{ "cells": [ { "cell_type": "markdown", "source": [ "To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n", "
\n", " \n", " \n", " Join Discord if you need help + ⭐ Star us on Github ⭐\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).\n", "\n", "**[NEW] Llama-3 8b is trained on a crazy 15 trillion tokens! Llama-2 was 2 trillion.**\n", "\n", "Use our [Llama-3 8b Instruct](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing) notebook for conversational style finetunes.\n", "\n", "https://github.com/unslothai/unsloth?tab=readme-ov-file" ], "metadata": { "id": "IqM-T1RTzY6C" } }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes" ] }, { "cell_type": "markdown", "source": [ "* We support Llama, Mistral, Phi-3, Gemma, Yi, DeepSeek, Qwen, TinyLlama, Vicuna, Open Hermes etc\n", "* We support 16bit LoRA or 4bit QLoRA. Both 2x faster.\n", "* `max_seq_length` can be set to anything, since we do automatic RoPE Scaling via [kaiokendev's](https://kaiokendev.github.io/til) method.\n", "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)" ], "metadata": { "id": "r2v_X2fA0Df5" } }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 313, "referenced_widgets": [ "ff883cd3c84046679615688aeecbf1af", "9beffd1bd6b843b2b5078ecbbe371a90", "c06c79c6887140d7a85e850876c3a1c6", "c122b87ecfbb443d95bc962fe08597dc", "cf0c3d6afb14474ebf1f1cee9d5e2b64", "07675e21e9ab46dbb3e98937c9293b38", "fa6d98ccc92d4a839d632a93eb9ec25b", "1b21db3e0e424d38801d184bf8cc7212", "0b4712f7759749e7aeb81f7189847f7f", "a22165f04f8c438a8b43671fbb18103a", "06c029cb451a4b1087d666e0f7abd7fd", "0a42fbbda6144b4ba56b3d978bbcaf90", "ba6de510384148fda85371e344f02457", "a333fd5483ee46dab7a38f03ae8ed03c", "3d5206097a0d44f9b12070feb72b02ad", "799c5debe8474454a9777b72da8ab99e", "0aca903bbfa645c3a982c75d998b8ccd", "b6d0e78e937f4460a727ddded40e964f", "9c8a2d69b31040d3a4a96b7331a2867a", "d402bdc8838a47ad9a22c9bc4198b3c1", "dfb95a6a41fa437ea811f8d599eec60e", "26a73d7fef4d4a7a9f074785018f7305", "8345314cadd0478d9a08bc3a76ebdebc", "651aa2ecfb2646b7ad2aece839d6630e", "41522234a7ff4c409fa7bce7a6dbc968", "5b186f9cd84248ac82370ea9ca8e28cf", "74385c0212b545a597975476a3ff1260", "9ed04fb590774b60bd812415e140bc02", "9bfe49502a924c66bdcd3e8adc474bcf", "b443999e90d24197a7077bd61d512c22", "5b0749f217e94dc48f5be6c63dadb1c9", "b215aeddaf454d38abc9d44ef1bbd1d2", "ad5681f162de465d93e49aced5a1960c", "7537da4468994b9ab3a3ea9a438381db", "38e0c6c86f0e4a71890d5fd6e8acc12e", "422784e38cb4431f92fa6a18748da194", "c2dbff9c9c45442597b64375de7a2f2b", "19eeb5ea80b14ce6b064613c74a9cd48", "8f61bb0b62ba449e8825acbc46a4f8d4", "9222884c8fe64d358696f87d665fab33", "d23b84de3c7e4a02b2bc24a96ff15cc1", "69280420f5a04d5f9bf95d59c0bc5a43", "2033daae1224463ba90fa840bc30570e", "a95ba7b97e734d50ae1cf4c3a0ca2552", "a3c0223a4470466cada8071c493bb344", "55710ad5c34140cf9e1e061ee1ee150d", "4d084e9e729c405e9dc9aa894c0533b3", "3ccdf40f40b14c9389a684380515d107", "e31f02e521404075991f8d646daa8009", "5f9b0cb06bdb45c68b122c2a82d3a468", "59d12b0032e948238a2a28ca49030934", "8f5fc1a008dd4ecdaef7e075254d1c49", "b77efd2c9c1d4eddb345117972fc0ce8", "af058e735f0f45689ffc82adfb22e8bd", "77397bfe7b734a3a8e0c21e7a147cda5", "7daf5e72a19c49d7a1ff20021c967a60", "8ab7aacf41774f2ca2d2cd5d795f2de2", "8a954d84b64c4b6baf23de373424e879", "39de318dcd024702bb66d978afc48dea", "293376904a5141dc91c4c35a0960b2c6", "f071bf96a9a540eabbc1680603e15b63", "5fac3de7b57f48a7aa54edbc1597f26f", "76c89b06905f4c4ca33ed41b3c93b672", "bee1ecc5652043a5abaa4077f5e63299", "cc7c081019514b999ec3020e358870ee", "e07b0e507c89476bbc397d3f11ecda66" ] }, "id": "QmUBVEnvCDJv", "outputId": "c2e986fe-0735-402a-f9ce-eff5b86496b2" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/1.20k [00:00 0 ! Suggested 8, 16, 32, 64, 128\n", " target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n", " \"gate_proj\", \"up_proj\", \"down_proj\",],\n", " lora_alpha = 16,\n", " lora_dropout = 0, # Supports any, but = 0 is optimized\n", " bias = \"none\", # Supports any, but = \"none\" is optimized\n", " # [NEW] \"unsloth\" uses 30% less VRAM, fits 2x larger batch sizes!