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run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n
\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":"\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 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":"\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":"\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":"\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","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", + "\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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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 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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 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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": [ + "\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 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": [ + "\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", + "| Step | \n", + "Training Loss | \n", + "
|---|---|
| 1 | \n", + "2.630800 | \n", + "
| 2 | \n", + "3.890600 | \n", + "
| 3 | \n", + "2.046200 | \n", + "
| 4 | \n", + "2.309300 | \n", + "
| 5 | \n", + "2.590900 | \n", + "
| 6 | \n", + "2.039400 | \n", + "
| 7 | \n", + "1.953500 | \n", + "
| 8 | \n", + "1.769300 | \n", + "
| 9 | \n", + "2.016900 | \n", + "
| 10 | \n", + "1.801700 | \n", + "
| 11 | \n", + "1.576400 | \n", + "
| 12 | \n", + "1.695400 | \n", + "
| 13 | \n", + "2.032200 | \n", + "
| 14 | \n", + "1.696800 | \n", + "
| 15 | \n", + "2.109500 | \n", + "
| 16 | \n", + "2.254800 | \n", + "
| 17 | \n", + "1.357900 | \n", + "
| 18 | \n", + "1.598300 | \n", + "
| 19 | \n", + "1.539700 | \n", + "
| 20 | \n", + "1.648300 | \n", + "
| 21 | \n", + "1.754000 | \n", + "
| 22 | \n", + "1.735000 | \n", + "
| 23 | \n", + "2.434300 | \n", + "
| 24 | \n", + "1.987900 | \n", + "
| 25 | \n", + "1.295100 | \n", + "
| 26 | \n", + "2.180100 | \n", + "
| 27 | \n", + "2.082700 | \n", + "
| 28 | \n", + "1.410300 | \n", + "
| 29 | \n", + "1.446500 | \n", + "
| 30 | \n", + "1.435300 | \n", + "
| 31 | \n", + "1.730600 | \n", + "
| 32 | \n", + "1.551800 | \n", + "
| 33 | \n", + "1.482700 | \n", + "
| 34 | \n", + "1.575600 | \n", + "
| 35 | \n", + "2.223500 | \n", + "
| 36 | \n", + "2.106000 | \n", + "
| 37 | \n", + "1.657500 | \n", + "
| 38 | \n", + "1.472100 | \n", + "
| 39 | \n", + "1.612800 | \n", + "
| 40 | \n", + "1.556300 | \n", + "
| 41 | \n", + "1.471300 | \n", + "
| 42 | \n", + "1.350800 | \n", + "
| 43 | \n", + "1.383000 | \n", + "
| 44 | \n", + "1.837300 | \n", + "
| 45 | \n", + "1.466900 | \n", + "
| 46 | \n", + "1.402600 | \n", + "
| 47 | \n", + "1.303800 | \n", + "
| 48 | \n", + "1.289400 | \n", + "
