{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "IqM-T1RTzY6C" }, "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?tab=readme-ov-file#-installation-instructions).\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] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**\n", "\n", "**[NEW] Finetuning Mistral Small 22b fits in a 16GB GPU!**" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "!pip install unsloth\n", "# Also get the latest nightly Unsloth!\n", "!pip uninstall unsloth -y && pip install --upgrade --no-cache-dir \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"" ] }, { "cell_type": "markdown", "metadata": { "id": "r2v_X2fA0Df5" }, "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", "* [**NEW**] We make Gemma-2 9b / 27b **2x faster**! See our [Gemma-2 9b notebook](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing)\n", "* [**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)\n", "* [**NEW**] We make Mistral NeMo 12B 2x faster and fit in under 12GB of VRAM! [Mistral NeMo notebook](https://colab.research.google.com/drive/17d3U-CAIwzmbDRqbZ9NnpHxCkmXB6LZ0?usp=sharing)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 403, "referenced_widgets": [ "6a0264ad9e1045e9b276da5afea538e4", "6889ef6686684e8faee29a64986488ad", "28575a44bd4a4ff4995bef61a1ff923f", "ac2708c092394bdfbad40e65c5fcf5bd", "362a469d210a40c4beb44614d0dff94d", "3b208739190647aba89d38664f3c04ff", "13858c39f00043eab784b43e78f75e96", "be112d605e874960b57620ab725742c1", "71d2164bd80c4c7ebdba192b545307e0", "cfc8db4601c241f5aa1664a2df9ad0a2", "025890c6531d450b9ad67861cff5eb66", "974e02c218174ee3ac8f7097205dbea3", "b4c1a2579601463eae261e9d8d519658", "168b4bc0ddd548cc8e3632eee17f6333", "b79b90dfab6340b19cd27f40c497420d", "429d057b1f4b46b1a59deff19df8c51f", "ce48b32b9ad24145814ae20bfd91b56d", "9c4f0814b946400c8d35d973a8907f8a", "8657ee65ecd84d5a9060532e3120ebc2", "27c682c7ccd54f4e90597cbeff916f5b", "c5f5ed3035a54779ac1d2c5c7743ab6d", "730d88a3a02e4b528efd0b7a5b4bbe51", "c3f26313e34440e0918dc5e6c53fe256", "517365223084483a8d8b59410c6fe95a", "da6c2c2731cb4709a271bca5478a245d", "6dba4a23cf4547adb17eaf446eef7297", "67793453c68943329ccd77c0b5200f2a", "233836d2c7b048009a8d5f2039ff0483", "69fa12ed214d49df911ad7f7e4cf215b", "ef2bd43356e547c8b6813308b61eab04", "3c9e9bb23c054fc8b9f3a6658e8d5a40", "9c6d70b74f3d4c0c9fce7e8c379a4d2f", "538539b1e9134709b26ac83664b5b114", "f4334c21a9d24e48b43232cce101e395", "38b7d7231e3a4b078ff15c0af7d4bab6", "3f3b84e262d744558b132ac31da96081", "afb067fcfc1a4bc8aee1206e0e061100", "fad80247656042788748ea5c5c9f64df", "82c8aca0f1a24c38a2c111dfa85df233", "9dc305d92ded49e58e76469d67d9a2a7", "749165aa361b47eda47e778a37176012", "a4ff849ee64f4acd8296a24d2370a25b", "b064befe0f57430ab07d62c18ea1b8e3", "c5d1c7b31fe044f5b4882b29c0c6948b", "d02ed9830c494a59a67c1e95c2b684bd", "7e0bca45410b467c92f97deaaf153ce6", "16f2c1312876443784df678b0fe424b1", "1480812645954012a9dc22049f1295a5", "856c8aab5f8e451ebc7e19e79105ed0b", "4664916ffbe54f33adbf1b416fd5f384", "6f3bafec64374b68af5d6bc79774951d", "d75e04ea7c4644a3a3390b98284eb71e", "9f7cf13438a84bb29002d4dcf9583fd4", "7943696966dd4434900e30aed33fafda", "542a9b28ae904946a6fbbb57d1815e07", "d38ace4d6758421f8ac21b5317a90cda", "ab25c4681afc4516acd2daf62c2e526b", "fd706226b91847a39fc3dde16df4ce9d", "3f47030858274683b8b14ad06cc8c0d5", "252c63f990844e079f30f825d2c07184", "722ae7185340428ea42f1f0322d11811", "6e394dc9bb584d7b84885265267f2174", "677de59ed6a941eb980abac52eae5a69", "cec0767678744a81a457f9bfa2287e92", "c5e622547fb048128d67240d16152f79", "251591a1f67849b59664988d900c9320" ] }, "id": "QmUBVEnvCDJv", "outputId": "5253330d-8a3d-4826-ab64-87f2ef9f1aaa" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.10.7: Fast Mistral patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.5.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.post2. 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": "6a0264ad9e1045e9b276da5afea538e4", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/4.14G [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", "metadata": { "id": "vITh0KVJ10qX" }, "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 `mistral3` 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)." ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "cef6b12b6552416ba87bdecf578c190f", "02426c5157ef4c92b1f912a2078032d6", "86dc1f03a7384d7b926c83ba9ad67625", "cd7d8e06b17c458593aba91e9981acd5", "e4f0ba3f0d6a487db87859777110804f", "13ee10ac59fb4401adb269038138e1d6", "bff66c38b69e4641865a4cbb532f47c1", "25fc5fc784a44aa491b841f185ef374d", "b882d5d3ca8442aaa2ddef15e47e5b45", "7d7f3cdde6e7482580883140dd1ba3ee", "467cef144dbd4d99bb1f5111c8ca25fa", "f9e1cac85c4e43bfb5e87c2b07d2d082", "26cb5c9cb78349b2b4e3243752093cc4", "9ad7254e49c84949a63df3569a9f6a34", "e9461ce46eb3437e8181e7d87031e048", "f4cde1cee7034425a60ecad0b5c07dda", "f61217d62cf94257b6cc9673d8e6cab1", "e26f854045484cd7a78f4df5b2e2bdc5", "9fe9c71e86824b8ba7f6757fb961f769", "37aeacc4ed6349b096efc3513d027c53", "62e5a5dea5fe404fb6b54561b17dfed2", "eaca2be4971344d6b56dcf64c3a69a8e", "fde39522ee7e4cd8bf6c5c8e6d07d1c3", "e82aa85c090e40b199df04e9761c0867", "6869bd240f6a4c27b19fa101cc665001", "3cc5e854