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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 + support us if you can!\n</div>\n\nTo install Unsloth on your own computer, follow the installation instructions on our Github page [here](https://github.com/unslothai/unsloth#installation-instructions---conda).\n\nYou 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).","metadata":{"id":"IqM-T1RTzY6C"}},{"cell_type":"markdown","source":"## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n\nI suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)","metadata":{}},{"cell_type":"code","source":"%%capture\n!pip install -U \"xformers<0.0.26\" --index-url https://download.pytorch.org/whl/cu121\n!pip install \"unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git\"\n\n# Temporary fix for https://github.com/huggingface/datasets/issues/6753\n!pip install datasets==2.16.0 fsspec==2023.10.0 gcsfs==2023.10.0\n\nimport os\nos.environ[\"WANDB_DISABLED\"] = \"true\"","metadata":{"execution":{"iopub.status.busy":"2024-05-25T04:02:01.128438Z","iopub.execute_input":"2024-05-25T04:02:01.128773Z","iopub.status.idle":"2024-05-25T04:05:55.463554Z","shell.execute_reply.started":"2024-05-25T04:02:01.128749Z","shell.execute_reply":"2024-05-25T04:05:55.462209Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\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* [**NEW**] 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.","metadata":{"id":"r2v_X2fA0Df5"}},{"cell_type":"code","source":"from unsloth import FastLanguageModel\nimport torch\nmax_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\ndtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\nload_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.\nfourbit_models = [\n \"unsloth/mistral-7b-bnb-4bit\",\n \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n \"unsloth/llama-2-7b-bnb-4bit\",\n \"unsloth/llama-2-13b-bnb-4bit\",\n \"unsloth/codellama-34b-bnb-4bit\",\n \"unsloth/tinyllama-bnb-4bit\",\n \"unsloth/llama-3-8b-bnb-4bit\",\n \"unsloth/llama-3-70b-bnb-4bit\",\n] # More models at https://huggingface.co/unsloth\n\nmodel, tokenizer = FastLanguageModel.from_pretrained(\n model_name = \"Orenguteng/Llama-3-8B-Lexi-Uncensored\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\n max_seq_length = max_seq_length,\n dtype = dtype,\n load_in_4bit = load_in_4bit,\n use_gradient_checkpointing = \"unsloth\", # We cut memory usage by a further 30% and now support fine-tuning of LLMs with 4x longer context windows!\n # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n)","metadata":{"id":"QmUBVEnvCDJv","outputId":"5eff0d61-05b4-471c-eea2-c2e84a915109","execution":{"iopub.status.busy":"2024-05-25T04:06:55.008762Z","iopub.execute_input":"2024-05-25T04:06:55.009117Z","iopub.status.idle":"2024-05-25T04:07:35.338067Z","shell.execute_reply.started":"2024-05-25T04:06:55.009090Z","shell.execute_reply":"2024-05-25T04:07:35.337098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"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","source":"model = FastLanguageModel.get_peft_model(\n model,\n r = 32, # 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 use_gradient_checkpointing = \"unsloth\", # 4x longer contexts auto supported!\n random_state = 3407,\n use_rslora = False, # We support rank stabilized LoRA\n loftq_config = None, # And LoftQ\n)","metadata":{"id":"6bZsfBuZDeCL","outputId":"b630cc80-ff95-45a2-cc0d-38666010d73b","execution":{"iopub.status.busy":"2024-05-25T04:23:33.920458Z","iopub.execute_input":"2024-05-25T04:23:33.920865Z","iopub.status.idle":"2024-05-25T04:23:34.015573Z","shell.execute_reply.started":"2024-05-25T04:23:33.920836Z","shell.execute_reply":"2024-05-25T04:23:34.014490Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Data\"></a>\n### Data Prep\nWe 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\nIf you want to use the `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n\nFor text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing).","metadata":{"id":"vITh0KVJ10qX"}},{"cell_type":"code","source":"from datasets import load_dataset\nimport json\nfrom unsloth.chat_templates import get_chat_template\n\ntokenizer = get_chat_template(\n tokenizer,\n chat_template = \"llama-3\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n map_eos_token = True, # Maps <|im_end|> to </s> instead\n)\n\ndef formatting_prompts_func(convos):\n texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n return { \"text\" : texts, }\n\nwith open(\"/kaggle/input/the-group-chat/output-10k-c-dropout.json\") as chatfile:\n convos = [json.loads(j) for j in chatfile.readlines()]\n\nwith open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n convos += [json.loads(j) for j in chatfile.readlines()]\n \ndataset = formatting_prompts_func(convos)","metadata":{"id":"LjY75GoYUCB8","outputId":"9f40f734-788c-4793-c1af-e9d003337612","execution":{"iopub.status.busy":"2024-05-25T04:28:11.710969Z","iopub.execute_input":"2024-05-25T04:28:11.711971Z","iopub.status.idle":"2024-05-25T04:28:13.097432Z","shell.execute_reply.started":"2024-05-25T04:28:11.711936Z","shell.execute_reply":"2024-05-25T04:28:13.096601Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from datasets import Dataset\ndataset = Dataset.from_dict(dataset)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Train\"></a>\n### Train the model\nNow 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","source":"from trl import SFTTrainer\nfrom transformers import TrainingArguments\n\ntrainer = SFTTrainer(\n model = model,\n tokenizer = tokenizer,\n train_dataset = dataset,\n dataset_text_field = \"text\",\n max_seq_length = max_seq_length,\n dataset_num_proc = 2,\n packing = False, # Can make training 5x faster for short sequences.\n args = TrainingArguments(\n per_device_train_batch_size = 2,\n gradient_accumulation_steps = 4,\n warmup_steps = 5,\n num_train_epochs=1,\n learning_rate = 2e-4,\n fp16 = not torch.cuda.is_bf16_supported(),\n bf16 = torch.cuda.is_bf16_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 report_to = \"none\",\n ),\n)","metadata":{"id":"95_Nn-89DhsL","outputId":"4b809e6d-271f-446f-dec8-abe0d13259f8","execution":{"iopub.status.busy":"2024-05-25T04:28:27.973142Z","iopub.execute_input":"2024-05-25T04:28:27.973856Z","iopub.status.idle":"2024-05-25T04:28:28.119131Z","shell.execute_reply.started":"2024-05-25T04:28:27.973822Z","shell.execute_reply":"2024-05-25T04:28:28.117976Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#@title Show current memory stats\ngpu_stats = torch.cuda.get_device_properties(0)\nstart_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\nmax_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\nprint(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\nprint(f\"{start_gpu_memory} GB of memory reserved.\")","metadata":{"id":"2ejIt2xSNKKp","cellView":"form","outputId":"4815a050-0c0f-4a6a-9d93-b01c44eaea35","execution":{"iopub.status.busy":"2024-04-06T16:21:16.730485Z","iopub.execute_input":"2024-04-06T16:21:16.730782Z","iopub.status.idle":"2024-04-06T16:21:16.737279Z","shell.execute_reply.started":"2024-04-06T16:21:16.730754Z","shell.execute_reply":"2024-04-06T16:21:16.736403Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"trainer_stats = trainer.train()","metadata":{"id":"yqxqAZ7KJ4oL","outputId":"3cf26aac-6042-4458-c4a6-d8849efb6a95","execution":{"iopub.status.busy":"2024-04-06T16:21:16.738651Z","iopub.execute_input":"2024-04-06T16:21:16.739026Z","iopub.status.idle":"2024-04-06T16:30:10.783093Z","shell.execute_reply.started":"2024-04-06T16:21:16.738993Z","shell.execute_reply":"2024-04-06T16:30:10.782238Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#@title Show final memory and time stats\nused_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\nused_memory_for_lora = round(used_memory - start_gpu_memory, 3)\nused_percentage = round(used_memory /max_memory*100, 3)\nlora_percentage = round(used_memory_for_lora/max_memory*100, 3)\nprint(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\nprint(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\nprint(f\"Peak reserved memory = {used_memory} GB.\")\nprint(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\nprint(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\nprint(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")","metadata":{"id":"pCqnaKmlO1U9","cellView":"form","outputId":"cf63d152-e152-468c-ba0d-938e0d2f71a0","execution":{"iopub.status.busy":"2024-04-06T16:30:10.784435Z","iopub.execute_input":"2024-04-06T16:30:10.7848Z","iopub.status.idle":"2024-04-06T16:30:10.791887Z","shell.execute_reply.started":"2024-04-06T16:30:10.784767Z","shell.execute_reply":"2024-04-06T16:30:10.791092Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Inference\"></a>\n### Inference\nLet's run the model! You can change the instruction and input - leave the output blank!","metadata":{"id":"ekOmTR1hSNcr"}},{"cell_type":"code","source":"if False:\n # alpaca_prompt = Copied from above\n FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n inputs = tokenizer(\n [\n alpaca_prompt.format(\n \"Continue the fibonnaci sequence.\", # instruction\n \"1, 1, 2, 3, 5, 8\", # 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 tokenizer.batch_decode(outputs)","metadata":{"id":"kR3gIAX-SM2q","outputId":"5b71f982-38c0-44c8-a4e5-58cd20b5a585","execution":{"iopub.status.busy":"2024-04-06T16:30:10.793045Z","iopub.execute_input":"2024-04-06T16:30:10.793321Z","iopub.status.idle":"2024-04-06T16:30:13.837651Z","shell.execute_reply.started":"2024-04-06T16:30:10.793298Z","shell.execute_reply":"2024-04-06T16:30:13.836679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!","metadata":{"id":"CrSvZObor0lY"}},{"cell_type":"code","source":"if False:\n # alpaca_prompt = Copied from above\n FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n inputs = tokenizer(\n [\n alpaca_prompt.format(\n \"Continue the fibonnaci sequence.\", # instruction\n \"1, 1, 2, 3, 5, 8\", # input\n \"\", # output - leave this blank for generation!\n )\n ], return_tensors = \"pt\").to(\"cuda\")\n\n from transformers import TextStreamer\n text_streamer = TextStreamer(tokenizer)\n _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)","metadata":{"id":"e2pEuRb1r2Vg","outputId":"084aab62-2122-436a-c0cb-8871986640eb","execution":{"iopub.status.busy":"2024-04-06T16:30:13.840849Z","iopub.execute_input":"2024-04-06T16:30:13.841138Z","iopub.status.idle":"2024-04-06T16:30:15.541954Z","shell.execute_reply.started":"2024-04-06T16:30:13.841114Z","shell.execute_reply":"2024-04-06T16:30:15.54076Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"<a name=\"Save\"></a>\n### Saving, loading finetuned models\nTo save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n\n**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!","metadata":{"id":"uMuVrWbjAzhc"}},{"cell_type":"code","source":"#model.save_pretrained(\"lora_model\") # Local saving\nmodel.push_to_hub(\"scoliono/groupchat_lora_lexi_8b\", token = \"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\")","metadata":{"id":"upcOlWe7A1vc","execution":{"iopub.status.busy":"2024-04-06T16:30:15.543701Z","iopub.execute_input":"2024-04-06T16:30:15.544355Z","iopub.status.idle":"2024-04-06T16:30:16.234142Z","shell.execute_reply.started":"2024-04-06T16:30:15.544315Z","shell.execute_reply":"2024-04-06T16:30:16.233363Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:","metadata":{"id":"AEEcJ4qfC7Lp"}},{"cell_type":"code","source":"if False:\n from unsloth import FastLanguageModel\n model, tokenizer = FastLanguageModel.from_pretrained(\n model_name = \"scoliono/groupchat_lora_instruct\", # YOUR MODEL YOU USED FOR TRAINING\n max_seq_length = max_seq_length,\n dtype = dtype,\n load_in_4bit = load_in_4bit,\n )\n FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n\n # alpaca_prompt = You MUST copy from above!