\n", " use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for very long context\n", " random_state = 3407,\n", " use_rslora = False, # We support rank stabilized LoRA\n", " loftq_config = None, # And LoftQ\n", ")" ] }, { "cell_type": "markdown", "source": [ "\n", "### Data Prep\n", "We now use the Alpaca dataset from [yahma](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is a filtered version of 52K of the original [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html). You can replace this code section with your own data prep.\n", "\n", "**[NOTE]** To train only on completions (ignoring the user's input) read TRL's docs [here](https://huggingface.co/docs/trl/sft_trainer#train-on-completions-only).\n", "\n", "**[NOTE]** Remember to add the **EOS_TOKEN** to the tokenized output!! Otherwise you'll get infinite generations!\n", "\n", "If you want to use the `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ], "metadata": { "id": "vITh0KVJ10qX" } }, { "cell_type": "code", "execution_count": 37, "metadata": { "id": "LjY75GoYUCB8" }, "outputs": [], "source": [ "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "{}\n", "\n", "### Input:\n", "{}\n", "\n", "### Response:\n", "{}\"\"\"\n", "\n", "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", "def formatting_prompts_func(examples):\n", " instructions = examples[\"instruction\"]\n", " inputs = examples[\"input\"]\n", " outputs = examples[\"output\"]\n", " texts = []\n", " for instruction, input, output in zip(instructions, inputs, outputs):\n", " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", " texts.append(text)\n", " return { \"text\" : texts, }\n", "pass\n", "\n", "from datasets import load_dataset\n", "dataset = load_dataset(\"harry85/alpaca-cleaned-harry\", split = \"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True,)\n" ] }, { "cell_type": "code", "source": [ "#dataset.head()" ], "metadata": { "id": "GJJqORnVbJ_4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "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`!" ], "metadata": { "id": "idAEIeSQ3xdS" } }, { "cell_type": "code", "execution_count": 38, "metadata": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 156, "referenced_widgets": [ "994d720814ee4c2092bbbf826624232d", "486dcf59946c4e0ba8d81454705c4622", "0fee6dfe3a234304a4f7f762e2322015", "40234b68c85e436a81d11386c130d5ca", "92c9d5e53215423786a2417564b0a223", "32a580941e0c4b40aa150dc983f5792f", "7211447d333242daa2e4b45ceca5bf80", "751de11ef70f405bb82c7bea03a02c0f", "724ffdc42eaa4a0895cbc7fce511bb4f", "45c1a6f5442d412db02dd7ba2690ad90", "f219a81178404a0c9e4b3e248d73d589" ] }, "outputId": "155e75d8-c203-49e4-91d4-e9a7a0262c40" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", " self.pid = os.fork()\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Map (num_proc=2): 0%| | 0/4 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
11.970400
21.970400
31.971100
41.970400
51.970400
61.970400
71.971100
81.970400
91.955600
101.970400
111.970400
121.955600
131.955600
141.971100
151.971100
161.971100
171.970400
181.955600
191.971100
201.955600
211.970400
221.970400
231.971100
241.970400
251.971100
261.970400
271.970400
281.971100
291.955600
301.971100
311.970400
321.970400
331.971000
341.970300
351.970300
361.955300
371.970900
381.970200
391.955400
401.955500
411.970300
421.955500
431.970900
441.970800
451.955400
461.970800
471.970900
481.970200
491.955200
501.970100
511.970200
521.955300
531.955500
541.970900
551.970900
561.970900
571.955400
581.970400
591.970200
601.970900
611.970400
621.955400
631.955500
641.955300
651.955400
661.970900
671.955400
681.955400
691.970800
701.970900
711.970300
721.970900
731.955600
741.955500
751.970900
761.970300
771.970300
781.971000
791.970900
801.955400
811.955300
821.970900
831.970300
841.970900
851.955300
861.970200
871.970800
881.955300
891.955300
901.955200
911.970200
921.970400
931.955200
941.970100
951.955300
961.970300
971.970400
981.970700
991.955400
1001.955400