| 49 | \n", + "2.615500 | \n", + "
| 50 | \n", + "1.423800 | \n", + "
| 51 | \n", + "1.415600 | \n", + "
| 52 | \n", + "1.592000 | \n", + "
| 53 | \n", + "1.259700 | \n", + "
| 54 | \n", + "1.572500 | \n", + "
| 55 | \n", + "1.458800 | \n", + "
| 56 | \n", + "1.322500 | \n", + "
| 57 | \n", + "1.411800 | \n", + "
| 58 | \n", + "1.847200 | \n", + "
| 59 | \n", + "1.725800 | \n", + "
| 60 | \n", + "1.620000 | \n", + "
| 61 | \n", + "1.664900 | \n", + "
| 62 | \n", + "1.662400 | \n", + "
| 63 | \n", + "2.695700 | \n", + "
| 64 | \n", + "1.526500 | \n", + "
| 65 | \n", + "1.645500 | \n", + "
| 66 | \n", + "1.431200 | \n", + "
| 67 | \n", + "2.222500 | \n", + "
| 68 | \n", + "1.723900 | \n", + "
| 69 | \n", + "1.636600 | \n", + "
| 70 | \n", + "1.557700 | \n", + "
| 71 | \n", + "1.690900 | \n", + "
| 72 | \n", + "2.912400 | \n", + "
| 73 | \n", + "1.290300 | \n", + "
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| 90 | \n", + "1.970000 | \n", + "
| 91 | \n", + "2.784900 | \n", + "
| 92 | \n", + "1.193300 | \n", + "
| 93 | \n", + "1.194500 | \n", + "
| 94 | \n", + "1.298700 | \n", + "
| 95 | \n", + "1.497400 | \n", + "
| 96 | \n", + "1.489800 | \n", + "
| 97 | \n", + "1.329000 | \n", + "
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| 125 | \n", + "1.666900 | \n", + "
| 126 | \n", + "1.890500 | \n", + "
| 127 | \n", + "2.156600 | \n", + "
| 128 | \n", + "1.403700 | \n", + "
| 129 | \n", + "1.523600 | \n", + "
| 130 | \n", + "1.526900 | \n", + "
| 131 | \n", + "1.265600 | \n", + "
| 132 | \n", + "1.518900 | \n", + "
| 133 | \n", + "1.244900 | \n", + "
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| 139 | \n", + "1.240400 | \n", + "
| 140 | \n", + "1.249600 | \n", + "
| 141 | \n", + "1.292200 | \n", + "
| 142 | \n", + "2.007600 | \n", + "
| 143 | \n", + "1.521400 | \n", + "
| 144 | \n", + "2.039100 | \n", + "
| 145 | \n", + "1.960900 | \n", + "
| 146 | \n", + "1.939400 | \n", + "
| 147 | \n", + "1.669800 | \n", + "
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| 149 | \n", + "1.829700 | \n", + "
| 150 | \n", + "1.790300 | \n", + "
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| 198 | \n", + "2.002600 | \n", + "
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| 1755 | \n", + "1.188500 | \n", + "
| 1756 | \n", + "1.068700 | \n", + "
| 1757 | \n", + "1.221800 | \n", + "
| 1758 | \n", + "1.329400 | \n", + "
| 1759 | \n", + "1.368200 | \n", + "
| 1760 | \n", + "1.488300 | \n", + "
| 1761 | \n", + "1.155600 | \n", + "
| 1762 | \n", + "1.554500 | \n", + "
| 1763 | \n", + "1.608900 | \n", + "
| 1764 | \n", + "1.308300 | \n", + "
| 1765 | \n", + "1.215500 | \n", + "
| 1766 | \n", + "1.417500 | \n", + "
| 1767 | \n", + "1.134500 | \n", + "
| 1768 | \n", + "1.357100 | \n", + "
| 1769 | \n", + "1.532100 | \n", + "