\n\n inputs = tokenizer(\n [\n alpaca_prompt.format(\n \"What is a famous tall tower in Paris?\", # instruction\n \"\", # 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 tokenizer.batch_decode(outputs)","metadata":{"id":"MKX_XKs_BNZR","outputId":"05e5a193-dab0-41db-e07c-4b3afbdd7932","execution":{"iopub.status.busy":"2024-04-06T16:30:16.235412Z","iopub.execute_input":"2024-04-06T16:30:16.236127Z","iopub.status.idle":"2024-04-06T16:30:20.286318Z","shell.execute_reply.started":"2024-04-06T16:30:16.236092Z","shell.execute_reply":"2024-04-06T16:30:20.285241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**.","metadata":{"id":"QQMjaNrjsU5_"}},{"cell_type":"code","source":"if False:\n # I highly do NOT suggest - use Unsloth if possible\n from peft import AutoPeftModelForCausalLM\n from transformers import AutoTokenizer\n model = AutoPeftModelForCausalLM.from_pretrained(\n \"lora_model\", # YOUR MODEL YOU USED FOR TRAINING\n load_in_4bit = load_in_4bit,\n )\n tokenizer = AutoTokenizer.from_pretrained(\"lora_model\")","metadata":{"id":"yFfaXG0WsQuE","execution":{"iopub.status.busy":"2024-04-06T16:30:20.289045Z","iopub.execute_input":"2024-04-06T16:30:20.289914Z","iopub.status.idle":"2024-04-06T16:30:20.294953Z","shell.execute_reply.started":"2024-04-06T16:30:20.289877Z","shell.execute_reply":"2024-04-06T16:30:20.293978Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Saving to float16 for VLLM\n\nWe also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens.","metadata":{"id":"f422JgM9sdVT"}},{"cell_type":"code","source":"# Merge to 16bit\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n\n# Merge to 4bit\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n\n# Just LoRA adapters\nif False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\nif False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")","metadata":{"id":"iHjt_SMYsd3P","execution":{"iopub.status.busy":"2024-04-06T16:30:20.295979Z","iopub.execute_input":"2024-04-06T16:30:20.296285Z","iopub.status.idle":"2024-04-06T16:30:20.308979Z","shell.execute_reply.started":"2024-04-06T16:30:20.29626Z","shell.execute_reply":"2024-04-06T16:30:20.308167Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### GGUF / llama.cpp Conversion\nTo save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n\nSome supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.","metadata":{"id":"TCv4vXHd61i7"}},{"cell_type":"code","source":"# Save to 8bit Q8_0\nif False: model.save_pretrained_gguf(\"model\", tokenizer,)\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n\n# Save to 16bit GGUF\nif False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n\n# Save to q4_k_m GGUF\nif False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\nif False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")","metadata":{"id":"FqfebeAdT073","execution":{"iopub.status.busy":"2024-04-06T16:30:20.310103Z","iopub.execute_input":"2024-04-06T16:30:20.310443Z","iopub.status.idle":"2024-04-06T16:30:20.324421Z","shell.execute_reply.started":"2024-04-06T16:30:20.310419Z","shell.execute_reply":"2024-04-06T16:30:20.323668Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html).","metadata":{"id":"bDp0zNpwe6U_"}},{"cell_type":"markdown","source":"And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n\nSome other links:\n1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n\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.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> Support our work if you can! Thanks!\n</div>","metadata":{"id":"Zt9CHJqO6p30"}}]} \ No newline at end of file +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0ff91594", + "metadata": { + "id": "IqM-T1RTzY6C", + "papermill": { + "duration": 0.022416, + "end_time": "2024-11-19T19:01:59.936783", + "exception": false, + "start_time": "2024-11-19T19:01:59.914367", + "status": "completed" + }, + "tags": [] + }, + "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 + support us if you can!\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)." + ] + }, + { + "cell_type": "markdown", + "id": "9f31fd0e", + "metadata": { + "papermill": { + "duration": 0.01882, + "end_time": "2024-11-19T19:01:59.975791", + "exception": false, + "start_time": "2024-11-19T19:01:59.956971", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "## Kaggle is slow - you'll have to wait **5 minutes** for it to install.\n", + "\n", + "I suggest you to use our free Colab notebooks instead. I linked our Mistral Colab notebook here: [notebook](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5da70b6b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:02:00.014824Z", + "iopub.status.busy": "2024-11-19T19:02:00.014491Z", + "iopub.status.idle": "2024-11-19T19:06:21.486688Z", + "shell.execute_reply": "2024-11-19T19:06:21.485746Z" + }, + "papermill": { + "duration": 261.495285, + "end_time": "2024-11-19T19:06:21.489744", + "exception": false, + "start_time": "2024-11-19T19:01:59.994459", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pip3-autoremove\r\n", + " Downloading pip3_autoremove-1.2.2-py2.py3-none-any.whl.metadata (2.2 kB)\r\n", + "Requirement already satisfied: pip in /opt/conda/lib/python3.10/site-packages (from pip3-autoremove) (24.0)\r\n", + 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was not found and is required by the application\r\n", + "Skipping google.auth\r\n", + "scipy 1.14.1 is installed but scipy<1.14.0,>=1.7.0 is required\r\n", + "Redoing requirement with just package name...\r\n", + "google-api-core 2.11.1 is installed but google-api-core[grpc]<2.0.0dev,>=1.22.2 is required\r\n", + "Redoing requirement with just package name...\r\n", + "google-api-core 2.11.1 is installed but google-api-core[grpc]<2.0.0dev,>=1.14.0 is required\r\n", + "Redoing requirement with just package name...\r\n", + "pyarrow 16.1.0 is installed but pyarrow<15,>=2 is required\r\n", + "Redoing requirement with just package name...\r\n", + "jupyter-lsp 1.5.1 is installed but jupyter-lsp>=2.0.0 is required\r\n", + "Redoing requirement with just package name...\r\n", + "jupyter-lsp 1.5.1 is installed but jupyter-lsp>=2.0.0 is required\r\n", + "Redoing requirement with just package name...\r\n", + "google-cloud-storage 1.44.0 is installed but google-cloud-storage<3,>=2.2.1 is 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is required\r\n", + "Redoing requirement with just package name...\r\n", + "The 'libucx>=1.15.0' distribution was not found and is required by the application\r\n", + "Skipping libucx\r\n", + "packaging 21.3 is installed but packaging>=23.1 is required\r\n", + "Redoing requirement with just package name...\r\n", + "scipy 1.14.1 is installed but scipy<1.14,>=1.4.1 is required\r\n", + "Redoing requirement with just package name...\r\n", + "torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", + " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + "torchvision 0.19.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + " torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", + " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + "torchaudio 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + " torch 2.4.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + " sympy 1.13.3 (/opt/conda/lib/python3.10/site-packages)\r\n", + " mpmath 1.3.0 (/opt/conda/lib/python3.10/site-packages)\r\n", + "Found existing installation: sympy 1.13.3\r\n", + "Uninstalling sympy-1.13.3:\r\n", + " Successfully uninstalled sympy-1.13.3\r\n", + "Found existing installation: torchvision 0.19.0\r\n", + "Uninstalling torchvision-0.19.0:\r\n", + " Successfully uninstalled torchvision-0.19.0\r\n", + "Found existing installation: mpmath 1.3.0\r\n", + "Uninstalling mpmath-1.3.0:\r\n", + " Successfully uninstalled mpmath-1.3.0\r\n", + "Found existing installation: torch 2.4.0\r\n", + "Uninstalling torch-2.4.0:\r\n", + " Successfully uninstalled torch-2.4.0\r\n", + "Found existing installation: torchaudio 2.4.0\r\n", + "Uninstalling torchaudio-2.4.0:\r\n", + " Successfully uninstalled torchaudio-2.4.0\r\n", + "Looking in indexes: https://download.pytorch.org/whl/cu121\r\n", + "Collecting torch\r\n", + " Downloading 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jinja2->torch) (2.1.5)\r\n", + "Installing collected packages: mpmath, triton, sympy, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12, torch, xformers, torchvision, torchaudio\r\n", + "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n", + "fastai 2.7.17 requires torch<2.5,>=1.10, but you have torch 2.5.1+cu121 which is incompatible.\u001b[0m\u001b[31m\r\n", + "\u001b[0mSuccessfully installed mpmath-1.3.0 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-9.1.0.70 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.21.5 nvidia-nvjitlink-cu12-12.1.105 nvidia-nvtx-cu12-12.1.105 sympy-1.13.1 torch-2.5.1+cu121 torchaudio-2.5.1+cu121 torchvision-0.20.1+cu121 triton-3.1.0 xformers-0.0.28.post3\r\n", + "Collecting unsloth[kaggle-new]\r\n", + " Downloading unsloth-2024.11.7-py3-none-any.whl.metadata (59 kB)\r\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.7/59.7 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta 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installation: unsloth 2024.11.7\r\n", + "Uninstalling unsloth-2024.11.7:\r\n", + " Successfully uninstalled unsloth-2024.11.7\r\n", + "Collecting git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", + " Cloning https://github.com/unslothai/unsloth.git (to revision a2f8db3e7341f983af5814a2c56f54fa29ee548d) to /tmp/pip-req-build-7w3hakz0\r\n", + " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-7w3hakz0\r\n", + " Running command git rev-parse -q --verify 'sha^a2f8db3e7341f983af5814a2c56f54fa29ee548d'\r\n", + " Running command git fetch -q https://github.com/unslothai/unsloth.git a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", + " Running command git checkout -q a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", + " Resolved https://github.com/unslothai/unsloth.git to commit a2f8db3e7341f983af5814a2c56f54fa29ee548d\r\n", + " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \b-\b \b\\\b \b|\b \bdone\r\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n", + "\u001b[?25hBuilding wheels for collected packages: unsloth\r\n", + " Building wheel for unsloth (pyproject.toml) ... \u001b[?25l-\b \b\\\b \bdone\r\n", + "\u001b[?25h Created wheel for unsloth: filename=unsloth-2024.10.7-py3-none-any.whl size=164376 sha256=318d24041afad463f487f3927388d766e913ffa5b694f3e2e3b1a7851fa67a1c\r\n", + " Stored in directory: /root/.cache/pip/wheels/d5/c3/0d/98b9068092121456c620edb0a24e05fda5934229b776b63a7b\r\n", + "Successfully built unsloth\r\n", + "Installing collected packages: unsloth\r\n", + "Successfully installed unsloth-2024.10.7\r\n" + ] + } + ], + "source": [ + "#%%capture\n", + "!pip install pip3-autoremove\n", + "!pip-autoremove torch torchvision torchaudio -y\n", + "!pip install torch torchvision torchaudio xformers --index-url https://download.pytorch.org/whl/cu121\n", + "# https://github.com/unslothai/unsloth/issues/1284\n", + "!pip install unsloth[kaggle-new]\n", + "# Also get the latest nightly Unsloth!