoPE Scaling internally!\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mdtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;31m# None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mload_in_4bit\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;31m# Use 4bit quantization to reduce memory usage. Can be False.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'unsloth'","","\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"],"errorDetails":{"actions":[{"action":"open_url","actionText":"Open Examples","url":"/notebooks/snippets/importing_libraries.ipynb"}]}}]},{"cell_type":"code","source":["spc_prompt = \"\"\"{}\n","\n","{}\n","\n","Here is the expected response:\n","{}\"\"\"\n","\n","\n","inputs = tokenizer(\n","[\n","    spc_prompt.format(\n","        \"I will be user 1, and you will be user 2. I will provide the personas for user 2 (which is you), and you have to infer the persona of user 2 through the context that is given afterwards. I will provide the first 6 lines of the conversation between the 2 users with the given persona. Given the persona of user 2 (your persona) and the beginning of the conversation, you will need to reply to my prompt as if you were that user, and take on that user's personality based on the description provided. Only reply with one line of conversation. Here is the persona for user 2, \", # instruction\n","        r\"User2 persona: My name is Sam.\\nI am a professional photographer based in New York.\\nI have a passion for travel and capturing landscapes.\\nI also love cooking and experimenting with new recipes. Now here is the conversational context that you need to use: User 1 (Alex): Hi, I'm Alex.\\nUser 2 (Sam): Hi, Alex. I'm Sam.\\nUser 1 (Alex): Nice to meet you, Sam. Where are you from?\\nUser 2 (Sam): I'm from New York. How about you?\\nUser 1 (Alex): I'm from California. I go to UCLA, studying computer science.\\nUser 2 (Sam): That's awesome! What got you into computer science?\\nUser 1 (Alex): I've always been fascinated by technology, and I love learning about machine learning and AI. What about you? What do you do for a living?\\nUser 2 (Sam): I'm a professional photographer. I love traveling and capturing beautiful landscapes. What do you like to do in your free time?\\nUser 1 (Alex): That sounds amazing. How did you get into photography?\\nUser 2 (Sam): I've always loved taking pictures since I was a kid, and it just grew into a passion over time. What's your favorite hiking spot in California?\\nUser 1 (Alex): I love hiking in the Santa Monica Mountains. It's beautiful there. Do you have any favorite places to photograph?\", # input\n","        \"\", # output - leave this blank for generation!\n","    )\n","], return_tensors = \"pt\").to(\"cuda\")\n","\n","outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n","s = tokenizer.batch_decode(outputs)[0]\n","print(s)"],"metadata":{"id":"s4Dc9z7_PPRX","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1717605597310,"user_tz":420,"elapsed":3192,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"1d1bed84-3dcd-4d37-f8fc-b0768e94c40a"},"execution_count":39,"outputs":[{"output_type":"stream","name":"stdout","text":["<|begin_of_text|>I will be user 1, and you will be user 2. I will provide the personas for user 2 (which is you), and you have to infer the persona of user 2 through the context that is given afterwards. I will provide the first 6 lines of the conversation between the 2 users with the given persona. Given the persona of user 2 (your persona) and the beginning of the conversation, you will need to reply to my prompt as if you were that user, and take on that user's personality based on the description provided. Only reply with one line of conversation. Here is the persona for user 2, \n","\n","User2 persona: My name is Sam.\\nI am a professional photographer based in New York.\\nI have a passion for travel and capturing landscapes.\\nI also love cooking and experimenting with new recipes. Now here is the conversational context that you need to use: User 1 (Alex): Hi, I'm Alex.\\nUser 2 (Sam): Hi, Alex. I'm Sam.\\nUser 1 (Alex): Nice to meet you, Sam. Where are you from?\\nUser 2 (Sam): I'm from New York. How about you?\\nUser 1 (Alex): I'm from California. I go to UCLA, studying computer science.\\nUser 2 (Sam): That's awesome! What got you into computer science?\\nUser 1 (Alex): I've always been fascinated by technology, and I love learning about machine learning and AI. What about you? What do you do for a living?\\nUser 2 (Sam): I'm a professional photographer. I love traveling and capturing beautiful landscapes. What do you like to do in your free time?\\nUser 1 (Alex): That sounds amazing. How did you get into photography?\\nUser 2 (Sam): I've always loved taking pictures since I was a kid, and it just grew into a passion over time. What's your favorite hiking spot in California?\\nUser 1 (Alex): I love hiking in the Santa Monica Mountains. It's beautiful there. Do you have any favorite places to photograph?\n","\n","Here is the expected response:\n","User 2 (Sam): I love the Grand Canyon! I've been there a few times and it's always so beautiful. What's your favorite thing to photograph?<|end_of_text|>\n"]}]},{"cell_type":"code","source":["s = s[17:].split('<')[0].replace('\\\\n', '\\n')\n","print(s)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qZOyXn4eEe1F","executionInfo":{"status":"ok","timestamp":1717605603341,"user_tz":420,"elapsed":520,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"df8187d8-a676-4cdf-c328-8250977a08d7"},"execution_count":40,"outputs":[{"output_type":"stream","name":"stdout","text":["I will be user 1, and you will be user 2. I will provide the personas for user 2 (which is you), and you have to infer the persona of user 2 through the context that is given afterwards. I will provide the first 6 lines of the conversation between the 2 users with the given persona. Given the persona of user 2 (your persona) and the beginning of the conversation, you will need to reply to my prompt as if you were that user, and take on that user's personality based on the description provided. Only reply with one line of conversation. Here is the persona for user 2, \n","\n","User2 persona: My name is Sam.\n","I am a professional photographer based in New York.\n","I have a passion for travel and capturing landscapes.\n","I also love cooking and experimenting with new recipes. Now here is the conversational context that you need to use: User 1 (Alex): Hi, I'm Alex.\n","User 2 (Sam): Hi, Alex. I'm Sam.\n","User 1 (Alex): Nice to meet you, Sam. Where are you from?\n","User 2 (Sam): I'm from New York. How about you?\n","User 1 (Alex): I'm from California. I go to UCLA, studying computer science.\n","User 2 (Sam): That's awesome! What got you into computer science?\n","User 1 (Alex): I've always been fascinated by technology, and I love learning about machine learning and AI. What about you? What do you do for a living?\n","User 2 (Sam): I'm a professional photographer. I love traveling and capturing beautiful landscapes. What do you like to do in your free time?\n","User 1 (Alex): That sounds amazing. How did you get into photography?\n","User 2 (Sam): I've always loved taking pictures since I was a kid, and it just grew into a passion over time. What's your favorite hiking spot in California?\n","User 1 (Alex): I love hiking in the Santa Monica Mountains. It's beautiful there. Do you have any favorite places to photograph?\n","\n","Here is the expected response:\n","User 2 (Sam): I love the Grand Canyon! I've been there a few times and it's always so beautiful. What's your favorite thing to photograph?\n"]}]},{"cell_type":"code","source":["print(*results, sep='\\n')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"sC98COj7i1eb","executionInfo":{"status":"ok","timestamp":1717576778015,"user_tz":420,"elapsed":1524,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"b06dd234-f18e-4f61-9503-fb0bbb554d67"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["User 2: It's not too late! You can always learn.\n","User 2: That's awesome! I've never been to Disney World.\n","User 2: Me too! What's your favorite piece of music?\n","User 2: You won't be disappointed!\n","User 2: I love helping people find the information they need. It's so satisfying to see someone learn something new.\n","User 2: It's a beautiful country! You should definitely go.\n","User 2: It is. I love my job.\n","User 2: I agree! I love the feeling of being out in nature and the challenge of the trails.\n","User 2: I love classical music.\n","User 2: What are some of your favorite books?\n","User 2: That's great! I love history too.\n","User 2: What are you doing this weekend?\n","User 2: That's awesome! I'm not very good at either instrument, but I've always wanted to learn.\n","User 2: I love the sense of camaraderie and the feeling of being part of something bigger than myself.\n","User 2: I like to read, play video games, and grow roses in my garden.\n","User 2: I love science fiction and fantasy too! I'm currently reading a book called \"The Hunger Games\" by Suzanne Collins.\n","User 2: That sounds like a lot of fun!\n","User 2: That's cool! I've never heard of dog agility classes. What are they like?\n","User 2: Me too. It's so meaningful.