f9a2462fb4eac1b7bbc26ebb", "c45929291edb45db829d6bf4caeb3cc7", "0d91e0c88db442e9a5f74d66c75fe33a", "8e2ad3625e624f7ba3ade89be147652d", "58c7dee9928346aba7a1986e32a7a776", "2595d0c6016849d999725fe309b38414", "4a38b8f3bcd74aa6af1d2d143fc5c1d2", "5d69c9c0eb454c998628d042720175c2", "ee1700276c2c4f36a391c9c64606d5a6", "ac0d0682ad7b48c0859b1fd3fe38f9a3", "dcbe29b4ad914c36be45549a87712136", "4c00184bff2b4f61a8b2b0866948bab0", "31b710c446a04859bb1bc068b9c42bcc", "9ce97683d0d14b8682b88d297b05e1e8", "b2b5c455e3874760b0153297d85412fb", "406e55d150d54f9db3f324e487e3aae9", "d2176f04c91a4bc5ba031980c22e5b99", "e9755d20f6cb4674b0bc6adb6da5015d", "a1f1649939a64ae6980283dece79934b", "c70ca67cf8074d389200b28c96c41c86", "b37374a767fc41c2b9e354f7cb11a802", "b36d3d2d9604420f8798330b296b61ee", "7040bb77fe904a378382fb8f0bb3ac3f", "232edccc247f40afb3aaf4458a45c084", "04405d46d02d4d9d9cafcd1a47f18b19", "0301b3c2076c4e158e8f5af6ce74df77", "ad4fc982db814395b18baa756ea16bc7", "d2525b5577a6425f8c77d66c12c76cc1", "aba4df1d6c5b4fde90452a6d6241cb9f", "59cff3508e8b41a7a2923c19e0016ff4" ] }, "id": "LjY75GoYUCB8", "outputId": "55a6f704-a9f8-4fb9-a3b4-b54f9f33ec0c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "cef6b12b6552416ba87bdecf578c190f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/450 [00:00\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": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "5c15f734bd2c4caeb1d0682cdfb6053a", "e202f31c8f854fa19844b176bc2917c2", "17e3bbf2df974aa1b840e4511ed76afd", "758310411c2c4c7bb318d082ed1cecf1", "f73f729c27414608b106a61357ef1f9b", "f7c0f25f2e3b4b81bc8c7c65fa65ba65", "7bb43fd56e754302bbe77c083996fa97", "4562d6548a394508b6a86dd9fd2cb7ca", "8057d60eab0843d1bcb06ff794b911aa", "abf15b1dd4f548229fdec015657aed73", "1a4e9b55836f42cfbb36fc4f0a01252c" ] }, "id": "95_Nn-89DhsL", "outputId": "6b426a8c-dd7a-42b2-cc4b-df8b073d4117" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "5c15f734bd2c4caeb1d0682cdfb6053a", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/172026 [00:00\n", " \n", " \n", " [60/60 12:50, Epoch 0/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
StepTraining Loss
11.254200
21.113400
31.247700
40.995000
51.063900
61.108900
71.127300
81.058100
90.949900
100.822700
110.750700
120.564500
130.995400
140.566200
150.878100
160.841100
170.926300
180.582900
190.830300
200.451800
211.021900
220.686900
230.516700
240.861300
250.764900
260.914000
270.852000
280.910100
290.798400
300.697900
310.631000
320.627200
330.635200
340.933800
350.969300
360.789100
370.644300
380.782700
390.639500
400.746300
410.684400
420.687900
430.653900
440.881100
450.877500
460.951100
470.539300
480.860900
490.706300
500.602400
510.819100
520.816000
530.401500
540.552000
550.665500
560.772100
570.542500
580.864100
590.588400
600.682700

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "3fb07f2c-0a4b-4fef-fef0-cd47feb3bd41" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "795.0724 seconds used for training.\n", "13.25 minutes used for training.\n", "Peak reserved memory = 5.979 GB.\n", "Peak reserved memory for training = 1.459 GB.\n", "Peak reserved memory % of max memory = 40.541 %.\n", "Peak reserved memory for training % of max memory = 9.893 %.\n" ] } ], "source": [ "#@title Show final memory and time stats\n", "used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n", "used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n", "used_percentage = round(used_memory /max_memory*100, 3)\n", "lora_percentage = round(used_memory_for_lora/max_memory*100, 3)\n", "print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n", "print(f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\")\n", "print(f\"Peak reserved memory = {used_memory} GB.\")\n", "print(f\"Peak reserved memory for training = {used_memory_for_lora} GB.\")\n", "print(f\"Peak reserved memory % of max memory = {used_percentage} %.\")\n", "print(f\"Peak reserved memory for training % of max memory = {lora_percentage} %.\")" ] }, { "cell_type": "markdown", "metadata": { "id": "ekOmTR1hSNcr" }, "source": [ "\n", "### Inference\n", "Let's run the model! You can change the instruction and input - leave the output blank!\n", "\n", "**[NEW] Try 2x faster inference in a free Colab for Llama-3.1 8b Instruct [here](https://colab.research.google.com/drive/1T-YBVfnphoVc8E2E854qF3jdia2Ll2W2?usp=sharing)**" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "3cc3d51e-04ba-4440-aee8-1e5a9bbcc46d" }, "outputs": [ { "data": { "text/plain": [ "[' 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:\\nপ্যারিসের একটি বিখ্যাত লম্বা টাওয়ার কি?\\n\\n### Input:\\n\\n\\n### Response:\\nপ্যারিসের একটি বিখ্যাত লম্বা টাওয়ার হল একটি বিশ্ববিদ্যালয়ে']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "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", " \"প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার কি?