\n", + "!pip uninstall unsloth -y && pip install git+https://github.com/unslothai/unsloth.git@a2f8db3e7341f983af5814a2c56f54fa29ee548d" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6018b225", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:06:21.619747Z", + "iopub.status.busy": "2024-11-19T19:06:21.618961Z", + "iopub.status.idle": "2024-11-19T19:06:41.479598Z", + "shell.execute_reply": "2024-11-19T19:06:41.478738Z" + }, + "papermill": { + "duration": 19.925903, + "end_time": "2024-11-19T19:06:41.482153", + "exception": false, + "start_time": "2024-11-19T19:06:21.556250", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting git+https://github.com/unslothai/unsloth-zoo.git\r\n", + " Cloning https://github.com/unslothai/unsloth-zoo.git to /tmp/pip-req-build-0xpxksif\r\n", + " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth-zoo.git /tmp/pip-req-build-0xpxksif\r\n", + " Resolved https://github.com/unslothai/unsloth-zoo.git to commit f5421838ef8278cf96d0092d8271ecd6d433588c\r\n", + " Installing build dependencies ... \u001b[?25l-\b \b\\\b \b|\b \b/\b \bdone\r\n", + "\u001b[?25h Getting requirements to build wheel ... \u001b[?25l-\b \bdone\r\n", + "\u001b[?25h Preparing metadata (pyproject.toml) ... \u001b[?25l-\b \bdone\r\n", + "\u001b[?25hRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (2.5.1+cu121)\r\n", + "Requirement already satisfied: triton in /opt/conda/lib/python3.10/site-packages (from unsloth_zoo==2024.11.5) (3.1.0)\r\n", + "Requirement already satisfied: packaging in 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"2024-11-19T19:06:41.612002", + "exception": false, + "start_time": "2024-11-19T19:06:41.547396", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "* We support Llama, Mistral, CodeLlama, TinyLlama, Vicuna, Open Hermes etc\n", + "* And Yi, Qwen ([llamafied](https://huggingface.co/models?sort=trending&search=qwen+llama)), Deepseek, all Llama, Mistral derived archs.\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", + "* [**NEW**] 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." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "c7d55dc3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:06:41.737888Z", + "iopub.status.busy": "2024-11-19T19:06:41.737538Z", + "iopub.status.idle": "2024-11-19T19:08:58.672000Z", + "shell.execute_reply": "2024-11-19T19:08:58.671103Z" + }, + "id": "QmUBVEnvCDJv", + "outputId": "5eff0d61-05b4-471c-eea2-c2e84a915109", + "papermill": { + "duration": 136.999725, + "end_time": "2024-11-19T19:08:58.674026", + "exception": false, + "start_time": "2024-11-19T19:06:41.674301", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "==((====))== Unsloth 2024.10.7: Fast Llama patching. Transformers = 4.46.3.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.741 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.5.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post3. FA2 = False]\n", + " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", + "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "52307514a7d14c388004fc8ae3e7378e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors.index.json: 0%| | 0.00/23.9k [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "97c2f928e86f4374baa0f502ca5707e3", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Downloading shards: 0%| | 0/4 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "80a88015f8374bbd930529c4b9722389", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model-00001-of-00004.safetensors: 0%| | 0.00/4.98G [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d34a8cb6dd44a51a5ede4509989bb91", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model-00002-of-00004.safetensors: 0%| | 0.00/5.00G [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a953aa1afe6147e4896888080c1373ba", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model-00003-of-00004.safetensors: 0%| | 0.00/4.92G [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "679dfe3d327a41b2b518d55652625780", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model-00004-of-00004.safetensors: 0%| | 0.00/1.17G [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0b05cd87aeb141dd92f3756b79bf23e8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5412bb916c244149a7232f7cf8934dce", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "generation_config.json: 0%| | 0.00/194 [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e098d9b8d2124e30bd94fbc6e9161ad2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/50.9k [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8b9186a0443742c2ba93ae286db9885e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer.json: 0%| | 0.00/9.09M [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a81d251b9e494da1b759157f695e8d47", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "special_tokens_map.json: 0%| | 0.00/296 [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unsloth: We successfully patched the tokenizer to add a {% if add_generation_prompt %} to the chat_template.\n", + "This is not a bug, but please notify the Unsloth maintainers - thanks!\n", + "mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated does not have a padding token! Will use pad_token = <|finetune_right_pad_id|>.\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-bnb-4bit\",\n", + " \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n", + " \"unsloth/llama-2-7b-bnb-4bit\",\n", + " \"unsloth/llama-2-13b-bnb-4bit\",\n", + " \"unsloth/codellama-34b-bnb-4bit\",\n", + " \"unsloth/tinyllama-bnb-4bit\",\n", + " \"unsloth/llama-3-8b-bnb-4bit\",\n", + " \"unsloth/llama-3-70b-bnb-4bit\",\n", + "] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"mlabonne/Meta-Llama-3.1-8B-Instruct-abliterated\", # Choose ANY! eg teknium/OpenHermes-2.5-Mistral-7B\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", + "id": "2775c72b", + "metadata": { + "id": "SXd9bTZd1aaL", + "papermill": { + "duration": 0.072004, + "end_time": "2024-11-19T19:08:58.812761", + "exception": false, + "start_time": "2024-11-19T19:08:58.740757", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "We now add LoRA adapters so we only need to update 1 to 10% of all parameters!" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d4d1a72a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:08:58.951114Z", + "iopub.status.busy": "2024-11-19T19:08:58.950567Z", + "iopub.status.idle": "2024-11-19T19:09:04.490905Z", + "shell.execute_reply": "2024-11-19T19:09:04.490238Z" + }, + "id": "6bZsfBuZDeCL", + "outputId": "b630cc80-ff95-45a2-cc0d-38666010d73b", + "papermill": { + "duration": 5.61606, + "end_time": "2024-11-19T19:09:04.492928", + "exception": false, + "start_time": "2024-11-19T19:08:58.876868", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Unsloth 2024.10.7 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 = 32, # 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", + " use_gradient_checkpointing = \"unsloth\", # 4x longer contexts auto supported!\n", + " random_state = 3407,\n", + " use_rslora = False, # We support rank stabilized LoRA\n", + " loftq_config = None, # And LoftQ\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "cca764a5", + "metadata": { + "id": "vITh0KVJ10qX", + "papermill": { + "duration": 0.063926, + "end_time": "2024-11-19T19:09:04.622692", + "exception": false, + "start_time": "2024-11-19T19:09:04.558766", + "status": "completed" + }, + "tags": [] + }, + "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 `ChatML` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing).\n", + "\n", + "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "69a832a3", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:09:04.754265Z", + "iopub.status.busy": "2024-11-19T19:09:04.753481Z", + "iopub.status.idle": "2024-11-19T19:09:06.180842Z", + "shell.execute_reply": "2024-11-19T19:09:06.180121Z" + }, + "id": "LjY75GoYUCB8", + "outputId": "9f40f734-788c-4793-c1af-e9d003337612", + "papermill": { + "duration": 1.495636, + "end_time": "2024-11-19T19:09:06.182870", + "exception": false, + "start_time": "2024-11-19T19:09:04.687234", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from datasets import load_dataset\n", + "import json\n", + "from unsloth.chat_templates import get_chat_template\n", + "\n", + "tokenizer = get_chat_template(\n", + " tokenizer,\n", + " chat_template = \"llama-3\", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth\n", + " #mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"}, # ShareGPT style\n", + " map_eos_token = True, # Maps <|im_end|> to </s> instead\n", + ")\n", + "\n", + "def formatting_prompts_func(convos):\n", + " texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n", + " return { \"text\" : texts, }\n", + "\n", + "with open(\"/kaggle/input/the-group-chat/output-10k-c-dropout-nonames-replies.json\") as chatfile:\n", + " convos = [json.loads(j) for j in chatfile.readlines()]\n", + "\n", + "with open(\"/kaggle/input/toxicqa/toxicQAfinal.json\") as chatfile:\n", + " convos += [json.loads(j) for j in chatfile.readlines()]\n", + " \n", + "dataset = formatting_prompts_func(convos)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "6b4a347d", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:09:06.314334Z", + "iopub.status.busy": "2024-11-19T19:09:06.313377Z", + "iopub.status.idle": "2024-11-19T19:09:06.739552Z", + "shell.execute_reply": "2024-11-19T19:09:06.738597Z" + }, + "papermill": { + "duration": 0.493416, + "end_time": "2024-11-19T19:09:06.741610", + "exception": false, + "start_time": "2024-11-19T19:09:06.248194", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "dataset = Dataset.from_dict(dataset)" + ] + }, + { + "cell_type": "markdown", + "id": "4c45849c", + "metadata": { + "id": "idAEIeSQ3xdS", + "papermill": { + "duration": 0.064215, + "end_time": "2024-11-19T19:09:06.871810", + "exception": false, + "start_time": "2024-11-19T19:09:06.807595", + "status": "completed" + }, + "tags": [] + }, + "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`!" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7bbc400a", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:09:07.001740Z", + "iopub.status.busy": "2024-11-19T19:09:07.000573Z", + "iopub.status.idle": "2024-11-19T19:09:24.425284Z", + "shell.execute_reply": "2024-11-19T19:09:24.424466Z" + }, + "id": "95_Nn-89DhsL", + "outputId": "4b809e6d-271f-446f-dec8-abe0d13259f8", + "papermill": { + "duration": 17.491445, + "end_time": "2024-11-19T19:09:24.427211", + "exception": false, + "start_time": "2024-11-19T19:09:06.935766", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0f0c38ccb6c0402f84a66639ce3b0a2c", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map (num_proc=2): 0%| | 0/17983 [00:00<?, ? examples/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from trl import SFTTrainer\n", + "from transformers import TrainingArguments\n", + "\n", + "trainer = SFTTrainer(\n", + " model = model,\n", + " tokenizer = tokenizer,\n", + " train_dataset = dataset,\n", + " dataset_text_field = \"text\",\n", + " max_seq_length = max_seq_length,\n", + " dataset_num_proc = 2,\n", + " packing = False, # Can make training 5x faster for short sequences.