\n","User 2: So, what else are you up to?\n","User 2: I know what you mean. That's why I'm so glad I found Buddhism. It helps me to focus on the present and live in the moment.\n","User 2: That sounds like a great job. I'm sure you help a lot of people.\n","User 2: I might just do that.\n","User 2: That's interesting. I'm not sure I could do that.\n","User 2: I'm just watching some TV. What about you?\n","User 2: That's cool. I'm not very good, but I enjoy it.\n","User 2: I like to play soccer, watch movies, and hang out with my friends.\n","User 2: I like rock too! What are some of your favorite bands?\n","User 2: I like playing soccer.\n","User 2: That sounds nice. I'm sure it will be a beautiful day.\n","User 2: Nice to meet you too, Cloud.\n","User 2: I'm a car salesman.\n","User 2: It's not always easy, but it's worth it.\n","User 2: I've never heard of folk music. What is it?\n","User 2: I've heard good things about that game. I'll have to check it out.\n","User 2: I love watching the bees work. It's amazing to see how they communicate and cooperate with each other.\n","User 2: It's not as glamorous as it sounds. It's a lot of hard work.\n","User 2: I'm sure you'll do great!\n","User 2: I like to play soccer.\n","User 2: That's interesting. What do you want to do with your degree?\n","User 2: You should definitely give it a try! It's a great way to get some exercise and enjoy the outdoors.\n","User 2: Thanks! I'm glad you think so.\n","User 2: It's a great game. I've also played a lot of Fallout.\n","User 2: It's great to meet someone who likes the same things I do.\n","User 2: Really? What made you stop?\n","User 2: I'm a personal trainer.\n","User 2: That's amazing. I'm glad you're here.\n","User 2: My dogs are named Charlie, Bailey, and Sophie.\n","User 2: Me neither. It's always nice to meet someone who loves horses as much as I do.\n","User 2: It's definitely different, but it works for me.\n"]}]},{"cell_type":"code","source":["hf_token = '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'\n","model.push_to_hub(\"darthsid2000/lora_model\", token = hf_token) # Online saving\n","tokenizer.push_to_hub(\"darthsid2000/lora_model\", token = hf_token) # Online saving"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":99,"referenced_widgets":["7aa6551449b740c98bae1b10c86758ae","18a75509103845f49a9b06e6186a5b78","1c03a55cf2ed494e8ccc48567e236024","077fee77e218418c99c394db5637e9eb","023cca5429154248a86288b2e47858c2","6cbb0ff3537b48acb1e32461149117fe","2f30f5810881446289b2ce01b08a724f","1031392c0bb24e1c918191dd5f7ff221","98707ce1c8e64f608d701f16d7e395ca","e9013a1eb2224171ae0013bc40a457d1","6b68e55fc2ba41e5bc88848753b0bcee","06f0e83254ab4f038d09ca5bdf4ec1b6","4774b4f7676a4ca0be4e2433e80c87af","c33affbba6494afe9e6a227ef47293e6","c2f4b1905ad3421abc1d88b43e5ba25a","48ee0fc4862f431da6b5896da2c7b085","21b941c114c84090bdaeab3ce65203d2","c6d3d955762848d78f30a823bb1cc3b6","eeaad418e8d5419aa8cad2cb1b47f512","0a9e7d79d15242abaaffc299e5243575","775252fe82874ff3a653ce91be6b08bf","337ac775fdab4683898cdfb6cf25bba4"]},"id":"KbQO7r7Zt6SF","executionInfo":{"status":"ok","timestamp":1717579661682,"user_tz":420,"elapsed":22011,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"844aacec-5fcb-42b9-b415-d26256953972"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/579 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7aa6551449b740c98bae1b10c86758ae"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["adapter_model.safetensors:   0%|          | 0.00/168M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"06f0e83254ab4f038d09ca5bdf4ec1b6"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Saved model to https://huggingface.co/darthsid2000/lora_model\n"]}]},{"cell_type":"code","source":["# Merge to 16bit\n","if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n","if True: model.push_to_hub_merged(\"darthsid2000/model\", tokenizer, save_method = \"merged_16bit\", token = hf_token)\n","\n","# Merge to 4bit\n","if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n","if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n","\n","# Just LoRA adapters\n","if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n","if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":374,"referenced_widgets":["7a8630e88bc24f128d9bda522ac0fb6f","7b7eb0db75734412aba348d3569ac3e4","c77dd2317e8a4d52a054927efec34721","92aff42520d545e6b64b955c76840223","0f3b63f5ce664ce093bbd598d68a0223","3c3a6fc1639845b0b65455f687630e74","62f68c70986b405197c0f9753ab93f41","62f7953fea154309a78c35781cc5956c","8cb43e6cfa2a411cb116beab6e8720f6","78dd01a2ff824bd698d62cd451747f4d","e7fb0f0503f44792a92532eabea82810","3bd48450939144888da6948dd4692abd","90180316e17e41598226b06433994227","8334245a7c16416fb7876df5112c23f0","5337e3eb19584e61914cfb388e1bffd9","7e06fed2d5494873ac57953593f53bc8","115340a385e0430b840feaae85fd9575","437e1f5ab93a4058a10c57a0c84e927e","d88c82bf584a49ada37517745f2f3dd9","c7b97fa9ef634f11bc1ab73ea75a9c85","1c12fc7c676247e9a84956078fbe5bf4","5fdf97389407466a9f84dda5d1eccd74","ef138eb71d864728972a184bb9005b5d","5eacd40b7712417686c429415afb7e34","505e1f391a7e497b8ed2c6c670c19c83","a3e2a44c1cc34b77bec244f4cfcf602d","96e07b7dce6b40a5849cb0faefd9ec9a","b42609add0af41219c60b35c2418fda2","db4022c9efb043269f0464bb38d24dce","4d9fdbe91a7c49389a96084fb23794f5","ab78fc7575e24390ba1f369da4b461b5","b7481bf2141c4791ba6cc989e2e372ab","a166781e591f4f0e9b57148c446c948a","0ceeee5c869340aeaa7fc5fc65103494","623a837b929e4b80b9decfcee35a9e49","e49641cf50eb4de884671bccfaa1d695","6b6d0d6e263a46c980941822ce9b980a","bd0afcac733647029f0ce0b9a59c3a5b","0fc0a359a65e4624a7b54b27f19b8ef5","56de8c3cb0d043e68c6ad8492379a93a","25e4e13576304629b0121934aed4dab3","0d6b1d34f4474867b79ba2a7f1441d9e","88d801ddae6b4519b6109f8edbfeb1da","4898c2af647842b6bdb6c76b6c1f02c3","b3c14fcdf7ca4765a7e416a2d65c079d","90ba9c34f5b64879917ec0f3b5e79887","8a36163aae5a49ceb62994cbc54c530d","7956ac60308747249bc29b6ff680a8e4","b62f00ee601f44b8a132105a119c934d","80bd070982434b4caef30c26ecb35f33","b6d127810ff245d6ba3e269e726dea32","3e3b408bc96f4ae684b97a1e0b8e7396","04800a738b8b46928820d1810b2e4123","4acd595eceb9439fab09aadfc18b52a8","0d4436afa77e46ae8d9dd4d95c265c63","a8332b96d8854aea8186733a0ca4d835","acaeed9fc7a84b3699f3867db05cd5ad","49117eed5bba4a4987e9a5aa6b71d869","ec1f289e426b435faaf11414353b5be8","1d788573bc124f6c95609af21e91649e","ed2cf5f7f0844d979585de9d6a0b24c6","efbacb419f9b4d3b93cbb61bdb258bfa","219da8b5f6ce4d3f8a6bc5d295b76713","2dc79fce6daf43a09953a4f23609c72c","c4fe77e52c764009a3b21254debb52a3","95a40a857ba24507b30e886b0d8da135"]},"id":"JzEFrVQYupOw","executionInfo":{"status":"ok","timestamp":1717580556603,"user_tz":420,"elapsed":411479,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"9ca43bf1-8d13-4b9e-8bbd-69cdbe3b126c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["Unsloth: You are pushing to hub, but you passed your HF username = darthsid2000.\n","We shall truncate darthsid2000/model to model\n"]},{"output_type":"stream","name":"stdout","text":["Unsloth: Merging 4bit and LoRA weights to 16bit...\n","Unsloth: Will use up to 31.13 out of 52.96 RAM for saving.\n"]},{"output_type":"stream","name":"stderr","text":["100%|██████████| 32/32 [00:50<00:00,  1.57s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Unsloth: Saving tokenizer... Done.\n","Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n"]},{"output_type":"display_data","data":{"text/plain":["README.md:   0%|          | 0.00/579 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7a8630e88bc24f128d9bda522ac0fb6f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3bd48450939144888da6948dd4692abd"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ef138eb71d864728972a184bb9005b5d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0ceeee5c869340aeaa7fc5fc65103494"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b3c14fcdf7ca4765a7e416a2d65c079d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a8332b96d8854aea8186733a0ca4d835"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Done.\n","Saved merged model to https://huggingface.co/darthsid2000/model\n"]}]},{"cell_type":"code","source":["# definitions for carrying out bleu score calculations for two outputs\n","import subprocess\n","subprocess.run([\"pip\", \"install\", \"nltk\"])\n","import nltk\n","nltk.download('punkt')\n","nltk.download('averaged_perceptron_tagger')\n","nltk.download('universal_tagset')\n","\n","def bleu_score(hypothesis, reference):\n","    from nltk.translate.bleu_score import sentence_bleu\n","    return sentence_bleu([reference], hypothesis)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"gkCPGfFRoJxO","executionInfo":{"status":"ok","timestamp":1717605224578,"user_tz":420,"elapsed":7608,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"bc0c38cd-f3dd-454c-dffe-e01e6f36161f"},"execution_count":18,"outputs":[{"output_type":"stream","name":"stderr","text":["[nltk_data] Downloading package punkt to /root/nltk_data...\n","[nltk_data]   Unzipping tokenizers/punkt.zip.\n","[nltk_data] Downloading package averaged_perceptron_tagger to\n","[nltk_data]     /root/nltk_data...\n","[nltk_data]   Unzipping taggers/averaged_perceptron_tagger.zip.\n","[nltk_data] Downloading package universal_tagset to /root/nltk_data...