| 1770 | \n", + "1.204100 | \n", + "
| 1771 | \n", + "1.691600 | \n", + "
| 1772 | \n", + "1.774600 | \n", + "
| 1773 | \n", + "0.943600 | \n", + "
| 1774 | \n", + "1.458000 | \n", + "
| 1775 | \n", + "1.329100 | \n", + "
| 1776 | \n", + "1.531200 | \n", + "
| 1777 | \n", + "1.644400 | \n", + "
| 1778 | \n", + "1.598000 | \n", + "
| 1779 | \n", + "1.380400 | \n", + "
| 1780 | \n", + "1.974700 | \n", + "
| 1781 | \n", + "1.094100 | \n", + "
| 1782 | \n", + "1.476000 | \n", + "
| 1783 | \n", + "1.434500 | \n", + "
| 1784 | \n", + "1.174300 | \n", + "
| 1785 | \n", + "1.293600 | \n", + "
| 1786 | \n", + "1.651100 | \n", + "
| 1787 | \n", + "1.706500 | \n", + "
| 1788 | \n", + "1.309400 | \n", + "
| 1789 | \n", + "1.055200 | \n", + "
| 1790 | \n", + "1.560100 | \n", + "
| 1791 | \n", + "1.621100 | \n", + "
| 1792 | \n", + "1.362200 | \n", + "
| 1793 | \n", + "1.581300 | \n", + "
| 1794 | \n", + "1.439300 | \n", + "
| 1795 | \n", + "1.299800 | \n", + "
| 1796 | \n", + "1.108900 | \n", + "
| 1797 | \n", + "1.234900 | \n", + "
| 1798 | \n", + "1.420900 | \n", + "
| 1799 | \n", + "1.247500 | \n", + "
| 1800 | \n", + "1.209700 | \n", + "
| 1801 | \n", + "1.833500 | \n", + "
| 1802 | \n", + "1.369300 | \n", + "
| 1803 | \n", + "1.236900 | \n", + "
| 1804 | \n", + "1.576300 | \n", + "
| 1805 | \n", + "1.491300 | \n", + "
| 1806 | \n", + "1.096700 | \n", + "
| 1807 | \n", + "1.299100 | \n", + "
| 1808 | \n", + "1.450900 | \n", + "
| 1809 | \n", + "1.293600 | \n", + "
| 1810 | \n", + "1.529600 | \n", + "
| 1811 | \n", + "1.606500 | \n", + "
| 1812 | \n", + "1.229800 | \n", + "
| 1813 | \n", + "1.729600 | \n", + "
| 1814 | \n", + "2.069400 | \n", + "
| 1815 | \n", + "1.329100 | \n", + "
| 1816 | \n", + "1.600400 | \n", + "
| 1817 | \n", + "1.749900 | \n", + "
| 1818 | \n", + "1.199500 | \n", + "
| 1819 | \n", + "1.189900 | \n", + "
| 1820 | \n", + "1.206800 | \n", + "
| 1821 | \n", + "2.264400 | \n", + "
| 1822 | \n", + "1.283800 | \n", + "
| 1823 | \n", + "1.405200 | \n", + "
| 1824 | \n", + "1.227800 | \n", + "
| 1825 | \n", + "1.621800 | \n", + "
| 1826 | \n", + "1.393800 | \n", + "
| 1827 | \n", + "1.234300 | \n", + "
| 1828 | \n", + "1.360500 | \n", + "
| 1829 | \n", + "1.422900 | \n", + "
| 1830 | \n", + "1.388800 | \n", + "
| 1831 | \n", + "1.206300 | \n", + "
| 1832 | \n", + "1.281400 | \n", + "
| 1833 | \n", + "1.219400 | \n", + "
| 1834 | \n", + "1.233900 | \n", + "
| 1835 | \n", + "1.692200 | \n", + "
| 1836 | \n", + "1.649800 | \n", + "
| 1837 | \n", + "1.328300 | \n", + "
| 1838 | \n", + "1.920600 | \n", + "
| 1839 | \n", + "1.649000 | \n", + "
| 1840 | \n", + "1.306800 | \n", + "
| 1841 | \n", + "1.040500 | \n", + "
| 1842 | \n", + "1.506200 | \n", + "
| 1843 | \n", + "1.162700 | \n", + "
| 1844 | \n", + "1.144300 | \n", + "