\n", + " args = TrainingArguments(\n", + " per_device_train_batch_size = 2,\n", + " gradient_accumulation_steps = 4,\n", + " warmup_steps = 5,\n", + " num_train_epochs=1,\n", + " learning_rate = 2e-4,\n", + " fp16 = not torch.cuda.is_bf16_supported(),\n", + " bf16 = torch.cuda.is_bf16_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", + " report_to = \"none\",\n", + " ),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "5f90acfb", + "metadata": { + "cellView": "form", + "execution": { + "iopub.execute_input": "2024-11-19T19:09:24.559813Z", + "iopub.status.busy": "2024-11-19T19:09:24.558971Z", + "iopub.status.idle": "2024-11-19T19:09:24.564859Z", + "shell.execute_reply": "2024-11-19T19:09:24.564110Z" + }, + "id": "2ejIt2xSNKKp", + "outputId": "4815a050-0c0f-4a6a-9d93-b01c44eaea35", + "papermill": { + "duration": 0.072966, + "end_time": "2024-11-19T19:09:24.566638", + "exception": false, + "start_time": "2024-11-19T19:09:24.493672", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "GPU = Tesla T4. Max memory = 14.741 GB.\n", + "6.172 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": 9, + "id": "1a3a38b4", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-19T19:09:24.697522Z", + "iopub.status.busy": "2024-11-19T19:09:24.696820Z", + "iopub.status.idle": "2024-11-20T03:54:09.418782Z", + "shell.execute_reply": "2024-11-20T03:54:09.417866Z" + }, + "id": "yqxqAZ7KJ4oL", + "outputId": "3cf26aac-6042-4458-c4a6-d8849efb6a95", + "papermill": { + "duration": 31484.789349, + "end_time": "2024-11-20T03:54:09.420797", + "exception": false, + "start_time": "2024-11-19T19:09:24.631448", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", + " \\\\ /| Num examples = 17,983 | Num Epochs = 1\n", + "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", + "\\ / Total batch size = 8 | Total steps = 2,248\n", + " \"-____-\" Number of trainable parameters = 83,886,080\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + " <div>\n", + " \n", + " <progress value='2248' max='2248' style='width:300px; height:20px; vertical-align: middle;'></progress>\n", + " [2248/2248 8:44:22, Epoch 1/1]\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", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <td>1</td>\n", + " <td>2.630800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2</td>\n", + " <td>3.890600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>3</td>\n", + " <td>2.046200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>4</td>\n", + " <td>2.309300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>5</td>\n", + " <td>2.590900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>6</td>\n", + " <td>2.039400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>7</td>\n", + " <td>1.953500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>8</td>\n", + " <td>1.769300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>9</td>\n", + " <td>2.016900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>10</td>\n", + " <td>1.801700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>11</td>\n", + " <td>1.576400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>12</td>\n", + " <td>1.695400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>13</td>\n", + " <td>2.032200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>14</td>\n", + " <td>1.696800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>15</td>\n", + " <td>2.109500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>16</td>\n", + " <td>2.254800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>17</td>\n", + " <td>1.357900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>18</td>\n", + " <td>1.598300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>19</td>\n", + " <td>1.539700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>20</td>\n", + " <td>1.648300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>21</td>\n", + " <td>1.754000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>22</td>\n", + " <td>1.735000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>23</td>\n", + " <td>2.434300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>24</td>\n", + " <td>1.987900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>25</td>\n", + " <td>1.295100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>26</td>\n", + " <td>2.180100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>27</td>\n", + " <td>2.082700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>28</td>\n", + " <td>1.410300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>29</td>\n", + " <td>1.446500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>30</td>\n", + " <td>1.435300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>31</td>\n", + " <td>1.730600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>32</td>\n", + " <td>1.551800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>33</td>\n", + " <td>1.482700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>34</td>\n", + " <td>1.575600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>35</td>\n", + " <td>2.223500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>36</td>\n", + " <td>2.106000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>37</td>\n", + " <td>1.657500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>38</td>\n", + " <td>1.472100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>39</td>\n", + " <td>1.612800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>40</td>\n", + " <td>1.556300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>41</td>\n", + " <td>1.471300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>42</td>\n", + " <td>1.350800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>43</td>\n", + " <td>1.383000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>44</td>\n", + " <td>1.837300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>45</td>\n", + " <td>1.466900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>46</td>\n", + " <td>1.402600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>47</td>\n", + " <td>1.303800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>48</td>\n", + " <td>1.289400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>49</td>\n", + " <td>2.615500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>50</td>\n", + " <td>1.423800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>51</td>\n", + " <td>1.415600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>52</td>\n", + " <td>1.592000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>53</td>\n", + " <td>1.259700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>54</td>\n", + " <td>1.572500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>55</td>\n", + " <td>1.458800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>56</td>\n", + " <td>1.322500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>57</td>\n", + " <td>1.411800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>58</td>\n", + " <td>1.847200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>59</td>\n", + " <td>1.725800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>60</td>\n", + " <td>1.620000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>61</td>\n", + " <td>1.664900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>62</td>\n", + " <td>1.662400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>63</td>\n", + " <td>2.695700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>64</td>\n", + " <td>1.526500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>65</td>\n", + " <td>1.645500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>66</td>\n", + " <td>1.431200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>67</td>\n", + " <td>2.222500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>68</td>\n", + " <td>1.723900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>69</td>\n", + " <td>1.636600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>70</td>\n", + " <td>1.557700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>71</td>\n", + " <td>1.690900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>72</td>\n", + " <td>2.912400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>73</td>\n", + " <td>1.290300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>74</td>\n", + " <td>1.954400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>75</td>\n", + " <td>1.888500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>76</td>\n", + " <td>1.399600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>77</td>\n", + " <td>1.522700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>78</td>\n", + " <td>1.376900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>79</td>\n", + " <td>1.562900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>80</td>\n", + " <td>1.479700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>81</td>\n", + " <td>1.277100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>82</td>\n", + " <td>1.612200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>83</td>\n", + " <td>1.596400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>84</td>\n", + " <td>1.767900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>85</td>\n", + " <td>1.235800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>86</td>\n", + " <td>1.574400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>87</td>\n", + " <td>1.754300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>88</td>\n", + " <td>2.280800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>89</td>\n", + " <td>1.484400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>90</td>\n", + " <td>1.970000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>91</td>\n", + " <td>2.784900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>92</td>\n", + " <td>1.193300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>93</td>\n", + " <td>1.194500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>94</td>\n", + " <td>1.298700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>95</td>\n", + " <td>1.497400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>96</td>\n", + " <td>1.489800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>97</td>\n", + " <td>1.329000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>98</td>\n", + " <td>1.558400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>99</td>\n", + " <td>1.790600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>100</td>\n", + " <td>1.515000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>101</td>\n", + " <td>1.375700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>102</td>\n", + " <td>1.339900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>103</td>\n", + " <td>1.910300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>104</td>\n", + " <td>1.336000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>105</td>\n", + " <td>1.809300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>106</td>\n", + " <td>1.585100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>107</td>\n", + " <td>1.396300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>108</td>\n", + " <td>1.708700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>109</td>\n", + " <td>1.253400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>110</td>\n", + " <td>1.390500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>111</td>\n", + " <td>1.351900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>112</td>\n", + " <td>1.464300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>113</td>\n", + " <td>1.383400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>114</td>\n", + " <td>1.834200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>115</td>\n", + " <td>1.374400</td>\n", + " </tr>\n", + " <tr>\n", + " 