\n","[nltk_data]   Unzipping taggers/universal_tagset.zip.\n"]}]},{"cell_type":"code","source":["# !pip install rouge-score\n","subprocess.run([\"pip\", \"install\", \"rouge-score\"])\n","import rouge_score\n","\n","from rouge_score import rouge_scorer\n","\n","def calculate_rouge_score(hypothesis, reference):\n","\n","    scorer = rouge_scorer.RougeScorer(['rougeL'], use_stemmer=True)\n","    scores = scorer.score(reference, hypothesis)\n","    return scores['rougeL'].fmeasure\n","\n","\n","\n","hypothesis = \"The quick brown fox jumps over the lazy dog\"\n","reference = \"The quick brown fox jumped over the lazy dog\"\n","rouge_l_fmeasure = calculate_rouge_score(hypothesis, reference)\n","print(f\"ROUGE-L F-measure: {rouge_l_fmeasure}\")\n","\n","hypothesis = \"The slow purple fox jumps over the lazy fox\"\n","\n","rouge_Scores = calculate_rouge_score(hypothesis, reference)\n","print(f\"ROUGE-L F-measure: {rouge_Scores}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5eQ5t7sfoKzb","executionInfo":{"status":"ok","timestamp":1717605232675,"user_tz":420,"elapsed":8102,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"440e2c28-d68a-4f30-a117-e904f88947ce"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stdout","text":["ROUGE-L F-measure: 1.0\n","ROUGE-L F-measure: 0.6666666666666666\n"]}]},{"cell_type":"code","source":["# !pip install bert_score\n","subprocess.run([\"pip\", \"install\", \"bert_score\"])\n","import bert_score\n","\n","def calculate_bertscore(hypotheses, references, model_type='bert-base-uncased'):\n","    P, R, F1 = bert_score.score(hypotheses, references, model_type=model_type, lang=\"en\")\n","\n","    return {\n","        'precision': P.mean().item(),\n","        'recall': R.mean().item(),\n","        'f1': F1.mean().item()\n","    }\n","\n","# Example usage\n","hypotheses = [\"The quick brown fox jumps over the lazy dog\"]\n","references = [\"The quick brown fox jumped over the lazy dog\"]\n","bertscore_result = calculate_bertscore(hypotheses, references)\n","print(f\"BERTScore: {bertscore_result}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195,"referenced_widgets":["e00a94d5767143ee82628aaf329a20fc","23673ed8758c4a678cf983c6ba81ea94","e3c34e6d7ea44360abb0683136db2dfd","3dc953f07aea46369b892a099a253bf9","6eafb0a2c9404a7ba634de1e51703627","ab0571c47fc642be8f26b8acd265b96e","550dbb359d9c4c44a755d61e48c74376","4fcb74cb9dbb4b29bf4daa9c577734e1","be4a406d2636471f900f0b85be52d07a","b06fec5197c24614adcac6f0e013061c","0cc2db4946744de88df87ff3771ac237","8a018eb6c72d41e89300c475dce25c37","d16440bd71e7450382b3d5446aba2697","509e501c00904fa482a98df41d63224f","aa81f7c7a8b349ed8a016a55809670da","61685d8eb2d9461cafe12703a2f57736","60a13220e11545b0b507af0b0ae14c4e","22befd4beede455ea5425e98565afd3b","bcdffe6fc54b4fdd8620ff464ff2c05a","6984c54e2cdc4d8a9bc46fda53542c6f","d0f7423afdae4a7288647edc10bdf4d4","343e6452b0804d7c9aa7e461c300b82d","de0b432baab2465d9459355fe457b33b","9d1c06f1708949c38eddd816256412b8","7fd4daeccbe549e4b89b3f511d9edc3a","f5dbd40ca9074c82a1d9e9df41ce22e6","0cd7c14be8984e8984abb365e72840c6","89bc1722aad240c6ae80726f3fa28880","6b7922b444ab4cd6993dde07e8fe2fcd","c0984e732d7c418f9cb4730be668f1bc","890de385557f4ba7b88c9f8e0abb2774","85d9b7fddbd646f6b4070dc385c019d8","8a3d864a1aaa4e6f845d70ea393b63ec","a3325cb01db84d6e92058947f3022ed5","d51842b7534e4002a59c82fb70931eed","40e59bbbc86144d295b8c5057ced161c","f8d936412ef34008aa02b978b9cbe6a9","2e9140e87f5f46889a76abbf104ed817","595ddec3f282495b8a3f4f33d6fe5295","93a43a43c88449f4bf22b808956f51e2","e68be761aa9c4c29a106a5fd615f6a2c","d3766600e3bb46e9b8be69c59c4cc330","a720365792fe45149226a4badc90fc21","691fc7cb4d3843ef9bb3ab05745b887c","fa49b587ddd446789adba2db0f7c3c29","ba071da4e5c74f79a660cde6b5ebf4c0","8ab53dce49e14e35b853c4aee8be72dd","442edddec6c445a5b7d744a22fd81025","b8b4ea1ff1e743e1932023381ce86989","577c80f8f76c4153a7cf12e7331f644c","13c2cb0e73684653a9dc0073b8721add","cdbc852b3f37440f97970c69566bdd79","2828ca843d154cdcb2d2bafc087f7003","d5590275f03d45fab53f710ab1832206","1edd5ebcb2b442f0b8798ec27355579d"]},"id":"2zui16EIoQKF","executionInfo":{"status":"ok","timestamp":1717605297266,"user_tz":420,"elapsed":64595,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"ed5869a7-6810-4d19-f1ad-a9c45a60600b"},"execution_count":20,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/48.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e00a94d5767143ee82628aaf329a20fc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8a018eb6c72d41e89300c475dce25c37"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"de0b432baab2465d9459355fe457b33b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3325cb01db84d6e92058947f3022ed5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/440M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa49b587ddd446789adba2db0f7c3c29"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["BERTScore: {'precision': 0.9593021869659424, 'recall': 0.9593021869659424, 'f1': 0.9593021869659424}\n"]}]},{"cell_type":"code","source":["import torch\n","from transformers import GPT2LMHeadModel, GPT2Tokenizer\n","\n","def calculate_perplexity(text, model_name='gpt2'):\n","    tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n","    model = GPT2LMHeadModel.from_pretrained(model_name)\n","    input_ids = tokenizer.encode(text, return_tensors='pt')\n","    with torch.no_grad():\n","        loss = model(input_ids, labels=input_ids)[0]\n","    return loss.item()\n","\n","# Example usage\n","text = \"The quick brown fox jumps over the lazy dog\"\n","perplexity = calculate_perplexity(text)\n","print(f\"Perplexity: {perplexity}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":259,"referenced_widgets":["ded5c02dc21842de83f9ed2edfddcfe3","0c27389c36a04ec9adcaaeb466e8647a","105a61a5c5d74dc88039c8d0fb01fdb5","0f734711b3784abf913c6c774d60a9df","c042f744d61c4114b681ea53a2ef5c9e","f2284a89e91f47cd8d188a69d349bf8f","9a431d17ff594f1b8ef2ab0917f12a8e","5b00865c708d4fc3ae0a811b18ce6623","a2855b7688b9446fbcb9815f108a5d36","db766d2ec68b46ceb6b0fbb181f884be","c31fb8a8b9ec48c6b17593ed28b69aeb","3b579755e75b4f0086957c5c8cf050ad","6517c05956e04e3daab297cac3465de7","945229dd593943a5bad91566d99405eb","c67925c0feab4b1cae20b40328caa7bf","e0b0619c544849db95df686428f14a30","303d62d975ec44299fdf52f7c5b729a0","063493d13a6f4a7991b0c9ec831620e9","624fd140839a4feb8247daf329d8a389","4787b41571044f84855a2bf12afad4c2","165936714a034625b89f9dbd4756a994","a8a6fc8311cc4ba2aeafdb2bf214f883","1ddeda5374e14639b3966b23b6a7427b","9421346a37b64923a201c0d3d0b850ff","23cdef8118c04e93bc744d0f6d434fe2","cd82d6a1728740328310525413f9e3f3","45ee372afb51473ab8a4251d69ad65fc","4fad9183fa0842ad89e888632c545bf5","bce250159f0f46a2823ee1d3f8418a52","7beed72ce10a48f5aa645eb124c980c2","6eaffce49090438c83057d9cbc8081ec","c9df97b3402748468d16ee0333ba60b8","fc1281f1728b4ca391cbb05bef226f63","7cdd8cf94a4e4af5b6415c263c02510a","6e889e3eff7848c2a7182b77a200b099","a6d43d0a67514945af43c9c9c2e8f142","7c3075520e674e9480f20caa452af328","92059de8051b4e1ea2fbbae1b54610eb","ce92cc292af04bd991d9c304cbd8cb07","cc0c0f79959d4633b63513774270ffc7","b523efc8749a4abb80340efe3bf462fd","d688f51b3f994596b9c2f209a31ab051","c3dc6ed485cc4abcb095522b3f791f66","dea8b761d2be4a3b9d3bfcf0933c42e5","a4c9be0990774c67a0f57e98caf7ae06","be3f595264a746729422468cbdafc7d2","269ee67c89534effbf7dfa90e63833d1","ef30b0e54bba4c7f9443f0fbebd7a96b","1144ed2584a04bdd9e80e6326eeb3fd5","d692a08571174f4a80716291a6bd9e51","aba39723ee0045598646b2cafd60e9e5","1187e071afc24854b667f1473a69bd23","8f562da43510436db048d875757088fd","b0458e9a5a7e45799af6e7a6af00f06a","89fbc41430984093be7f1f65bba12a5a","7c6b405c953c47f7bc723ee5e2f680b7","a65d6d51eee74e0aa064132ecbcaf891","cfabced6b60a4536acd81ff10c72ffb0","5400e9bab89e4213b442218e9f87fef0","641535e63c4e486780dcdb1ccac95531","15b8a50185a142b2b8b0aa7367e6c4f6","bf328cdbdfd04d97af589f47081f9f90","f003ff1367064a149c8f42bd8dcf239c","b60507bd679146c4a858ffa5e9b1432d","2a9bd80ea1c64fec92ab3403c0d8b2c0","0d1b67efe0db459f893d85ae451ec93a","a549ae6369914552ad6704045ad3e679","50e982415d3d4851885d6ebd752403cc","4702139a6c534f7c99b056bd6c6cf80b","c2357838720b49e0bc93f6f38082e558","cfa8538f42da456cb7c154df12b1da7d","5b5f7d9a74f54bd0984e5d4abe5b858a","22d9d893c3f845829607c27731b05cb6","206c626b8af04b86b13f6e2fcea4d44c","285c9d9a50a54867a3b92f7ab258bb67","c2930f65e4f646628ba2dcc4be442bef","2cc31f325ef145ab9e8d7ddbd8d6cced"]},"id":"o_IWMkR9oWFz","executionInfo":{"status":"ok","timestamp":1717605304344,"user_tz":420,"elapsed":7091,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"34336aa5-696c-4f5a-c6fd-6edef867c5b3"},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ded5c02dc21842de83f9ed2edfddcfe3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3b579755e75b4f0086957c5c8cf050ad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1ddeda5374e14639b3966b23b6a7427b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7cdd8cf94a4e4af5b6415c263c02510a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a4c9be0990774c67a0f57e98caf7ae06"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c6b405c953c47f7bc723ee5e2f680b7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a549ae6369914552ad6704045ad3e679"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Perplexity: 5.426175117492676\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","def