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116 KiB
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116 KiB
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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"}}]} |