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<td>1.336700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>157</td>\n", + " <td>1.375500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>158</td>\n", + " <td>1.764100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>159</td>\n", + " <td>1.345000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>160</td>\n", + " <td>1.140800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>161</td>\n", + " <td>1.412700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>162</td>\n", + " <td>1.752300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>163</td>\n", + " <td>1.368300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>164</td>\n", + " <td>1.309200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>165</td>\n", + " <td>1.354100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>166</td>\n", + " <td>1.696000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>167</td>\n", + " <td>1.723500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>168</td>\n", + " <td>1.511200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>169</td>\n", + " <td>1.483300</td>\n", + " </tr>\n", + 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<td>1.449300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1874</td>\n", + " <td>1.752000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1875</td>\n", + " <td>1.119500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1876</td>\n", + " <td>1.629200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1877</td>\n", + " <td>1.250900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1878</td>\n", + " <td>1.278500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1879</td>\n", + " <td>1.146100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1880</td>\n", + " <td>1.473300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1881</td>\n", + " <td>1.767300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1882</td>\n", + " <td>2.117000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1883</td>\n", + " <td>1.203400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1884</td>\n", + " <td>1.110900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1885</td>\n", + " <td>1.209700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1886</td>\n", + " <td>1.846700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1887</td>\n", + " <td>1.157100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1888</td>\n", + " <td>1.283200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1889</td>\n", + " <td>1.315900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1890</td>\n", + " <td>1.324700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1891</td>\n", + " <td>1.127500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1892</td>\n", + " <td>1.395200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1893</td>\n", + " <td>1.597100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1894</td>\n", + " <td>1.311900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1895</td>\n", + " <td>1.535100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1896</td>\n", + " <td>1.238000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1897</td>\n", + " <td>1.085500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1898</td>\n", + " <td>2.029100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1899</td>\n", + " <td>1.333500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1900</td>\n", + " <td>2.012700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1901</td>\n", + " <td>1.641400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1902</td>\n", + " <td>1.488000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1903</td>\n", + " <td>1.340500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1904</td>\n", + " <td>1.455900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1905</td>\n", + " <td>1.677300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1906</td>\n", + " <td>1.308700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1907</td>\n", + " <td>1.223900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1908</td>\n", + " <td>1.346900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1909</td>\n", + " <td>1.164800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1910</td>\n", + " <td>1.174300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1911</td>\n", + " <td>1.026200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1912</td>\n", + " <td>1.380600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1913</td>\n", + " <td>1.522100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1914</td>\n", + " <td>1.313400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1915</td>\n", + " <td>1.511100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1916</td>\n", + " <td>1.089300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1917</td>\n", + " <td>1.535000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1918</td>\n", + " <td>1.491000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1919</td>\n", + " <td>2.140200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1920</td>\n", + " <td>1.641000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1921</td>\n", + " <td>1.373200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1922</td>\n", + " <td>1.744200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1923</td>\n", + " <td>1.527400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1924</td>\n", + " <td>1.944600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1925</td>\n", + " <td>1.717700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1926</td>\n", + " <td>1.371700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1927</td>\n", + " <td>1.276700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1928</td>\n", + " <td>1.350800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1929</td>\n", + " <td>1.415100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1930</td>\n", + " <td>1.429200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1931</td>\n", + " <td>1.726000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1932</td>\n", + " <td>1.432200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1933</td>\n", + " <td>1.130500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1934</td>\n", + " <td>1.152500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1935</td>\n", + " <td>1.406900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1936</td>\n", + " <td>0.945800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1937</td>\n", + " <td>2.123700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1938</td>\n", + " <td>1.462600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1939</td>\n", + " <td>1.302800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1940</td>\n", + " <td>1.542700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1941</td>\n", + " <td>1.646700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1942</td>\n", + " <td>1.091100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1943</td>\n", + " <td>1.525800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1944</td>\n", + " <td>1.805100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1945</td>\n", + " <td>1.385600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1946</td>\n", + " <td>1.384300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1947</td>\n", + " <td>1.424400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1948</td>\n", + " <td>1.356500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1949</td>\n", + " <td>1.430500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1950</td>\n", + " <td>1.129100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1951</td>\n", + " <td>1.396000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1952</td>\n", + " <td>1.267200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1953</td>\n", + " <td>1.109400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1954</td>\n", + " <td>1.476600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1955</td>\n", + " <td>1.661100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1956</td>\n", + " <td>1.362800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1957</td>\n", + " <td>1.185100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1958</td>\n", + " <td>1.316000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1959</td>\n", + " <td>1.235400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1960</td>\n", + " <td>1.674900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1961</td>\n", + " <td>1.447400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1962</td>\n", + " <td>1.646300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1963</td>\n", + " <td>1.040400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1964</td>\n", + " <td>1.741700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1965</td>\n", + " <td>1.412700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1966</td>\n", + " <td>1.575200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1967</td>\n", + " <td>1.043200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1968</td>\n", + " <td>1.716600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1969</td>\n", + " <td>1.285700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1970</td>\n", + " <td>1.453900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1971</td>\n", + " <td>1.383000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1972</td>\n", + " <td>1.758500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1973</td>\n", + " <td>1.173800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1974</td>\n", + " <td>1.188800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1975</td>\n", + " <td>1.487500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1976</td>\n", + " <td>1.367200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1977</td>\n", + " <td>1.105000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1978</td>\n", + " <td>1.591300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1979</td>\n", + " <td>1.161100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1980</td>\n", + " <td>1.501300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1981</td>\n", + " <td>1.301500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1982</td>\n", + " <td>1.481200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1983</td>\n", + " <td>1.153500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1984</td>\n", + " <td>1.289400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1985</td>\n", + " <td>1.539300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1986</td>\n", + " <td>1.703700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1987</td>\n", + " <td>1.267300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1988</td>\n", + " <td>1.294200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1989</td>\n", + " <td>1.357100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1990</td>\n", + " <td>1.253700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1991</td>\n", + " <td>1.334600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1992</td>\n", + " <td>1.718