extract_conversation_parts(csv_filename):\n","    data = pd.read_csv(csv_filename)\n","\n","    extracted_data = []\n","\n","    for _, row in data.iterrows():\n","        user1_persona = row['user 1 personas']\n","        user2_persona = row['user 2 personas']\n","        conversation = row['Best Generated Conversation']\n","\n","        conversation_lines = conversation.split('\\n')\n","\n","        if len(conversation_lines) >= 7:\n","            context = \"\\n\".join(conversation_lines[:6])\n","            prompt = conversation_lines[6]\n","            full_continued_convo = \"\\n\".join(conversation_lines[7:])\n","            single_continued_convo = conversation_lines[7]\n","\n","            extracted_data.append({\n","                'user1_persona': user1_persona,\n","                'user2_persona': user2_persona,\n","                'context': context,\n","                'prompt': prompt,\n","                'finished_convo': full_continued_convo,\n","                'prompt_response': single_continued_convo\n","            })\n","        else:\n","            print(f\"Warning: Conversation is too short in row {row.name}\")\n","\n","    return extracted_data\n","\n","csv_filename = '/content/drive/MyDrive/CS 263/Synthetic-Persona-Chat_valid.csv'\n","extracted_data = extract_conversation_parts(csv_filename)\n","\n","trunc_extracted_data = extracted_data[:50]\n","\n","for data in trunc_extracted_data:\n","    print(\"User 1 Persona:\", data['user1_persona'])\n","    print(\"User 2 Persona:\", data['user2_persona'])\n","    print(\"Context:\", data['context'])\n","    print(\"Prompt:\", data['prompt'])\n","    print(\"Expected response:\", data['prompt_response'])\n","    print()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZpdidXFmorm7","executionInfo":{"status":"ok","timestamp":1717605306380,"user_tz":420,"elapsed":2051,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"51528f57-d550-4f6b-8832-13b49a797cbc"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stdout","text":["User 1 Persona: I love to bake cookies.\n","I have a dogs.\n","The county wide bake sale is where i feel most at home.\n","Knitting is my passion.\n","User 2 Persona: I am a boy.\n","I can move objects with my mind.\n","I had to have a transplant.\n","I was born with my heart outside my body.\n","Context: User 1: Hi!\n","User 2: Hello!\n","User 1: What is your favorite thing to do?\n","User 2: I like to move objects with my mind.\n","User 1: That sounds like a lot of fun! I've always wanted to be able to do that.\n","User 2: It is! It's also really useful.\n","Prompt: User 1: What else do you like to do?\n","Expected response: User 2: I like to play video games and read comics.\n","\n","User 1 Persona: I am an animal activist.\n","The holidays make me depressed.\n","I have rainbow hair.\n","I spend my time bird watching with my cats.\n","User 2 Persona: I feel old.\n","I am currently in a juvenile detention center.\n","I will be released in about a month.\n","I am here for shoplifting.\n","Context: User 1: Hi, how are you?\n","User 2: I'm doing okay. I'm a little bored, but I'm trying to keep busy.\n","User 1: I'm glad to hear that. What are you doing to keep busy?\n","User 2: I'm reading a book, and I'm also playing some games.\n","User 1: That sounds fun. What book are you reading?\n","User 2: I'm reading a book about animal activism.\n","Prompt: User 1: Oh, that sounds really interesting! I love learning about animals.\n","Expected response: User 2: Me too! I'm an animal activist, so I'm always looking for ways to help animals.\n","\n","User 1 Persona: Cheeseburgers are my favorite food.\n","I like watching war documentaries.\n","I volunteer at a soup kitchen.\n","I am a retired gym teacher.\n","I was poor growing up.\n","User 2 Persona: I feel old.\n","I am currently in a juvenile detention center.\n","My mom is coming to visit me tomorrow.\n","I will be released in about a month.\n","I am here for shoplifting.\n","Context: User 1: Hello, my name is (name).\n","User 2: Hello, my name is (name).\n","User 1: It's nice to meet you. What do you do for fun?\n","User 2: I like to watch war documentaries.\n","User 1: Really? I love war documentaries too! What's your favorite one?\n","User 2: It's hard to say, I have so many favorites. But I think I would have to say \"The Battle of Midway.\"\n","Prompt: User 1: Oh, that's a great one! I love that one too.\n","Expected response: User 2: Me too! It's so well-made and it's so interesting.\n","\n","User 1 Persona: I plan to go to business school next year.\n","My parents are very wealthy bankers.\n","I grew up in manhattan.\n","I graduated from yale in 2011 with a degree in finance.\n","User 2 Persona: I am a male.\n","I own a house in florida.\n","I have a children and a dogs.\n","I enjoy american sports.\n","I work in it and have been at the same company for 15 years.\n","Context: User 1: I'm looking forward to going to business school next year.\n","User 2: That's great! What school are you going to?\n","User 1: I'm not sure yet, but I'm thinking about Wharton or Harvard.\n","User 2: Those are both great schools. I'm sure you'll get into one of them.\n","User 1: Thanks! I hope so.\n","User 2: What do you want to do with your business degree?\n","Prompt: User 1: I'm not sure yet, but I'm interested in working in investment banking.\n","Expected response: User 2: That's a great field. I know a few people who work in investment banking, and they all love it.\n","\n","User 1 Persona: I can drive a tractor.\n","My favorite color is red.\n","I enjoy listening to classical music.\n","My sister is a pole dancer.\n","User 2 Persona: I am a male.\n","I have a children and a dogs.\n","I enjoy american sports.\n","I work in it and have been at the same company for 15 years.\n","Context: User 1: I'm so glad we're both going to the concert tonight. I'm really excited to see the band.\n","User 2: Me too! I've been a fan of theirs for a long time.\n","User 1: What's your favorite song by them?\n","User 2: I'm not sure, I love all of their songs.\n","User 1: Me too. I think they're one of the best bands out there.\n","User 2: I agree. They're so talented.\n","Prompt: User 1: I'm really looking forward to hearing them live.\n","Expected response: User 2: Me too. I think it's going to be a great show.\n","\n","User 1 Persona: My father drove a car for nascar.\n","My favorite color is grey.\n","I have a german shepherd named barnaby.\n","I am terrified of scorpions.\n","I am employed by the us postal service.\n","User 2 Persona: I love dogs.\n","I live in alabama.\n","I like tacos.\n","I have one brother.\n","I have three sisters.\n","Context: User 1: What do you do for fun?\n","User 2: I love hanging out with my dogs and going for walks.\n","User 1: That sounds really fun. I love dogs too. What kind of dogs do you have?\n","User 2: I have a German Shepherd and a Golden Retriever.\n","User 1: Oh, those are both great breeds! I have a German Shepherd named Barnaby.\n","User 2: That's a great name! I love German Shepherds. They're so smart and loyal.\n","Prompt: User 1: They are! Barnaby is my best friend.\n","Expected response: User 2: I bet he is. I love dogs so much. They're always so happy to see me.\n","\n","User 1 Persona: My friends are all skateboarders.\n","I have a broken arm.\n","I am a skateboarder.\n","I am always wearing a hat.\n","User 2 Persona: I love dogs.\n","I live in alabama.\n","I have three sisters.\n","I like tacos.\n","Context: User 1: Hey!\n","User 2: Hi!\n","User 1: I'm going to a skate park with my friends tomorrow. Do you want to come?\n","User 2: I would love to, but I don't know how to skateboard.\n","User 1: That's okay! I can teach you.\n","User 2: That would be great! Thanks!\n","Prompt: User 1: No problem! What's your name?\n","Expected response: User 2: My name is Ashley.\n","\n","User 1 Persona: I feel old.\n","My mom is coming to visit me tomorrow.\n","I am here for shoplifting.\n","I will be released in about a month.\n","User 2 Persona: I am a vegan.\n","I will graduate from high school in two years.\n","I like riding horses.\n","My bedroom is purple and lime green.\n","Context: User 1: Hi, what's your name?\n","User 2: I'm [user 2's name].\n","User 1: Nice to meet you, [user 2's name].\n","User 2: Nice to meet you too.\n","User 1: So, what do you like to do for fun?\n","User 2: I like to ride horses, play video games, and watch movies.\n","Prompt: User 1: That sounds fun. I like to ride horses too.\n","Expected response: User 2: That's cool. What kind of horses do you like to ride?\n","\n","User 1 Persona: I read twenty books a year.\n","I am a stunt double as my second job.\n","I never broke a bone in my body ever in my life.\n","I was raised in a single parent household.\n","I only eat kosher.\n","User 2 Persona: I am a woman.\n","I am married.\n","I am a vegetarian.\n","I enjoy sports such as running.\n","Context: User 1: Hi!\n","User 2: Hi there!\n","User 1: What do you like to do for fun?\n","User 2: I like to run, I'm a vegetarian, and I'm married.\n","User 1: Oh, that's cool! I love reading, I'm a stunt double, and I was raised in a single parent household.\n","User 2: Wow, you're a stunt double? That's so cool! I've always wanted to do that.