| 1845 | \n", + "1.752300 | \n", + "
| 1846 | \n", + "1.480600 | \n", + "
| 1847 | \n", + "1.344200 | \n", + "
| 1848 | \n", + "1.239000 | \n", + "
| 1849 | \n", + "1.035800 | \n", + "
| 1850 | \n", + "1.217000 | \n", + "
| 1851 | \n", + "1.141900 | \n", + "
| 1852 | \n", + "1.149500 | \n", + "
| 1853 | \n", + "1.251000 | \n", + "
| 1854 | \n", + "1.430700 | \n", + "
| 1855 | \n", + "1.378100 | \n", + "
| 1856 | \n", + "1.654700 | \n", + "
| 1857 | \n", + "1.147900 | \n", + "
| 1858 | \n", + "1.401800 | \n", + "
| 1859 | \n", + "1.811800 | \n", + "
| 1860 | \n", + "1.690600 | \n", + "
| 1861 | \n", + "1.007700 | \n", + "
| 1862 | \n", + "1.311000 | \n", + "
| 1863 | \n", + "1.186500 | \n", + "
| 1864 | \n", + "1.114800 | \n", + "
| 1865 | \n", + "1.577400 | \n", + "
| 1866 | \n", + "1.390000 | \n", + "
| 1867 | \n", + "1.382800 | \n", + "
| 1868 | \n", + "1.575000 | \n", + "
| 1869 | \n", + "1.406900 | \n", + "
| 1870 | \n", + "1.411900 | \n", + "
| 1871 | \n", + "1.071300 | \n", + "
| 1872 | \n", + "1.575200 | \n", + "
| 1873 | \n", + "1.449300 | \n", + "
| 1874 | \n", + "1.752000 | \n", + "
| 1875 | \n", + "1.119500 | \n", + "
| 1876 | \n", + "1.629200 | \n", + "
| 1877 | \n", + "1.250900 | \n", + "
| 1878 | \n", + "1.278500 | \n", + "
| 1879 | \n", + "1.146100 | \n", + "
| 1880 | \n", + "1.473300 | \n", + "
| 1881 | \n", + "1.767300 | \n", + "
| 1882 | \n", + "2.117000 | \n", + "
| 1883 | \n", + "1.203400 | \n", + "
| 1884 | \n", + "1.110900 | \n", + "
| 1885 | \n", + "1.209700 | \n", + "
| 1886 | \n", + "1.846700 | \n", + "
| 1887 | \n", + "1.157100 | \n", + "
| 1888 | \n", + "1.283200 | \n", + "
| 1889 | \n", + "1.315900 | \n", + "
| 1890 | \n", + "1.324700 | \n", + "
| 1891 | \n", + "1.127500 | \n", + "
| 1892 | \n", + "1.395200 | \n", + "
| 1893 | \n", + "1.597100 | \n", + "
| 1894 | \n", + "1.311900 | \n", + "
| 1895 | \n", + "1.535100 | \n", + "
| 1896 | \n", + "1.238000 | \n", + "
| 1897 | \n", + "1.085500 | \n", + "
| 1898 | \n", + "2.029100 | \n", + "
| 1899 | \n", + "1.333500 | \n", + "
| 1900 | \n", + "2.012700 | \n", + "
| 1901 | \n", + "1.641400 | \n", + "
| 1902 | \n", + "1.488000 | \n", + "
| 1903 | \n", + "1.340500 | \n", + "
| 1904 | \n", + "1.455900 | \n", + "
| 1905 | \n", + "1.677300 | \n", + "
| 1906 | \n", + "1.308700 | \n", + "
| 1907 | \n", + "1.223900 | \n", + "
| 1908 | \n", + "1.346900 | \n", + "
| 1909 | \n", + "1.164800 | \n", + "
| 1910 | \n", + "1.174300 | \n", + "
| 1911 | \n", + "1.026200 | \n", + "
| 1912 | \n", + "1.380600 | \n", + "
| 1913 | \n", + "1.522100 | \n", + "
| 1914 | \n", + "1.313400 | \n", + "
| 1915 | \n", + "1.511100 | \n", + "
| 1916 | \n", + "1.089300 | \n", + "
| 1917 | \n", + "1.535000 | \n", + "
| 1918 | \n", + "1.491000 | \n", + "
| 1919 | \n", + "2.140200 | \n", + "