800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1993</td>\n", + " <td>1.563400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1994</td>\n", + " <td>1.647900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1995</td>\n", + " <td>1.547600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1996</td>\n", + " <td>1.389200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1997</td>\n", + " <td>1.322900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1998</td>\n", + " <td>1.340500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>1999</td>\n", + " <td>1.504700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2000</td>\n", + " <td>1.334000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2001</td>\n", + " <td>1.203100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2002</td>\n", + " <td>1.322800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2003</td>\n", + " <td>1.123500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2004</td>\n", + " <td>1.375200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2005</td>\n", + " <td>1.306000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2006</td>\n", + " <td>1.186800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2007</td>\n", + " <td>1.512000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2008</td>\n", + " <td>1.284300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2009</td>\n", + " <td>1.442800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2010</td>\n", + " <td>1.155800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2011</td>\n", + " <td>1.905600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2012</td>\n", + " <td>1.182600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2013</td>\n", + " <td>1.731600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2014</td>\n", + " <td>1.117500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2015</td>\n", + " <td>1.741300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2016</td>\n", + " <td>1.252900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2017</td>\n", + " <td>1.029700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2018</td>\n", + " <td>1.505600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2019</td>\n", + " <td>1.401000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2020</td>\n", + " <td>1.187700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2021</td>\n", + " <td>1.833800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2022</td>\n", + " <td>1.286800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2023</td>\n", + " <td>1.372400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2024</td>\n", + " <td>1.391300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2025</td>\n", + " <td>1.304800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2026</td>\n", + " <td>1.163900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2027</td>\n", + " <td>1.471400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2028</td>\n", + " <td>1.281000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2029</td>\n", + " <td>1.183200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2030</td>\n", + " <td>1.678900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2031</td>\n", + " <td>1.595700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2032</td>\n", + " <td>1.195000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2033</td>\n", + " <td>1.263200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2034</td>\n", + " <td>1.158200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2035</td>\n", + " <td>1.103000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2036</td>\n", + " <td>1.349300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2037</td>\n", + " <td>1.183100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2038</td>\n", + " <td>1.350600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2039</td>\n", + " <td>1.523100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2040</td>\n", + " <td>1.237700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2041</td>\n", + " <td>1.607700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2042</td>\n", + " <td>1.245600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2043</td>\n", + " <td>1.104900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2044</td>\n", + " <td>1.557800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2045</td>\n", + " <td>1.367800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2046</td>\n", + " <td>1.236800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2047</td>\n", + " <td>1.188600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2048</td>\n", + " <td>1.180500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2049</td>\n", + " <td>1.279400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2050</td>\n", + " <td>1.853500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2051</td>\n", + " <td>1.236400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2052</td>\n", + " <td>1.266600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2053</td>\n", + " <td>1.298100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2054</td>\n", + " <td>1.339700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2055</td>\n", + " <td>1.247300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2056</td>\n", + " <td>1.892200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2057</td>\n", + " <td>1.289800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2058</td>\n", + " <td>1.443800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2059</td>\n", + " <td>1.269000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2060</td>\n", + " <td>1.321000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2061</td>\n", + " <td>1.594500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2062</td>\n", + " <td>1.992100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2063</td>\n", + " <td>1.409600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2064</td>\n", + " <td>1.185900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2065</td>\n", + " <td>1.257600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2066</td>\n", + " <td>1.630700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2067</td>\n", + " <td>1.443100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2068</td>\n", + " <td>1.848100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2069</td>\n", + " <td>1.965000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2070</td>\n", + " <td>1.972600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2071</td>\n", + " <td>1.723600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2072</td>\n", + " <td>1.100800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2073</td>\n", + " <td>1.829900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2074</td>\n", + " <td>1.374600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2075</td>\n", + " <td>1.558600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2076</td>\n", + " <td>1.320900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2077</td>\n", + " <td>1.538300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2078</td>\n", + " <td>1.125100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2079</td>\n", + " <td>1.539000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2080</td>\n", + " <td>1.351400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2081</td>\n", + " <td>1.666900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2082</td>\n", + " <td>1.358900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2083</td>\n", + " <td>1.170800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2084</td>\n", + " <td>1.263400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2085</td>\n", + " <td>1.038400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2086</td>\n", + " <td>1.350100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2087</td>\n", + " <td>1.527600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2088</td>\n", + " <td>1.416600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2089</td>\n", + " <td>1.632500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2090</td>\n", + " <td>1.022900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2091</td>\n", + " <td>1.270300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2092</td>\n", + " <td>1.265800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2093</td>\n", + " <td>1.895400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2094</td>\n", + " <td>1.294000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2095</td>\n", + " <td>1.276000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2096</td>\n", + " <td>1.436200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2097</td>\n", + " <td>1.248000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2098</td>\n", + " <td>1.505700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2099</td>\n", + " <td>1.201300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2100</td>\n", + " <td>1.612800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2101</td>\n", + " <td>1.577500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2102</td>\n", + " <td>2.045800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2103</td>\n", + " <td>1.448800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2104</td>\n", + " <td>1.463300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2105</td>\n", + " <td>1.385300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2106</td>\n", + " <td>1.318200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2107</td>\n", + " <td>1.241900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2108</td>\n", + " <td>2.427100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2109</td>\n", + " <td>1.897000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2110</td>\n", + " <td>2.441200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2111</td>\n", + " <td>1.286000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2112</td>\n", + " <td>1.421300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2113</td>\n", + " <td>1.428900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2114</td>\n", + " <td>1.471300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2115</td>\n", + " <td>1.356700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2116</td>\n", + " <td>1.223000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2117</td>\n", + " <td>1.253100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2118</td>\n", + " <td>1.542300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2119</td>\n", + " <td>1.530200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2120</td>\n", + " <td>1.381900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2121</td>\n", + " <td>1.474300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2122</td>\n", + " <td>1.542500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2123</td>\n", + " <td>1.249200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2124</td>\n", + " <td>1.272600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2125</td>\n", + " <td>1.536700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2126</td>\n", + " <td>1.666900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2127</td>\n", + " <td>1.646300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2128</td>\n", + " <td>1.243100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2129</td>\n", + " <td>1.347400