\n","Prompt: User 1: It's actually a lot of fun! I get to do all sorts of cool things, like fall off of buildings and drive cars really fast.\n","Expected response: User 2: That sounds amazing! I'm sure it's a lot of work, though.\n","\n","User 1 Persona: I was born in the early 80 s.\n","My favorite toy as a child as my lite brite.\n","I love new kids on the block.\n","I take dance lessons once a week.\n","User 2 Persona: I am a woman.\n","I have a dogs.\n","I attend church every week.\n","I work as a school teacher.\n","I enjoy gardening and walking outdoors.\n","Context: User 1: Hi, I'm [user 1's name].\n","User 2: Hi, I'm [user 2's name].\n","User 1: What do you like to do for fun?\n","User 2: I like to go to church, garden, and walk outdoors.\n","User 1: That's cool. I like to dance, listen to music, and read books.\n","User 2: Oh, I like music too! What kind of music do you listen to?\n","Prompt: User 1: I like all kinds of music, but my favorite is New Kids on the Block.\n","Expected response: User 2: I've never heard of them. What kind of music do they play?\n","\n","User 1 Persona: My favorite color is navy blue.\n","I am ex military.\n","I donate a part of my salary to charity each month.\n","I volunteer my time with a local bunny rescue.\n","I work fulltime in a shipyard.\n","User 2 Persona: I am a woman.\n","I work as a school teacher.\n","I attend church every week.\n","I enjoy gardening and walking outdoors.\n","I have a dogs.\n","Context: User 1: Hi there!\n","User 2: Hello! How are you doing today?\n","User 1: I'm doing well, thanks! How about you?\n","User 2: I'm doing good too! So, what do you do for fun?\n","User 1: I like to volunteer my time with a local bunny rescue. I also enjoy gardening and walking outdoors.\n","User 2: That's great! I love to garden too. What kind of flowers do you like to grow?\n","Prompt: User 1: I like to grow a variety of flowers, but my favorite are roses.\n","Expected response: User 2: Roses are beautiful! I love their fragrance.\n","\n","User 1 Persona: I love rollercoasters and sky diving.\n","I do like watching cooking shows.\n","I am not a good swimmer at all.\n","Hello i just moved here from germany.\n","User 2 Persona: I am married.\n","I am a vegetarian.\n","My favorite color is blue.\n","I am a woman.\n","Context: User 1: Hi, I'm new to the city. What's good to do around here?\n","User 2: There are a lot of great restaurants and bars. There are also a lot of parks and hiking trails.\n","User 1: I'm not much of a hiker, but I love food and I love to go out.\n","User 2: There are a lot of great restaurants in the city. We have everything from Indian food to Italian food to sushi.\n","User 1: What's your favorite restaurant?\n","User 2: I love the Italian restaurant called Il Forno. The food is amazing and the atmosphere is really nice.\n","Prompt: User 1: I've heard good things about Il Forno. I'll have to check it out.\n","Expected response: User 2: You should! It's one of my favorites.\n","\n","User 1 Persona: I am in an open polyamorous relationship.\n","I also have a dog walking business.\n","I like to watch the olympics.\n","I have three dogs.\n","My father was a door to door salesman.\n","User 2 Persona: I am married.\n","I enjoy sports such as running.\n","I am a vegetarian.\n","My favorite color is blue.\n","I am a woman.\n","Context: User 1: Hello there!\n","User 2: Hello! How are you today?\n","User 1: I'm doing well! What about you?\n","User 2: I'm doing great, thank you!\n","User 1: What do you like to do for fun?\n","User 2: I enjoy running, yoga, and reading.\n","Prompt: User 1: Oh, that's great! I love running too.\n","Expected response: User 2: Me too! It's a great way to clear your head and relax.\n","\n","User 1 Persona: I have three children.\n","My wife and kids are the best.\n","I am a plumber.\n","My favorite ice cream flavor is chocolate.\n","I love going to the park with my three children and my wife.\n","User 2 Persona: I diet a lot.\n","I am high maintenance.\n","I love listening to britney spears.\n","I like to tan in tanning beds.\n","I love spending money.\n","Context: User 1: Hello.\n","User 2: Hello.\n","User 1: How are you?\n","User 2: I am good. How are you?\n","User 1: I am good. What do you like to do for fun?\n","User 2: I like to listen to Britney Spears, tan in tanning beds, and diet.\n","Prompt: User 1: What's your favorite Britney Spears song?\n","Expected response: User 2: I like Toxic.\n","\n","User 1 Persona: I live in canada.\n","I am in the army.\n","My grandfather served in world war a.\n","I did not graduate high school.\n","My favourite food is shawarma.\n","User 2 Persona: I diet a lot.\n","I like to tan in tanning beds.\n","I am high maintenance.\n","I love spending money.\n","I love listening to britney spears.\n","Context: User 1: Hi, I'm [user1].\n","User 2: Hi, I'm [user2].\n","User 1: It's nice to meet you.\n","User 2: You too.\n","User 1: Where are you from?\n","User 2: I'm from [user2_location].\n","Prompt: User 1: Oh, cool. I'm from [user1_location].\n","Expected response: User 2: What do you do for a living?\n","\n","User 1 Persona: I ride the bus to school.\n","I hate math class.\n","My brother is older than me.\n","I am on the soccer team.\n","User 2 Persona: I have 5cats.\n","I am a construction worker.\n","My cats are very special to me.\n","I enjoy building houses.\n","Context: User 1: Hi, I'm [User 1's name].\n","User 2: Hi, I'm [User 2's name].\n","User 1: What do you do for work?\n","User 2: I'm a construction worker.\n","User 1: Oh, that's cool. I've always been interested in building houses.\n","User 2: It's a lot of work, but it's also very rewarding.\n","Prompt: User 1: I bet it is. Do you have any pets?\n","Expected response: User 2: Yeah, I have five cats.\n","\n","User 1 Persona: I am 13 years old.\n","My brother is older than me.\n","I hate math class.\n","I ride the bus to school.\n","I am on the soccer team.\n","User 2 Persona: I have 5cats.\n","My cats are very special to me.\n","I enjoy building houses.\n","I am a construction worker.\n","Context: User 1: Hi!\n","User 2: Hello! How are you today?\n","User 1: I'm doing well, thanks! How about you?\n","User 2: I'm doing great, thank you!\n","User 1: What do you do for a living?\n","User 2: I'm a construction worker.\n","Prompt: User 1: That's cool! I've always been interested in construction.\n","Expected response: User 2: It's a great job! I get to work outdoors, and I get to build things.\n","\n","User 1 Persona: I like to play chess.\n","I am in my thirties.\n","I am single and with two dogs.\n","I love taking bubble baths.\n","User 2 Persona: I like tacos.\n","I have one brother.\n","I have three sisters.\n","I love dogs.\n","I live in alabama.\n","Context: User 1: Hey there, what's up?\n","User 2: Nothing much, just hanging out with my dogs.\n","User 1: Oh cool, I love dogs! What kind do you have?\n","User 2: I have a golden retriever and a poodle.\n","User 1: Oh nice, I love those breeds! I have two dogs myself, a terrier and a dachshund.\n","User 2: Oh that's awesome! I love dogs, they're the best.\n","Prompt: User 1: Me too, they make the best companions.\n","Expected response: User 2: So what do you like to do for fun?\n","\n","User 1 Persona: My wife and kids are the best.\n","My favorite ice cream flavor is chocolate.\n","I am a plumber.\n","I love going to the park with my three children and my wife.\n","I have three children.\n","User 2 Persona: I like tacos.\n","I have three sisters.\n","I love dogs.\n","I live in alabama.\n","I have one brother.\n","Context: User 1: Hi, what's your name?\n","User 2: My name is User 2.\n","User 1: Nice to meet you, User 2. What do you like to do for fun?\n","User 2: I like to go to the park with my sisters and dogs.\n","User 1: That sounds fun! Do you have any other hobbies?\n","User 2: I like to cook and bake.\n","Prompt: User 1: That's awesome! I love to cook too. What's your favorite thing to cook?\n","Expected response: User 2: I love to make tacos.\n","\n","User 1 Persona: I have zero family that i am close to.\n","My name is george.\n","I am a bee farmer.\n","I married a super model from italy.\n","My favorite food is papaya.\n","User 2 Persona: I am a doctor.\n","My favorite movie is friday.\n","I enjoy going to the park.\n","I am now looking for a new job.\n","Context: User 1: I'm George, what's your name?\n","User 2: I'm Sarah, nice to meet you!\n","User 1: You too! What do you do for fun?\n","User 2: I like to go to the park and watch movies.\n","User 1: I like going to the park too! I'm a bee farmer.\n","User 2: Oh, that's really cool! I've never met a bee farmer before.\n","Prompt: User 1: Yeah, it's a pretty unique job. I love it though.\n","Expected response: User 2: I can imagine. Bees are pretty amazing creatures.\n","\n","User 1 Persona: Its red with blue stripe so it shiny when i race.\n","I have light skin with big brown eyes.\n","My main transportation is my motorbike.\n","I wear a motorbike helmet to protect my head.\n","User 2 Persona: I am a doctor.\n","I am now looking for a new job.\n","My favorite movie is friday.\n","I enjoy going to the park.\n","Context: User 1: Hi, I'm [user 1 name].\n","User 2: Hi, I'm [user 2 name].\n","User 1: What do you do for a living?\n","User 2: I'm a doctor.\n","User 1: Oh, that's cool. I'm a nurse.