| 1920 | \n", + "1.641000 | \n", + "
| 1921 | \n", + "1.373200 | \n", + "
| 1922 | \n", + "1.744200 | \n", + "
| 1923 | \n", + "1.527400 | \n", + "
| 1924 | \n", + "1.944600 | \n", + "
| 1925 | \n", + "1.717700 | \n", + "
| 1926 | \n", + "1.371700 | \n", + "
| 1927 | \n", + "1.276700 | \n", + "
| 1928 | \n", + "1.350800 | \n", + "
| 1929 | \n", + "1.415100 | \n", + "
| 1930 | \n", + "1.429200 | \n", + "
| 1931 | \n", + "1.726000 | \n", + "
| 1932 | \n", + "1.432200 | \n", + "
| 1933 | \n", + "1.130500 | \n", + "
| 1934 | \n", + "1.152500 | \n", + "
| 1935 | \n", + "1.406900 | \n", + "
| 1936 | \n", + "0.945800 | \n", + "
| 1937 | \n", + "2.123700 | \n", + "
| 1938 | \n", + "1.462600 | \n", + "
| 1939 | \n", + "1.302800 | \n", + "
| 1940 | \n", + "1.542700 | \n", + "
| 1941 | \n", + "1.646700 | \n", + "
| 1942 | \n", + "1.091100 | \n", + "
| 1943 | \n", + "1.525800 | \n", + "
| 1944 | \n", + "1.805100 | \n", + "
| 1945 | \n", + "1.385600 | \n", + "
| 1946 | \n", + "1.384300 | \n", + "
| 1947 | \n", + "1.424400 | \n", + "
| 1948 | \n", + "1.356500 | \n", + "
| 1949 | \n", + "1.430500 | \n", + "
| 1950 | \n", + "1.129100 | \n", + "
| 1951 | \n", + "1.396000 | \n", + "
| 1952 | \n", + "1.267200 | \n", + "
| 1953 | \n", + "1.109400 | \n", + "
| 1954 | \n", + "1.476600 | \n", + "
| 1955 | \n", + "1.661100 | \n", + "
| 1956 | \n", + "1.362800 | \n", + "
| 1957 | \n", + "1.185100 | \n", + "
| 1958 | \n", + "1.316000 | \n", + "
| 1959 | \n", + "1.235400 | \n", + "
| 1960 | \n", + "1.674900 | \n", + "
| 1961 | \n", + "1.447400 | \n", + "
| 1962 | \n", + "1.646300 | \n", + "
| 1963 | \n", + "1.040400 | \n", + "
| 1964 | \n", + "1.741700 | \n", + "
| 1965 | \n", + "1.412700 | \n", + "
| 1966 | \n", + "1.575200 | \n", + "
| 1967 | \n", + "1.043200 | \n", + "
| 1968 | \n", + "1.716600 | \n", + "
| 1969 | \n", + "1.285700 | \n", + "
| 1970 | \n", + "1.453900 | \n", + "
| 1971 | \n", + "1.383000 | \n", + "
| 1972 | \n", + "1.758500 | \n", + "
| 1973 | \n", + "1.173800 | \n", + "
| 1974 | \n", + "1.188800 | \n", + "
| 1975 | \n", + "1.487500 | \n", + "
| 1976 | \n", + "1.367200 | \n", + "
| 1977 | \n", + "1.105000 | \n", + "
| 1978 | \n", + "1.591300 | \n", + "
| 1979 | \n", + "1.161100 | \n", + "
| 1980 | \n", + "1.501300 | \n", + "
| 1981 | \n", + "1.301500 | \n", + "
| 1982 | \n", + "1.481200 | \n", + "
| 1983 | \n", + "1.153500 | \n", + "
| 1984 | \n", + "1.289400 | \n", + "
| 1985 | \n", + "1.539300 | \n", + "
| 1986 | \n", + "1.703700 | \n", + "
| 1987 | \n", + "1.267300 | \n", + "
| 1988 | \n", + "1.294200 | \n", + "
| 1989 | \n", + "1.357100 | \n", + "
| 1990 | \n", + "1.253700 | \n", + "
| 1991 | \n", + "1.334600 | \n", + "
| 1992 | \n", + "1.718800 | \n", + "
| 1993 | \n", + "1.563400 | \n", + "
| 1994 | \n", + "1.647900 | \n", + "