\", # instruction\n", " \"\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", "tokenizer.batch_decode(outputs)" ] }, { "cell_type": "markdown", "metadata": { "id": "CrSvZObor0lY" }, "source": [ " You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "d2ec001d-f2c9-4b15-cf3c-8fe07dbf34ea" }, "outputs": [ { "name": "stdout", "output_type": "stream", "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", "ক্রমাগত ফিবোনাচি সিকোয়েন্স করবেন\n", "\n", "### Input:\n", "1, 1, 2, 3, 5, 8\n", "\n", "### Response:\n", "1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025, 121393\n" ] } ], "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", " \"ক্রমাগত ফিবোনাচি সিকোয়েন্স করবেন\", # instruction\n", " \"1, 1, 2, 3, 5, 8\", # input\n", " \"\", # output - leave this blank for generation!\n", " )\n", "], return_tensors = \"pt\").to(\"cuda\")\n", "\n", "from transformers import TextStreamer\n", "text_streamer = TextStreamer(tokenizer)\n", "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" ] }, { "cell_type": "markdown", "metadata": { "id": "uMuVrWbjAzhc" }, "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!" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 194, "referenced_widgets": [ "b394d711ae9242c788265f24c9a5ee42", "b416ab6b52f3458fac03d62c44ff70c3", "a1b0289b4fb146a59b9a3cbf3d1960d0", "17b0a9fada8c402f90f7ab8ac3f84006", "0994869c36a4402fa6bb0391c28027e0", "635701e66e4f44b39ee094f1e3953af9", "f5537922d4e94925a118b4102e1b5db3", "9fee45523ded45d99517e718e1229408", "ab0f71e2ef9543a799d270221b181e81", "af4415d6ed8e4f149cbe55ad78640a8e", "30983f8961bf4d83aacb77139690ed63", "25016ec6a1ff42f893105c6e2abcc587", "812857d3b17b403f989b3686cd520c02", "473bd6610c564b8c8ad7a4cfa842979d", "52081839832a457982c1415013515c97", "3e568bf542e34c269de58bf11fd360b7", "1b5347ed1b584555b82fe2e064595c67", "4db8741fb5334720b19a134b0686eaea", "9e51e029a44145c4ab2572cf91841696", "2fb10f28bb0a4ed498f37668d890daed", "8185830cefd94ee9805e4d79e8a20370", "268e472f9e5845fc895e167d0513aece", "a597206990d34c50a27d7f818ec24648", "7677cdddfa104dc9bdc352596fd57679", "dfa8f1e30c524bd9904de846bcb471df", "1703f1e2cede4393bdc5576aa28d8d26", "5b28753db78b45e3a083839ed0d71f17", "5259049306d74fa39e4184250a24c042", "8c9fd310580848ca8cc29b9b1bedc034", "d143438a3e574a78be2e052dc6dae33d", "34172978ed324047b813e6c42f5d8930", "3f88168116bf490297e5e8fa0cd31796", "5ac8c654086747a6850b6462ccee2c35", "f4087fc8d18c4b9f8edf648f812f4b40", "0e8b4643c42a462d90cbddd169fbb17b", "d6f1a54080fe47699cff262103aa3c5a", "4f98983f315f498981f5aa4de015076e", "9c3c23ca3aab4f72ba94c8dc769d8e92", "bcfa0a1a0022496e850f1d44cac0532b", "391cef2e49a74c94bf484fda4b413b2b", "54205208826b40d28f6387784198101a", "4fb5a1222abd43c885f6d4e38f80ccf2", "bd47b355ee6b45b681d0a03162e0872e", "b7b47d22e60b4f70bcab335bccc016f7", "4abd679e38dc4be09425ce126264175e", "a1d2719d076145d8a88e101a53937596", "161c263284df4f68a4358e4452dbef3a", "f288ceb6bc954b9cab4113f679266350", "66996de2024046ff9801f1d36859098c", "9827853eb4ff417387631c3bc95e5d49", "db269406493e40ee9257aeb1d9256901", "07f800e8119b48f8b51dff9f48397810", "1eae97f1eef448a28334a0055482204e", "b7183b3341c34670aacfd32ec049ac25", "93538debadaf4cad9b3563ea1b7c7e74" ] }, "id": "upcOlWe7A1vc", "outputId": "aee8974c-a0ad-4a2a-bca6-aeebd1e5adcf" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "b394d711ae9242c788265f24c9a5ee42", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/588 [00:00 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", "প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার কি?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার হল একটি বিশ্ববিদ্যালয়ের সাথে সংযোগ করার জন্য ব্যবহৃত একটি বিশ্ববিদ্যালয়ের সাথে \n" ] } ], "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"MistralBangla3v7b\", # 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", " \"প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার কি?\", # 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)" ] }, { "cell_type": "markdown", "metadata": { "id": "QQMjaNrjsU5_" }, "source": [ "You can also use Hugging Face's `AutoModelForPeftCausalLM`. Only use this if you do not have `unsloth` installed. It can be hopelessly slow, since `4bit` model downloading is not supported, and Unsloth's **inference is 2x faster**." ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "id": "yFfaXG0WsQuE" }, "outputs": [], "source": [ "if False:\n", " # I highly do NOT suggest - use Unsloth if possible\n", " from peft import AutoPeftModelForCausalLM\n", " from transformers import AutoTokenizer\n", " model = AutoPeftModelForCausalLM.from_pretrained(\n", " \"MistralBangla3v7b\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"MistralBangla3v7b\")" ] }, { "cell_type": "markdown", "metadata": { "id": "f422JgM9sdVT" }, "source": [ "### Saving to float16 for VLLM\n", "\n", "We also support saving to `float16` directly. Select `merged_16bit` for float16 or `merged_4bit` for int4. We also allow `lora` adapters as a fallback. Use `push_to_hub_merged` to upload to your Hugging Face account! You can go to https://huggingface.co./settings/tokens for your personal tokens." ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "id": "iHjt_SMYsd3P" }, "outputs": [], "source": [ "# Merge to 16bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_16bit\",)\n", "if False: model.push_to_hub_merged(\"vaugheu/MistralBangla3v7b\", 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(\"vaugheu/MistralBangla3v7b\", 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(\"vaugheu/MistralBangla3v7b\", tokenizer, save_method = \"lora\", token = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "TCv4vXHd61i7" }, "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.