\n","User 2: Nice! What kind of nursing do you do?\n","Prompt: User 1: I'm a pediatric nurse.\n","Expected response: User 2: Oh, that must be really rewarding.\n","\n","User 1 Persona: I waitress during the day to supplement my income.\n","I have light skin with big brown eyes.\n","My main transportation is my motorbike.\n","Its red with blue stripe so it shiny when i race.\n","User 2 Persona: I am a doctor.\n","My daughter is a child prodigy.\n","I am now looking for a new job.\n","I enjoy going to the park.\n","Context: User 1: Hi! I'm a waitress during the day to supplement my income.\n","User 2: Hi there! I'm a doctor.\n","User 1: Cool! What kind of doctor?\n","User 2: I'm a pediatrician.\n","User 1: Oh, that's great! I love kids.\n","User 2: Me too! They're the best.\n","Prompt: User 1: I have a daughter who's a child prodigy.\n","Expected response: User 2: Really? That's amazing! What's she so good at?\n","\n","User 1 Persona: I hate the color pink.\n","My father was a construction worker.\n","I am a huge coffee lover.\n","I enjoy mountain biking as a hobby.\n","User 2 Persona: I drive a bmw.\n","My parents are very wealthy bankers.\n","I plan to go to business school next year.\n","I graduated from yale in 2011 with a degree in finance.\n","I grew up in manhattan.\n","Context: User 1: What do you do for fun?\n","User 2: I like to go to the opera and ballet.\n","User 1: Oh, that's nice. I'm not really into opera or ballet.\n","User 2: What do you like to do for fun?\n","User 1: I like to go hiking, biking, and camping. I also love to read.\n","User 2: That sounds like fun. I've never been hiking or biking before.\n","Prompt: User 1: You should try it! It's great exercise and you get to see some beautiful scenery.\n","Expected response: User 2: Maybe I will. What books do you like to read?\n","\n","User 1 Persona: I am a blonde girl with really short hair.\n","My favorite hobbies are listening to music and playing video games.\n","My favorite shirts are band shirts and ruffle sleeves.\n","I am rather skinny as i like to stay in shape.\n","User 2 Persona: I hate my job.\n","I am 40 years old.\n","I work as a car salesman.\n","My wife spends all my money.\n","I am planning on getting a divorce.\n","Context: User 1: I'm a blonde girl with really short hair. My favorite hobbies are listening to music and playing video games. I love wearing band shirts and ruffle sleeves. I'm rather skinny as I like to stay in shape.\n","User 2: I'm 40 years old. I work as a car salesman. I hate my job and my wife spends all my money. I'm planning on getting a divorce.\n","User 1: What's your favorite band?\n","User 2: The Beatles.\n","User 1: Nice! I love them too.\n","User 2: What kind of games do you like to play?\n","Prompt: User 1: I like to play RPGs and action games.\n","Expected response: User 2: I like to play RPGs too.\n","\n","User 1 Persona: My favorite band is up and i got to see them in concert once.\n","I love to play board games.\n","For vacation i enjoy time at the beach.\n","I am an elementary school teacher.\n","User 2 Persona: I hate my job.\n","My wife spends all my money.\n","I am 40 years old.\n","I work as a car salesman.\n","I am planning on getting a divorce.\n","Context: User 1: Hi! My name is John.\n","User 2: Hi John! My name is Mary.\n","User 1: What do you do for fun?\n","User 2: I like to play board games and go to the beach.\n","User 1: Me too! I love board games. What's your favorite one?\n","User 2: I love Scrabble. What's yours?\n","Prompt: User 1: I love Monopoly. I'm actually an elementary school teacher.\n","Expected response: User 2: Oh, that's cool. I work as a car salesman.\n","\n","User 1 Persona: I grew up in manhattan.\n","I drive a bmw.\n","I plan to go to business school next year.\n","My parents are very wealthy bankers.\n","I graduated from yale in 2011 with a degree in finance.\n","User 2 Persona: I hate my job.\n","My wife spends all my money.\n","I am 40 years old.\n","I am planning on getting a divorce.\n","I work as a car salesman.\n","Context: User 1: Hey, I'm [User 1's name]. What's your name?\n","User 2: I'm [User 2's name]. Nice to meet you.\n","User 1: You too. What do you do for a living?\n","User 2: I'm a car salesman.\n","User 1: Oh, that's interesting. How do you like it?\n","User 2: It's not bad, but I'm not sure I want to do it forever.\n","Prompt: User 1: Why not?\n","Expected response: User 2: Well, I'm not really passionate about cars. I'm more interested in finance.\n","\n","User 1 Persona: I enjoy coloring books.\n","My husband is adopted.\n","I have a german shepherd dog.\n","I live is a rural farming community.\n","User 2 Persona: I hate my job.\n","I am planning on getting a divorce.\n","My wife spends all my money.\n","I work as a car salesman.\n","Context: User 1: Hello, my name is John.\n","User 2: Hello, nice to meet you, my name is Jane.\n","User 1: It's nice to meet you too, Jane. What do you do for fun?\n","User 2: I like to go for walks in the park.\n","User 1: That sounds nice. I like to go for walks too.\n","User 2: Where do you live?\n","Prompt: User 1: I live in a rural farming community.\n","Expected response: User 2: That sounds nice. I live in the city.\n","\n","User 1 Persona: My mom passed away when i was young.\n","I love playing softball.\n","I work in the insurance business.\n","I recently was fired and had to get a new job.\n","My father is in the army.\n","User 2 Persona: I hate school.\n","I have blue eyes and red hair that comes to my waist.\n","I am a 12 year old girl.\n","I love to play pobox.\n","Context: User 1: What do you like to do for fun?\n","User 2: I like to play pobox.\n","User 1: What's that?\n","User 2: It's a game where you send messages to people and they have to guess who you are.\n","User 1: That sounds like fun. I've never heard of it before.\n","User 2: It's really fun! You should try it.\n","Prompt: User 1: I might have to. I'm always looking for new games to play.\n","Expected response: User 2: It's a good way to meet new people too.\n","\n","User 1 Persona: I have a cat named spook.\n","My favorite band is the who.\n","I work for our local supermarket.\n","My favorite food is pizza with black olives.\n","I have never been out of ohio.\n","User 2 Persona: I have a dogs.\n","I am scared of dying.\n","Knitting is my passion.\n","The county wide bake sale is where i feel most at home.\n","I love to bake cookies.\n","Context: User 1: Hi there, what's your name?\n","User 2: Hi, my name is Amy.\n","User 1: It's nice to meet you, Amy. What are you passionate about?\n","User 2: I'm passionate about knitting and baking.\n","User 1: That's cool! I love to bake too. What's your favorite thing to bake?\n","User 2: I love to bake cookies. I'm actually going to the county wide bake sale this weekend.\n","Prompt: User 1: Oh, that's fun! I've never been to a bake sale before.\n","Expected response: User 2: You should go! It's a lot of fun. You can try a bunch of different kinds of cookies.\n","\n","User 1 Persona: Hey my name is larry and i am a chef.\n","L also love comic books.\n","If i am not cooking in the kitchen then i am definitely on the water.\n","I love cooking but i also enjoy fishing.\n","User 2 Persona: I have a dogs.\n","I love to bake cookies.\n","I am scared of dying.\n","The county wide bake sale is where i feel most at home.\n","Context: User 1: I'm Larry, what's your name?\n","User 2: Hi Larry, I'm Kate.\n","User 1: Nice to meet you Kate.\n","User 2: Nice to meet you too Larry.\n","User 1: What do you like to do for fun?\n","User 2: I love to bake cookies and I also love my dog.\n","Prompt: User 1: That's cool! I love to cook too and I also love fishing.\n","Expected response: User 2: That's awesome! I've never been fishing before, but it looks like a lot of fun.\n","\n","User 1 Persona: I work from home.\n","I like to spend my money on cars.\n","I watch too much tv in spare time.\n","I have never had a steady relationship.\n","I go to the gym most days just to socialize.\n","User 2 Persona: I have a dogs.\n","I love to bake cookies.\n","Knitting is my passion.\n","The county wide bake sale is where i feel most at home.\n","Context: User 1: Hi! I'm Tom. What's your name?\n","User 2: Hi Tom! I'm Sarah.\n","User 1: It's nice to meet you, Sarah.\n","User 2: You too, Tom.\n","User 1: What do you do for fun?\n","User 2: I like to knit and bake cookies.\n","Prompt: User 1: That's cool! I like to watch TV and go to the gym.\n","Expected response: User 2: I go to the gym too!\n","\n","User 1 Persona: I am a blonde girl with really short hair.\n","My favorite shirts are band shirts and ruffle sleeves.\n","I love wearing skinny jeans and leggings.\n","I am rather skinny as i like to stay in shape.\n","My favorite hobbies are listening to music and playing video games.\n","User 2 Persona: I have a dogs.\n","I attend church every week.\n","I am a woman.\n","I work as a school teacher.\n","Context: User 1: Hello!\n","User 2: Hi there!\n","User 1: I like your hair.\n","User 2: Thank you! I like yours too!\n","User 1: Thanks! I'm a big fan of band shirts.\n","User 2: Me too! I have a lot of them.\n","Prompt: User 1: What are some of your favorite bands?\n","Expected response: User 2: I like a lot of different bands, but my favorites are The Killers, Arctic Monkeys, and The 1975.