| 1995 | \n", + "1.547600 | \n", + "
| 1996 | \n", + "1.389200 | \n", + "
| 1997 | \n", + "1.322900 | \n", + "
| 1998 | \n", + "1.340500 | \n", + "
| 1999 | \n", + "1.504700 | \n", + "
| 2000 | \n", + "1.334000 | \n", + "
| 2001 | \n", + "1.203100 | \n", + "
| 2002 | \n", + "1.322800 | \n", + "
| 2003 | \n", + "1.123500 | \n", + "
| 2004 | \n", + "1.375200 | \n", + "
| 2005 | \n", + "1.306000 | \n", + "
| 2006 | \n", + "1.186800 | \n", + "
| 2007 | \n", + "1.512000 | \n", + "
| 2008 | \n", + "1.284300 | \n", + "
| 2009 | \n", + "1.442800 | \n", + "
| 2010 | \n", + "1.155800 | \n", + "
| 2011 | \n", + "1.905600 | \n", + "
| 2012 | \n", + "1.182600 | \n", + "
| 2013 | \n", + "1.731600 | \n", + "
| 2014 | \n", + "1.117500 | \n", + "
| 2015 | \n", + "1.741300 | \n", + "
| 2016 | \n", + "1.252900 | \n", + "
| 2017 | \n", + "1.029700 | \n", + "
| 2018 | \n", + "1.505600 | \n", + "
| 2019 | \n", + "1.401000 | \n", + "
| 2020 | \n", + "1.187700 | \n", + "
| 2021 | \n", + "1.833800 | \n", + "
| 2022 | \n", + "1.286800 | \n", + "
| 2023 | \n", + "1.372400 | \n", + "
| 2024 | \n", + "1.391300 | \n", + "
| 2025 | \n", + "1.304800 | \n", + "
| 2026 | \n", + "1.163900 | \n", + "
| 2027 | \n", + "1.471400 | \n", + "
| 2028 | \n", + "1.281000 | \n", + "
| 2029 | \n", + "1.183200 | \n", + "
| 2030 | \n", + "1.678900 | \n", + "
| 2031 | \n", + "1.595700 | \n", + "
| 2032 | \n", + "1.195000 | \n", + "
| 2033 | \n", + "1.263200 | \n", + "
| 2034 | \n", + "1.158200 | \n", + "
| 2035 | \n", + "1.103000 | \n", + "
| 2036 | \n", + "1.349300 | \n", + "
| 2037 | \n", + "1.183100 | \n", + "
| 2038 | \n", + "1.350600 | \n", + "
| 2039 | \n", + "1.523100 | \n", + "
| 2040 | \n", + "1.237700 | \n", + "
| 2041 | \n", + "1.607700 | \n", + "
| 2042 | \n", + "1.245600 | \n", + "
| 2043 | \n", + "1.104900 | \n", + "
| 2044 | \n", + "1.557800 | \n", + "
| 2045 | \n", + "1.367800 | \n", + "
| 2046 | \n", + "1.236800 | \n", + "
| 2047 | \n", + "1.188600 | \n", + "
| 2048 | \n", + "1.180500 | \n", + "
| 2049 | \n", + "1.279400 | \n", + "
| 2050 | \n", + "1.853500 | \n", + "
| 2051 | \n", + "1.236400 | \n", + "
| 2052 | \n", + "1.266600 | \n", + "
| 2053 | \n", + "1.298100 | \n", + "
| 2054 | \n", + "1.339700 | \n", + "
| 2055 | \n", + "1.247300 | \n", + "
| 2056 | \n", + "1.892200 | \n", + "
| 2057 | \n", + "1.289800 | \n", + "
| 2058 | \n", + "1.443800 | \n", + "
| 2059 | \n", + "1.269000 | \n", + "
| 2060 | \n", + "1.321000 | \n", + "
| 2061 | \n", + "1.594500 | \n", + "
| 2062 | \n", + "1.992100 | \n", + "
| 2063 | \n", + "1.409600 | \n", + "
| 2064 | \n", + "1.185900 | \n", + "
| 2065 | \n", + "1.257600 | \n", + "
| 2066 | \n", + "1.630700 | \n", + "
| 2067 | \n", + "1.443100 | \n", + "
| 2068 | \n", + "1.848100 | \n", + "
| 2069 | \n", + "1.965000 | \n", + "