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "pCqnaKmlO1U9" }, "outputs": [], "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", "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!" ], "metadata": { "id": "ekOmTR1hSNcr" } }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"Given a positive integer, generate a sequence of numbers leading to 1.\", # instruction\n", " \"Number: 6\", # 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", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "4649ab70-bc33-4195-e772-7d0e884b230a" }, "execution_count": 42, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nGiven a positive integer, generate a sequence of numbers leading to 1.\\n\\n### Input:\\nNumber: 6\\n\\n### Response:\\n1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1\\n<|end_of_text|>']" ] }, "metadata": {}, "execution_count": 42 } ] }, { "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": [ "# 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", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "39a6fc62-da67-443d-8073-7b34578ad918" }, "execution_count": 43, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "Continue the fibonnaci sequence.\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "13\n", "<|end_of_text|>\n" ] } ] }, { "cell_type": "code", "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"who is haris hota\", # instruction\n", " \"\", # 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": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Xx5CdpGTwcU7", "outputId": "65f2133b-5309-4236-9255-e542c488394d" }, "execution_count": 48, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "\n", "### Instruction:\n", "who is haris hota\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "<|end_of_text|>\n" ] } ] }, { "cell_type": "markdown", "source": [ "\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!" ], "metadata": { "id": "uMuVrWbjAzhc" } }, { "cell_type": "code", "source": [ "model.save_pretrained(\"model_LLAMA3_Finetuned_HH\") # Local saving\n", "tokenizer.save_pretrained(\"model_LLAMA3_Finetuned_Tokenizer_HH\")\n", "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" ], "metadata": { "id": "upcOlWe7A1vc", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "306b672f-73a9-4719-c656-0066baf35856" }, "execution_count": 32, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "('model_LLAMA3_Finetuned_Tokenizer_HH/tokenizer_config.json',\n", " 'model_LLAMA3_Finetuned_Tokenizer_HH/special_tokens_map.json',\n", " 'model_LLAMA3_Finetuned_Tokenizer_HH/tokenizer.json')" ] }, "metadata": {}, "execution_count": 32 } ] }, { "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 = \"lora_model\", # 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", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "be14674f-5865-40b8-8d9d-b93327464f13" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nWhat is a famous tall tower in Paris?\\n\\n### Input:\\n\\n\\n### Response:\\nOne of the most famous tall towers in Paris is the Eiffel Tower. Built in 1889, it stands at 324 meters tall and is located on the Champ de Mars in the 7th arrondissement of Paris. The Eiffel Tower is a wrought-iron lattice tower designed by the French']" ] }, "metadata": {}, "execution_count": 12 } ] }, { "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_count": null, "outputs": [] }, { "cell_type": "markdown", "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." ], "metadata": { "id": "f422JgM9sdVT" } }, { "cell_type": "code", "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 = \"\")" ], "metadata": { "id": "iHjt_SMYsd3P" }, "execution_count": 33, "outputs": [] }, { "cell_type": "markdown", "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." ], "metadata": { "id": "TCv4vXHd61i7" } }, { "cell_type": "code", "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 = \"\")" ], "metadata": { "id": "FqfebeAdT073" }, "execution_count": 34, "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", "\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 πŸ€— 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", "9. [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)\n", "\n", "

\n", " \n", " \n", " Support our work if you can! Thanks!\n", "
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