\n","\n","User 1 Persona: Since the surgery i have had strange powers.\n","I was born with my heart outside my body.\n","I can move objects with my mind.\n","I had to have a transplant.\n","I am a boy.\n","User 2 Persona: I like donuts.\n","My favorite band is the insane clown posse.\n","On weekends i do live action roleplaying events.\n","I play a lot of call of duty.\n","Context: User 1: What's up?\n","User 2: Not much, just chillin'. You?\n","User 1: Same here.\n","User 2: I like your hair.\n","User 1: Thanks. I like your shirt.\n","User 2: Thanks. It's a band shirt.\n","Prompt: User 1: Oh, cool. What band?\n","Expected response: User 2: Insane Clown Posse.\n","\n","User 1 Persona: I work in it and have been at the same company for 15 years.\n","I have a children and a dogs.\n","I am a male.\n","I own a house in florida.\n","I enjoy american sports.\n","User 2 Persona: I like donuts.\n","On weekends i do live action roleplaying events.\n","I play a lot of call of duty.\n","I drive a ford pinto.\n","My favorite band is the insane clown posse.\n","Context: User 1: Hello!\n","User 2: Hi there! How's it going?\n","User 1: Not bad, just hanging out. What about you?\n","User 2: I'm doing well, just got back from a live action roleplaying event.\n","User 1: Oh, that sounds like fun! What was it like?\n","User 2: It was great! We were all dressed up as different characters and we acted out a story.\n","Prompt: User 1: That sounds like a lot of fun! I've never been to one of those before.\n","Expected response: User 2: You should check one out sometime, they're really fun.\n","\n","User 1 Persona: I coach little league baseball.\n","I also like a glass of wine in the evenings.\n","Coffee is my preferred beverage.\n","I have five sons.\n","I drive a mini van.\n","User 2 Persona: I am a plumber.\n","My wife and kids are the best.\n","I have three children.\n","My favorite ice cream flavor is chocolate.\n","Context: User 1: Hi, I'm [user 1].\n","User 2: Hi, I'm [user 2].\n","User 1: I'm a coach for a little league baseball team.\n","User 2: That's cool! I'm a plumber.\n","User 1: What's your favorite flavor of ice cream?\n","User 2: Chocolate. What's yours?\n","Prompt: User 1: Coffee.\n","Expected response: User 2: Oh, I like coffee too! But I only drink it in the morning.\n","\n","User 1 Persona: I have a hat collection of over 1000 hats.\n","I won a gold medal in the 1992 olympics.\n","My favorite season is winter.\n","I have a weakness for vanilla ice cream.\n","I love cats and have two cats.\n","User 2 Persona: I am a plumber.\n","My favorite ice cream flavor is chocolate.\n","My wife and kids are the best.\n","I have three children.\n","Context: User 1: Hi! I'm John.\n","User 2: Hi, I'm Jane.\n","User 1: It's nice to meet you, Jane.\n","User 2: You too, John.\n","User 1: So, what do you do for a living?\n","User 2: I'm a plumber.\n","Prompt: User 1: Oh, cool! I've always been interested in plumbing.\n","Expected response: User 2: It's a good trade.\n","\n","User 1 Persona: I only listen to country music.\n","I work my parents farms.\n","I have a pet cow.\n","I am an electrician.\n","User 2 Persona: I am a recluse.\n","I have an allergy to water.\n","I am working on a building a robot to be my body double.\n","I am a famous twitch streamer.\n","Context: User 1: Hi, how are you today?\n","User 2: I'm doing well, thank you for asking.\n","User 1: That's good to hear.\n","User 2: What do you do for a living?\n","User 1: I'm an electrician.\n","User 2: Oh, that's interesting. What's that like?\n","Prompt: User 1: It's a lot of hard work, but it's also very rewarding. I get to work on a variety of projects and meet new people all the time.\n","Expected response: User 2: That sounds like fun.\n","\n","User 1 Persona: I am an electrician.\n","I work my parents farms.\n","I have a pet cow.\n","I drive a big truck and live in country.\n","I only listen to country music.\n","User 2 Persona: I am a recluse.\n","I am a famous twitch streamer.\n","I am working on a building a robot to be my body double.\n","I helped design the game starcraft.\n","I have an allergy to water.\n","Context: User 1: Hey! I'm an electrician who works on my parent's farm. I have a pet cow and drive a big truck. I live in the country and only listen to country music. What about you?\n","User 2: I'm a famous Twitch streamer who is a recluse. I'm allergic to water and working on building a robot to be my body double. I also helped design the game Starcraft.\n","User 1: Wow, you sound like an interesting person! I've never met a Twitch streamer before. What's it like to be famous?\n","User 2: It's fun, but it can also be a lot of pressure. I have to be careful about what I say and do, because I know that people are always watching.\n","User 1: I can imagine. But it sounds like you're good at it, since you're famous.\n","User 2: Thank you! I try my best.\n","Prompt: User 1: So, what's your favorite thing about being a Twitch streamer?\n","Expected response: User 2: I love being able to connect with people from all over the world. It's like having a global community of friends.\n","\n","User 1 Persona: My favorite color is orange.\n","I like to go hiking in the mornings to see the sunset.\n","I have a pet fish.\n","I just broke up with boyfriend two months ago.\n","User 2 Persona: I am a recluse.\n","I helped design the game starcraft.\n","I am working on a building a robot to be my body double.\n","I have an allergy to water.\n","I am a famous twitch streamer.\n","Context: User 1: Hi! I'm [user 1].\n","User 2: Hi! I'm [user 2].\n","User 1: It's nice to meet you! What do you do for a living?\n","User 2: I'm a recluse, so I don't really do much.\n","User 1: Oh, that sounds interesting. What does that mean?\n","User 2: I just prefer to stay in my house and not interact with people.\n","Prompt: User 1: I see. I'm not really a big extrovert myself, so I can understand that.\n","Expected response: User 2: Yeah, I think we're probably more similar than we are different.\n","\n","User 1 Persona: I never broke a bone in my body ever in my life.\n","I only eat kosher.\n","I read twenty books a year.\n","I am a stunt double as my second job.\n","I was raised in a single parent household.\n","User 2 Persona: I enjoy poetry.\n","I am a huge star wars fan.\n","I try various coffees as a hobby.\n","I played football for a division a college.\n","Context: User 1: Hi there!\n","User 2: Hello!\n","User 1: I'm (name).\n","User 2: Nice to meet you, (name). I'm (name).\n","User 1: What do you do for a living?\n","User 2: I'm a software engineer.\n","Prompt: User 1: That's cool! I'm a stunt double.\n","Expected response: User 2: Really? That sounds dangerous!\n","\n","User 1 Persona: I will graduate from high school in two years.\n","I am a vegan.\n","I have brown curly hair.\n","My bedroom is purple and lime green.\n","User 2 Persona: I am a buddhist.\n","I live in alaska.\n","I have not spoken in three months.\n","I like meditation.\n","I quit my job to pursue my spirituality full time.\n","Context: User 1: Hi! I'm [user 1's name].\n","User 2: Hello. I am [user 2's name].\n","User 1: Nice to meet you! What do you do for fun?\n","User 2: I like to meditate.\n","User 1: Oh, that's cool. I've never tried it before.\n","User 2: It's a great way to relax and clear your mind.\n","Prompt: User 1: I'm sure it is. I've always been interested in trying it, but I've never had the time.\n","Expected response: User 2: Well, you should definitely try it sometime. It's really helpful.\n","\n","User 1 Persona: I buy only organic food products.\n","I spend a lot of time browsing the internet.\n","I have solar panels on my roof.\n","I am a feminist.\n","User 2 Persona: I am a buddhist.\n","I quit my job to pursue my spirituality full time.\n","I have not spoken in three months.\n","I like meditation.\n","I live in alaska.\n","Context: User 1: Hi! I'm [user 1 name].\n","User 2: Hi there! I'm [user 2 name].\n","User 1: It's nice to meet you!\n","User 2: You too!\n","User 1: What do you do for fun?\n","User 2: I meditate and practice Buddhism.\n","Prompt: User 1: I've never tried meditation before. What's it like?\n","Expected response: User 2: It's very relaxing. It helps me to clear my mind and focus on the present moment.\n","\n","User 1 Persona: I own two vintage mustangs.\n","My favorite music is country.\n","I like to go to country concerts on weekends.\n","I like to work on vintage cars.\n","User 2 Persona: I am a buddhist.\n","I quit my job to pursue my spirituality full time.\n","I have not spoken in three months.\n","I like meditation.\n","I live in alaska.\n","Context: User 1: Hey.\n","User 2: Hello.\n","User 1: I'm (name).\n","User 2: It's nice to meet you.\n","User 1: Same to you. What do you like to do for fun?\n","User 2: I like to meditate and practice Buddhism.\n","Prompt: User 1: Oh, that's interesting. I've never been a Buddhist myself, but I've always been curious about it.\n","Expected response: User 2: It's a wonderful religion. It's all about living in the present moment and being mindful of your actions.\n","\n","User 1 Persona: I move south during the winter.\n","I have been retired for a years.\n","I enjoy mowing my lawn on sunny days.\n","I like to grill outdoors.\n","I go gambling in my spare time.