| 2070 | \n", + "1.972600 | \n", + "
| 2071 | \n", + "1.723600 | \n", + "
| 2072 | \n", + "1.100800 | \n", + "
| 2073 | \n", + "1.829900 | \n", + "
| 2074 | \n", + "1.374600 | \n", + "
| 2075 | \n", + "1.558600 | \n", + "
| 2076 | \n", + "1.320900 | \n", + "
| 2077 | \n", + "1.538300 | \n", + "
| 2078 | \n", + "1.125100 | \n", + "
| 2079 | \n", + "1.539000 | \n", + "
| 2080 | \n", + "1.351400 | \n", + "
| 2081 | \n", + "1.666900 | \n", + "
| 2082 | \n", + "1.358900 | \n", + "
| 2083 | \n", + "1.170800 | \n", + "
| 2084 | \n", + "1.263400 | \n", + "
| 2085 | \n", + "1.038400 | \n", + "
| 2086 | \n", + "1.350100 | \n", + "
| 2087 | \n", + "1.527600 | \n", + "
| 2088 | \n", + "1.416600 | \n", + "
| 2089 | \n", + "1.632500 | \n", + "
| 2090 | \n", + "1.022900 | \n", + "
| 2091 | \n", + "1.270300 | \n", + "
| 2092 | \n", + "1.265800 | \n", + "
| 2093 | \n", + "1.895400 | \n", + "
| 2094 | \n", + "1.294000 | \n", + "
| 2095 | \n", + "1.276000 | \n", + "
| 2096 | \n", + "1.436200 | \n", + "
| 2097 | \n", + "1.248000 | \n", + "
| 2098 | \n", + "1.505700 | \n", + "
| 2099 | \n", + "1.201300 | \n", + "
| 2100 | \n", + "1.612800 | \n", + "
| 2101 | \n", + "1.577500 | \n", + "
| 2102 | \n", + "2.045800 | \n", + "
| 2103 | \n", + "1.448800 | \n", + "
| 2104 | \n", + "1.463300 | \n", + "
| 2105 | \n", + "1.385300 | \n", + "
| 2106 | \n", + "1.318200 | \n", + "
| 2107 | \n", + "1.241900 | \n", + "
| 2108 | \n", + "2.427100 | \n", + "
| 2109 | \n", + "1.897000 | \n", + "
| 2110 | \n", + "2.441200 | \n", + "
| 2111 | \n", + "1.286000 | \n", + "
| 2112 | \n", + "1.421300 | \n", + "
| 2113 | \n", + "1.428900 | \n", + "
| 2114 | \n", + "1.471300 | \n", + "
| 2115 | \n", + "1.356700 | \n", + "
| 2116 | \n", + "1.223000 | \n", + "
| 2117 | \n", + "1.253100 | \n", + "
| 2118 | \n", + "1.542300 | \n", + "
| 2119 | \n", + "1.530200 | \n", + "
| 2120 | \n", + "1.381900 | \n", + "
| 2121 | \n", + "1.474300 | \n", + "
| 2122 | \n", + "1.542500 | \n", + "
| 2123 | \n", + "1.249200 | \n", + "
| 2124 | \n", + "1.272600 | \n", + "
| 2125 | \n", + "1.536700 | \n", + "
| 2126 | \n", + "1.666900 | \n", + "
| 2127 | \n", + "1.646300 | \n", + "
| 2128 | \n", + "1.243100 | \n", + "
| 2129 | \n", + "1.347400 | \n", + "
| 2130 | \n", + "1.240400 | \n", + "
| 2131 | \n", + "1.707300 | \n", + "
| 2132 | \n", + "1.480700 | \n", + "
| 2133 | \n", + "1.199700 | \n", + "
| 2134 | \n", + "1.202100 | \n", + "
| 2135 | \n", + "1.802800 | \n", + "
| 2136 | \n", + "1.467500 | \n", + "
| 2137 | \n", + "1.199000 | \n", + "
| 2138 | \n", + "1.374700 | \n", + "
| 2139 | \n", + "1.688600 | \n", + "
| 2140 | \n", + "1.698300 | \n", + "
| 2141 | \n", + "1.324000 | \n", + "
| 2142 | \n", + "1.414500 | \n", + "
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"
+ ],
+ "text/plain": [
+ "