\n", "\n", "[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/drive/1WZDi7APtQ9VsvOrQSSC5DDtxq159j8iZ?usp=sharing)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "9b1d1d58b37f4c0d8d43cd0bfe013238", "59fab15e1c644925b04e051a02b42fa6", "48aeec0877b54f7bab9cdb19e5792d6b", "f751fcf5656b4240a576a0f4f309c51f", "a73546aa20c54fc6aa8e414ebd91a336", "7f35ec8e33ad453e905b9cad7a8cc2e5", "c9a42afb532542c78e86ff4618406c47", "843bc72177104b4782b95cb2f741129a", "e13f629d29b0413985598507411192a3", "528b64da057d43c395ff2ce1147a8576", "6894219580074769999b672cb6e677cd", "6ded895c12da47c89d0238e8ee66d11a", "4962b782c5ca4670af98825a3438ac13", "cde99f9f0c26432fbf9c34ee12f04852", "2db4c72d8b2448ffad721123c1641cc7", "9a33b54ab97449aa90aa43c3a017a419", "79d52fabb462442a97f443c6b361f213", "bc13c65b38ed4653ab3f9a99d861f4dc", "9eeec3eb1b384a51b606d71e8ad9a171", "ba699d43cb8f431b92a45858f6f0f6a5", "fe84cb01a2fc4803b6959f18b194c476", "c8509c42f414476e8ff36ca377a9b062" ] }, "id": "FqfebeAdT073", "outputId": "ae80596c-250b-4d21-ce8d-3acc4c0a0f68" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: You have 1 CPUs. Using `safe_serialization` is 10x slower.\n", "We shall switch to Pytorch saving, which will take 3 minutes and not 30 minutes.\n", "To force `safe_serialization`, set it to `None` instead.\n", "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n", "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", "Unsloth: Will remove a cached repo with size 4.1G\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 5.77 out of 12.67 RAM for saving.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 53%|█████▎ | 17/32 [00:02<00:01, 10.91it/s]We will save to Disk and not RAM now.\n", "100%|██████████| 32/32 [01:45<00:00, 3.31s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", "Unsloth: Saving model/pytorch_model-00001-of-00003.bin...\n", "Unsloth: Saving model/pytorch_model-00002-of-00003.bin...\n", "Unsloth: Saving model/pytorch_model-00003-of-00003.bin...\n", "Done.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: Converting mistral model. Can use fast conversion = True.\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "==((====))== Unsloth: Conversion from QLoRA to GGUF information\n", " \\\\ /| [0] Installing llama.cpp will take 3 minutes.\n", "O^O/ \\_/ \\ [1] Converting HF to GGUF 16bits will take 3 minutes.\n", "\\ / [2] Converting GGUF 16bits to ['q8_0'] will take 10 minutes each.\n", " \"-____-\" In total, you will have to wait at least 16 minutes.\n", "\n", "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n", "Unsloth: [1] Converting model at model into q8_0 GGUF format.\n", "The output location will be /content/model/unsloth.Q8_0.gguf\n", "This will take 3 minutes...\n", "INFO:hf-to-gguf:Loading model: model\n", "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n", "INFO:hf-to-gguf:Exporting model...\n", "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00003.bin'\n", "INFO:hf-to-gguf:token_embd.weight, torch.float16 --> Q8_0, shape = {4096, 32768}\n", "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.0.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.1.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.2.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.3.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.4.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.5.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.6.attn_q.weight, 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1024}\n", "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.7.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.7.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", 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"INFO:hf-to-gguf:blk.9.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.9.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.10.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.10.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.10.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.10.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.10.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.10.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00003.bin'\n", "INFO:hf-to-gguf:blk.10.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.10.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.10.ffn_norm.weight, 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"INFO:hf-to-gguf:blk.13.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.13.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.13.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.14.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.14.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.14.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.14.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.14.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", 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"INFO:hf-to-gguf:blk.15.