\n","User 2 Persona: I am a feminist.\n","I buy only organic food products.\n","I have solar panels on my roof.\n","I spend a lot of time browsing the internet.\n","I love social medial especially twitter.\n","Context: User 1: Hello! What do you like to do for fun?\n","User 2: I like to browse the internet and use social media, especially Twitter.\n","User 1: I see. I'm not much of a social media person myself. I prefer to spend my time outside.\n","User 2: Oh, that's interesting. What do you like to do outside?\n","User 1: I like to go for walks, bike rides, and play golf. I also love gardening.\n","User 2: Gardening sounds like fun. I've never really tried it before.\n","Prompt: User 1: You should! It's a great way to relax and get some exercise.\n","Expected response: User 2: I might have to give it a try. I'm always looking for new ways to relax.\n","\n","User 1 Persona: I love wearing skinny jeans and leggings.\n","My favorite shirts are band shirts and ruffle sleeves.\n","I am a blonde girl with really short hair.\n","I am rather skinny as i like to stay in shape.\n","My favorite hobbies are listening to music and playing video games.\n","User 2 Persona: I am a feminist.\n","I buy only organic food products.\n","I spend a lot of time browsing the internet.\n","I have solar panels on my roof.\n","I love social medial especially twitter.\n","Context: User 1: Hi, what do you like to do for fun?\n","\n","User 2: I love to play video games and listen to music.\n","\n","User 1: Me too! What kind of music do you like?\n","\n","Prompt: User 2: I like all kinds of music, but I'm really into indie rock right now.\n","Expected response: \n","\n","User 1 Persona: I enjoy listening to classical music.\n","My sister is a pole dancer.\n","I can drive a tractor.\n","I am a christian.\n","My favorite color is red.\n","User 2 Persona: I am a musician.\n","I also study languages.\n","My favorite spanish word is trabajo.\n","One of the languages that i am currently studying is spanish.\n","Context: User 1: Hello, my name is John.\n","User 2: Hello, I am Maria. What do you like to do for fun?\n","User 1: I like to listen to classical music and I am a Christian.\n","User 2: I am also a musician. I like to play the guitar and I am currently studying Spanish.\n","User 1: Oh, that's great! I play the piano and I like to sing. I also know a little bit of Spanish.\n","User 2: That's awesome! We should play together sometime.\n","Prompt: User 1: That would be fun!\n","Expected response: User 2: Are you familiar with the word \"trabajo\"?\n","\n","User 1 Persona: I was raised on a horse farm.\n","I want to be a lawyer.\n","I dream of moving to the city.\n","I am from a small town.\n","User 2 Persona: I am a musician.\n","I also study languages.\n","One of the languages that i am currently studying is spanish.\n","My favorite spanish word is trabajo.\n","Context: User 1: I'm from a small town and I want to move to the city.\n","User 2: That's really cool! I live in the city and I love it.\n","User 1: What do you like about living in the city?\n","User 2: There are so many things to do and see. I love being able to go to concerts, shows, and museums. And there are always people around, so it's never lonely.\n","User 1: That's what I like about the city too! I'm also a musician, so I love being able to go to concerts and shows all the time.\n","User 2: That's awesome! What kind of music do you play?\n","Prompt: User 1: I play guitar and sing. I'm in a band with some friends of mine.\n","Expected response: User 2: That's so cool! I've always wanted to be in a band.\n","\n","User 1 Persona: I am here for shoplifting.\n","I feel old.\n","I will be released in about a month.\n","I am currently in a juvenile detention center.\n","User 2 Persona: I have two dogs.\n","My favorite music is country.\n","I like to go to country concerts on weekends.\n","I like to work on vintage cars.\n","I own two vintage mustangs.\n","Context: User 1: Hi, what's your name?\n","User 2: Hi, my name is [name].\n","User 1: Nice to meet you, [name]. What do you do for fun?\n","User 2: I like to go to country concerts on weekends, and I like to work on vintage cars.\n","User 1: That's cool! I love country music too. Do you have any favorite artists?\n","User 2: I love George Strait, Toby Keith, and Alan Jackson.\n","Prompt: User 1: Me too! I love their music. What's your favorite vintage car?\n","Expected response: User 2: I have two vintage mustangs, and they're both my favorite.\n","\n","User 1 Persona: I volunteer in a homeless shelter.\n","I enjoy being around people.\n","In my spare time i do volunteer work.\n","I like to workout a a times a week.\n","I am a professional wrestler.\n","User 2 Persona: I have two dogs.\n","I like to work on vintage cars.\n","My favorite music is country.\n","I own two vintage mustangs.\n","Context: User 1: Hi! What do you like to do for fun?\n","User 2: I like to work on vintage cars and go to concerts.\n","User 1: That's cool! I love working on cars too. What kind of cars do you like to work on?\n","User 2: I like working on vintage Mustangs. I have two of them.\n","User 1: That's awesome! I've always wanted to own a Mustang.\n","User 2: You should definitely get one. They're a lot of fun to drive.\n","Prompt: User 1: I know, I've been thinking about it for a while.\n","Expected response: User 2: Well, if you ever do, let me know and I can help you with the work.\n","\n","User 1 Persona: I love spending money.\n","I am high maintenance.\n","I like to tan in tanning beds.\n","I love listening to britney spears.\n","I diet a lot.\n","User 2 Persona: I like shopping.\n","I currently work for a watch dealer.\n","I often shop for shoes and watches.\n","My dream car is a rolls royce ghost.\n","My family does not support my career choices.\n","Context: User 1: Hey!\n","User 2: Hi!\n","User 1: What's up?\n","User 2: Not much, just working.\n","User 1: Oh, what do you do for work?\n","User 2: I work at a watch dealer.\n","Prompt: User 1: Oh, that's cool! I love shopping for watches.\n","Expected response: User 2: Me too! I have quite a collection.\n","\n"]}]},{"cell_type":"code","source":["# finetunes validation\n","single_model_responses = results\n","single_expected_responses = [data['prompt_response'] for data in trunc_extracted_data]"],"metadata":{"id":"0RdnRLr7phv7","executionInfo":{"status":"ok","timestamp":1717605306380,"user_tz":420,"elapsed":7,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["bertscore_result = calculate_bertscore(single_expected_responses, single_model_responses)\n","print(f\"BERTScore: {bertscore_result}\")\n","\n","# Model : Model 1"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UvPY_9SSq-Vp","executionInfo":{"status":"ok","timestamp":1717605307518,"user_tz":420,"elapsed":1143,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"207a6e2e-a02e-4605-8e78-77f90764dbde"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["BERTScore: {'precision': 0.55182284116745, 'recall': 0.5391867160797119, 'f1': 0.5393038392066956}\n"]},{"output_type":"stream","name":"stderr","text":["Warning: Empty candidate sentence detected; setting raw BERTscores to 0.\n"]}]},{"cell_type":"code","source":["#calculating bleu score\n","# Model : MODEL 1\n","avg = 0\n","for i in range(50):\n","    hypothesis = single_model_responses[i]\n","    reference = single_expected_responses[i]\n","    bleu_score_val = bleu_score(hypothesis, reference)\n","    avg += bleu_score_val/50\n","\n","print(f\"Average BLEU Score: {avg}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UlzZfkrBrIig","executionInfo":{"status":"ok","timestamp":1717605307519,"user_tz":420,"elapsed":10,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"7aac89f9-e06a-4abd-e60b-6c46ca388c91"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stdout","text":["Average BLEU Score: 0.1989026773727388\n"]}]},{"cell_type":"code","source":["#CALCULATING ROUGE SCORE\n","# Model: MODEL 1\n","avg = 0\n","for i in range(50):\n","    hypothesis = single_model_responses[i]\n","    reference = single_expected_responses[i]\n","    rouge_score_val = calculate_rouge_score(hypothesis, reference)\n","    avg += rouge_score_val/50\n","\n","print(f\"Average ROUGE Score baseline: {avg}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TRrkvD9rrNJQ","executionInfo":{"status":"ok","timestamp":1717605307519,"user_tz":420,"elapsed":5,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"31571557-ecf6-4919-9493-1db9ec70e4dd"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stdout","text":["Average ROUGE Score baseline: 0.26166100673124515\n"]}]},{"cell_type":"code","source":["#perplexity\n","#Model: MODEL 1\n","avg = 0\n","for i in range(50):\n","    text = single_model_responses[i]\n","    perplexity = calculate_perplexity(text)\n","    avg += perplexity/50\n","\n","print(f\"Average Perplexity baseline: {avg}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RPpOqHwNrQyN","executionInfo":{"status":"ok","timestamp":1717605371090,"user_tz":420,"elapsed":63574,"user":{"displayName":"SIDDARTH CHALASANI","userId":"03877369754210018754"}},"outputId":"ded3bea7-c0c5-4520-ed58-ccfb4abbca4e"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Perplexity baseline: 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