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2130</td>\n", + " <td>1.240400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2131</td>\n", + " <td>1.707300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2132</td>\n", + " <td>1.480700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2133</td>\n", + " <td>1.199700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2134</td>\n", + " <td>1.202100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2135</td>\n", + " <td>1.802800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2136</td>\n", + " <td>1.467500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2137</td>\n", + " <td>1.199000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2138</td>\n", + " <td>1.374700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2139</td>\n", + " <td>1.688600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2140</td>\n", + " <td>1.698300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2141</td>\n", + " <td>1.324000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2142</td>\n", + " <td>1.414500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2143</td>\n", + " <td>1.875900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2144</td>\n", + " <td>1.325200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2145</td>\n", + " <td>1.566500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2146</td>\n", + " <td>1.250600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2147</td>\n", + " <td>1.428000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2148</td>\n", + " <td>1.498400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2149</td>\n", + " <td>1.564300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2150</td>\n", + " <td>1.161100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2151</td>\n", + " <td>1.302200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2152</td>\n", + " <td>2.096400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2153</td>\n", + " <td>2.035500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2154</td>\n", + " <td>1.613100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2155</td>\n", + " <td>1.231100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2156</td>\n", + " <td>1.586100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2157</td>\n", + " <td>1.632300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2158</td>\n", + " <td>1.241100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2159</td>\n", + " <td>1.634800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2160</td>\n", + " <td>1.406300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2161</td>\n", + " <td>1.202800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2162</td>\n", + " <td>1.786200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2163</td>\n", + " <td>1.317200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2164</td>\n", + " <td>1.662700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2165</td>\n", + " <td>1.107200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2166</td>\n", + " <td>1.316000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2167</td>\n", + " <td>1.307700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2168</td>\n", + " <td>1.530900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2169</td>\n", + " <td>1.149300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2170</td>\n", + " <td>1.932500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2171</td>\n", + " <td>1.565200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2172</td>\n", + " <td>1.171800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2173</td>\n", + " <td>1.433600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2174</td>\n", + " <td>1.202100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2175</td>\n", + " <td>1.938400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2176</td>\n", + " <td>1.752000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2177</td>\n", + " <td>1.347400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2178</td>\n", + " <td>1.149800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2179</td>\n", + " <td>1.058000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2180</td>\n", + " <td>1.166900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2181</td>\n", + " <td>1.536500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2182</td>\n", + " <td>1.125400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2183</td>\n", + " <td>1.385100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2184</td>\n", + " <td>1.353000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2185</td>\n", + " <td>1.516800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2186</td>\n", + " <td>1.530400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2187</td>\n", + " <td>1.435800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2188</td>\n", + " <td>1.716300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2189</td>\n", + " <td>1.272100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2190</td>\n", + " <td>2.123100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2191</td>\n", + " <td>1.586500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2192</td>\n", + " <td>1.136500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2193</td>\n", + " <td>1.392300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2194</td>\n", + " <td>1.025900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2195</td>\n", + " <td>1.360300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2196</td>\n", + " <td>1.496100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2197</td>\n", + " <td>2.067000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2198</td>\n", + " <td>1.226700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2199</td>\n", + " <td>1.702900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2200</td>\n", + " <td>1.249700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2201</td>\n", + " <td>1.100700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2202</td>\n", + " <td>0.975700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2203</td>\n", + " <td>1.589000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2204</td>\n", + " <td>1.240000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2205</td>\n", + " <td>1.398200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2206</td>\n", + " <td>1.490700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2207</td>\n", + " <td>1.447900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2208</td>\n", + " <td>1.478700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2209</td>\n", + " <td>1.427600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2210</td>\n", + " <td>1.725500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2211</td>\n", + " <td>1.476800</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2212</td>\n", + " <td>1.958500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2213</td>\n", + " <td>1.426400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2214</td>\n", + " <td>1.639300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2215</td>\n", + " <td>1.646200</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2216</td>\n", + " <td>1.823300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2217</td>\n", + " <td>1.333400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2218</td>\n", + " <td>1.142500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2219</td>\n", + " <td>1.508600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2220</td>\n", + " <td>2.200100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2221</td>\n", + " <td>1.579700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2222</td>\n", + " <td>1.151400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2223</td>\n", + " <td>1.449600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2224</td>\n", + " <td>1.169100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2225</td>\n", + " <td>1.495000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2226</td>\n", + " <td>1.555500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2227</td>\n", + " <td>1.301300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2228</td>\n", + " <td>1.158000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2229</td>\n", + " <td>1.273100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2230</td>\n", + " <td>1.725400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2231</td>\n", + " <td>1.451500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2232</td>\n", + " <td>1.227900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2233</td>\n", + " <td>1.666000</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2234</td>\n", + " <td>1.284600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2235</td>\n", + " <td>1.223300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2236</td>\n", + " <td>1.857500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2237</td>\n", + " <td>1.610700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2238</td>\n", + " <td>1.853600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2239</td>\n", + " <td>1.503600</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2240</td>\n", + " <td>1.569900</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2241</td>\n", + " <td>1.335400</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2242</td>\n", + " <td>1.489300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2243</td>\n", + " <td>1.528300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2244</td>\n", + " <td>1.360300</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2245</td>\n", + " <td>1.085500</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2246</td>\n", + " <td>1.272100</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2247</td>\n", + " <td>1.243700</td>\n", + " </tr>\n", + " <tr>\n", + " <td>2248</td>\n", + " <td>1.471000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table><p>" + ], + "text/plain": [ + "<IPython.core.display.HTML object>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "c4c8f35c", + "metadata": { + "cellView": "form", + "execution": { + "iopub.execute_input": "2024-11-20T03:54:09.565224Z", + "iopub.status.busy": "2024-11-20T03:54:09.564605Z", + "iopub.status.idle": "2024-11-20T03:54:09.570920Z", + "shell.execute_reply": "2024-11-20T03:54:09.570099Z" + }, + "id": "pCqnaKmlO1U9", + "outputId": "cf63d152-e152-468c-ba0d-938e0d2f71a0", + "papermill": { + "duration": 0.079663, + "end_time": "2024-11-20T03:54:09.572687", + "exception": false, + "start_time": "2024-11-20T03:54:09.493024", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31481.3839 seconds used for training.\n", + "524.69 minutes used for training.\n", + "Peak reserved memory = 9.363 GB.\n", + "Peak reserved memory for training = 3.191 GB.\n", + "Peak reserved memory % of max memory = 63.517 %.\n", + "Peak reserved memory for training % of max memory = 21.647 %.\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": "markdown", + "id": "6176fe1e", + "metadata": { + "id": "ekOmTR1hSNcr", + "papermill": { + "duration": 0.070529, + "end_time": "2024-11-20T03:54:09.714353", + "exception": false, + "start_time": "2024-11-20T03:54:09.643824", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "<a name=\"Inference\"></a>\n", + "### Inference\n", + "Let's run the model! You can change the instruction and input - leave the output blank!" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "04171c34", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:09.856688Z", + "iopub.status.busy": "2024-11-20T03:54:09.855912Z", + "iopub.status.idle": "2024-11-20T03:54:09.860764Z", + "shell.execute_reply": "2024-11-20T03:54:09.859922Z" + }, + "id": "kR3gIAX-SM2q", + "outputId": "5b71f982-38c0-44c8-a4e5-58cd20b5a585", + "papermill": { + "duration": 0.077355, + "end_time": "2024-11-20T03:54:09.862368", + "exception": false, + "start_time": "2024-11-20T03:54:09.785013", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " # alpaca_prompt = Copied from above\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + " inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # 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", + " tokenizer.batch_decode(outputs)" + ] + }, + { + "cell_type": "markdown", + "id": "ec51acf7", + "metadata": { + "id": "CrSvZObor0lY", + "papermill": { + "duration": 0.070507, + "end_time": "2024-11-20T03:54:10.004648", + "exception": false, + "start_time": "2024-11-20T03:54:09.934141", + "status": "completed" + }, + "tags": [] + }, + "source": [ + " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "97035490", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:10.145654Z", + "iopub.status.busy": "2024-11-20T03:54:10.145402Z", + "iopub.status.idle": "2024-11-20T03:54:10.150359Z", + "shell.execute_reply": "2024-11-20T03:54:10.149572Z" + }, + "id": "e2pEuRb1r2Vg", + "outputId": "084aab62-2122-436a-c0cb-8871986640eb", + "papermill": { + "duration": 0.077256, + "end_time": "2024-11-20T03:54:10.151976", + "exception": false, + "start_time": "2024-11-20T03:54:10.074720", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " # alpaca_prompt = Copied from above\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + " inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + " ], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + " from transformers import TextStreamer\n", + " text_streamer = TextStreamer(tokenizer)\n", + " _ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ] + }, + { + "cell_type": "markdown", + "id": "a4179ebd", + "metadata": { + "id": "uMuVrWbjAzhc", + "papermill": { + "duration": 0.070519, + "end_time": "2024-11-20T03:54:10.292849", + "exception": false, + "start_time": "2024-11-20T03:54:10.222330", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "<a name=\"Save\"></a>\n", + "### Saving, loading finetuned models\n", + "To save the final model as LoRA adapters, either use Huggingface's `push_to_hub` for an online save or `save_pretrained` for a local save.\n", + "\n", + "**[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "3974619b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:10.434709Z", + "iopub.status.busy": "2024-11-20T03:54:10.434458Z", + "iopub.status.idle": "2024-11-20T03:54:15.042600Z", + "shell.execute_reply": "2024-11-20T03:54:15.041442Z" + }, + "id": "upcOlWe7A1vc", + "papermill": { + "duration": 4.681486, + "end_time": "2024-11-20T03:54:15.044402", + "exception": false, + "start_time": "2024-11-20T03:54:10.362916", + "status": "completed" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "20ebd44d861249a9bbba6b4d7cf7a1af", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "README.md: 0%| | 0.00/615 [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5699440a265c4a44a6f21a6551c0a49e", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1 [00:00<?, ?it/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "208d530d5bc9489c84b120adee331875", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "adapter_model.safetensors: 0%| | 0.00/336M [00:00<?, ?B/s]" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved model to https://huggingface.co/scoliono/groupchat_lora_abliterated_instruct-3.1-8b\n" + ] + } + ], + "source": [ + "#model.save_pretrained(\"lora_model\") # Local saving\n", + "from kaggle_secrets import UserSecretsClient\n", + "user_secrets = UserSecretsClient()\n", + "hf_token = user_secrets.get_secret(\"hf_token\")\n", + "\n", + "model.push_to_hub(\"scoliono/groupchat_lora_abliterated_instruct-3.1-8b\", token = hf_token)" + ] + }, + { + "cell_type": "markdown", + "id": "28be027e", + "metadata": { + "id": "AEEcJ4qfC7Lp", + "papermill": { + "duration": 0.070774, + "end_time": "2024-11-20T03:54:15.188324", + "exception": false, + "start_time": "2024-11-20T03:54:15.117550", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f190efeb", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:15.329954Z", + "iopub.status.busy": "2024-11-20T03:54:15.329369Z", + "iopub.status.idle": "2024-11-20T03:54:15.334813Z", + "shell.execute_reply": "2024-11-20T03:54:15.333988Z" + }, + "id": "MKX_XKs_BNZR", + "outputId": "05e5a193-dab0-41db-e07c-4b3afbdd7932", + "papermill": { + "duration": 0.077834, + "end_time": "2024-11-20T03:54:15.336380", + "exception": false, + "start_time": "2024-11-20T03:54:15.258546", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " from unsloth import FastLanguageModel\n", + " model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"scoliono/groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "\n", + " # alpaca_prompt = You MUST copy from above!\n", + "\n", + " inputs = tokenizer(\n", + " [\n", + " alpaca_prompt.format(\n", + " \"What is a famous tall tower in Paris?\", # instruction\n", + " \"\", # 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", + " tokenizer.batch_decode(outputs)" + ] + }, + { + "cell_type": "markdown", + "id": "0dbf42b6", + "metadata": { + "id": "QQMjaNrjsU5_", + "papermill": { + "duration": 0.070237, + "end_time": "2024-11-20T03:54:15.514274", + "exception": false, + "start_time": "2024-11-20T03:54:15.444037", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "ec3b9df7", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:15.656780Z", + "iopub.status.busy": "2024-11-20T03:54:15.656173Z", + "iopub.status.idle": "2024-11-20T03:54:15.660705Z", + "shell.execute_reply": "2024-11-20T03:54:15.659857Z" + }, + "id": "yFfaXG0WsQuE", + "papermill": { + "duration": 0.077345, + "end_time": "2024-11-20T03:54:15.662229", + "exception": false, + "start_time": "2024-11-20T03:54:15.584884", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "if False:\n", + " # I highly do NOT suggest - use Unsloth if possible\n", + " from peft import AutoPeftModelForCausalLM\n", + " from transformers import AutoTokenizer\n", + " model = AutoPeftModelForCausalLM.from_pretrained(\n", + " \"groupchat_lora_abliterated_instruct-3.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n", + " load_in_4bit = load_in_4bit,\n", + " )\n", + " tokenizer = AutoTokenizer.from_pretrained(\"groupchat_lora_abliterated_instruct-3.1-8b\")" + ] + }, + { + "cell_type": "markdown", + "id": "b599046e", + "metadata": { + "id": "f422JgM9sdVT", + "papermill": { + "duration": 0.07051, + "end_time": "2024-11-20T03:54:15.803541", + "exception": false, + "start_time": "2024-11-20T03:54:15.733031", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### Saving to float16 for VLLM\n", + "\n", + "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "56eec57b", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:15.946500Z", + "iopub.status.busy": "2024-11-20T03:54:15.945712Z", + "iopub.status.idle": "2024-11-20T03:54:15.951204Z", + "shell.execute_reply": "2024-11-20T03:54:15.950368Z" + }, + "id": "iHjt_SMYsd3P", + "papermill": { + "duration": 0.079821, + "end_time": "2024-11-20T03:54:15.952812", + "exception": false, + "start_time": "2024-11-20T03:54:15.872991", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Merge to 16bit\n", + "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", + "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_16bit\", 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 = \"\")" + ] + }, + { + "cell_type": "markdown", + "id": "d87b3974", + "metadata": { + "id": "TCv4vXHd61i7", + "papermill": { + "duration": 0.0692, + "end_time": "2024-11-20T03:54:16.091909", + "exception": false, + "start_time": "2024-11-20T03:54:16.022709", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "### GGUF / llama.cpp Conversion\n", + "To save to `GGUF` / `llama.cpp`, we support it natively now! We clone `llama.cpp` and we default save it to `q8_0`. We allow all methods like `q4_k_m`. Use `save_pretrained_gguf` for local saving and `push_to_hub_gguf` for uploading to HF.\n", + "\n", + "Some supported quant methods (full list on our [Wiki page](https://github.com/unslothai/unsloth/wiki#gguf-quantization-options)):\n", + "* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n", + "* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n", + "* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ec62bb3e", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-20T03:54:16.232549Z", + "iopub.status.busy": "2024-11-20T03:54:16.232264Z", + "iopub.status.idle": "2024-11-20T03:54:16.237502Z", + "shell.execute_reply": "2024-11-20T03:54:16.236662Z" + }, + "id": "FqfebeAdT073", + "papermill": { + "duration": 0.07695, + "end_time": "2024-11-20T03:54:16.239011", + "exception": false, + "start_time": "2024-11-20T03:54:16.162061", + "status": "completed" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Save to 8bit Q8_0\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", + "\n", + "# Save to 16bit GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", + "\n", + "# Save to q4_k_m GGUF\n", + "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", + "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" + ] + }, + { + "cell_type": "markdown", + "id": "8bb60882", + "metadata": { + "id": "bDp0zNpwe6U_", + "papermill": { + "duration": 0.070344, + "end_time": "2024-11-20T03:54:16.380192", + "exception": false, + "start_time": "2024-11-20T03:54:16.309848", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." + ] + }, + { + "cell_type": "markdown", + "id": "e36cffc3", + "metadata": { + "id": "Zt9CHJqO6p30", + "papermill": { + "duration": 0.070243, + "end_time": "2024-11-20T03:54:16.520198", + "exception": false, + "start_time": "2024-11-20T03:54:16.449955", + "status": "completed" + }, + "tags": [] + }, + "source": [ + "And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/u54VK8m8tk) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n", + "\n", + "Some other links:\n", + "1. Zephyr DPO 2x faster [free Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing)\n", + "2. Llama 7b 2x faster [free Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing)\n", + "3. TinyLlama 4x faster full Alpaca 52K in 1 hour [free Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing)\n", + "4. CodeLlama 34b 2x faster [A100 on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing)\n", + "5. Mistral 7b [free Kaggle version](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook)\n", + "6. We also did a [blog](https://huggingface.co/blog/unsloth-trl) with 🤗 HuggingFace, and we're in the TRL [docs](https://huggingface.co/docs/trl/main/en/sft_trainer#accelerate-fine-tuning-2x-using-unsloth)!\n", + "7. `ChatML` for ShareGPT datasets, [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing)\n", + "8. Text completions like novel writing [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)\n", + "\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.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> Support our work if you can! 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