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.16.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.16.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.16.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.16.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.16.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.16.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.16.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.16.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.17.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.17.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.17.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.17.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.17.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.17.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.17.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.17.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.18.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.18.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.18.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.18.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.18.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.18.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.18.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.18.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.18.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.19.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.19.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.19.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.19.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.19.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.19.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.19.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.19.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.19.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.20.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.20.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.20.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.20.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.20.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.20.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.20.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.20.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.20.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.21.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.21.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.21.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.21.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.21.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.21.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.21.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.21.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.21.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.22.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.22.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.22.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00003.bin'\n", "INFO:hf-to-gguf:blk.22.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.22.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.22.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.22.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.22.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.23.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.23.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.23.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.23.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.23.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.23.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.23.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.23.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.23.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.24.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.24.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.24.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.24.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.24.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.24.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.24.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.24.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.24.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.25.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.25.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.25.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.25.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.25.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.25.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.25.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.25.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.25.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.26.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.26.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.26.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.26.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.26.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.26.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.26.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.26.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.26.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.27.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.27.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.27.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.27.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.27.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.27.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.27.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.27.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.27.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.28.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.28.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.28.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.28.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.28.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.28.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.28.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.28.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.28.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.29.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.29.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.29.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.29.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.29.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.29.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.29.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.29.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.29.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.30.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.30.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.30.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.30.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.30.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.30.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.30.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.30.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.30.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.31.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.31.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.31.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.31.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.31.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.31.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.31.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.31.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.31.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:output_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:output.weight, torch.float16 --> Q8_0, shape = {4096, 32768}\n", "INFO:hf-to-gguf:Set meta model\n", "INFO:hf-to-gguf:Set model parameters\n", "INFO:hf-to-gguf:gguf: context length = 32768\n", "INFO:hf-to-gguf:gguf: embedding length = 4096\n", "INFO:hf-to-gguf:gguf: feed forward length = 14336\n", "INFO:hf-to-gguf:gguf: head count = 32\n", "INFO:hf-to-gguf:gguf: key-value head count = 8\n", "INFO:hf-to-gguf:gguf: rope theta = 1000000.0\n", "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-05\n", "INFO:hf-to-gguf:gguf: file type = 7\n", "INFO:hf-to-gguf:Set model tokenizer\n", "INFO:gguf.vocab:Setting special token type bos to 1\n", "INFO:gguf.vocab:Setting special token type eos to 2\n", "INFO:gguf.vocab:Setting special token type unk to 0\n", "INFO:gguf.vocab:Setting special token type pad to 770\n", "INFO:gguf.vocab:Setting add_bos_token to True\n", "INFO:gguf.vocab:Setting add_eos_token to False\n", "INFO:hf-to-gguf:Set model quantization version\n", "INFO:gguf.gguf_writer:Writing the following files:\n", "INFO:gguf.gguf_writer:/content/model/unsloth.Q8_0.gguf: n_tensors = 291, total_size = 7.7G\n", "Writing: 100%|██████████| 7.70G/7.70G [03:48<00:00, 33.6Mbyte/s]\n", "INFO:hf-to-gguf:Model successfully exported to /content/model/unsloth.Q8_0.gguf\n", "Unsloth: Conversion completed! Output location: /content/model/unsloth.Q8_0.gguf\n", "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 5.9 out of 12.67 RAM for saving.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 32/32 [02:44<00:00, 5.13s/it]\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving model... This might take 5 minutes for Llama-7b...\n", "Unsloth: Saving vaugheu/MistralBangla3v7b/pytorch_model-00001-of-00003.bin...\n", "Unsloth: Saving vaugheu/MistralBangla3v7b/pytorch_model-00002-of-00003.bin...\n", "Unsloth: Saving vaugheu/MistralBangla3v7b/pytorch_model-00003-of-00003.bin...\n", "Done.\n", "==((====))== Unsloth: Conversion from QLoRA to GGUF information\n", " \\\\ /| [0] Installing llama.cpp will take 3 minutes.\n", "O^O/ \\_/ \\ [1] Converting HF to GGUF 16bits will take 3 minutes.\n", "\\ / [2] Converting GGUF 16bits to ['q8_0'] will take 10 minutes each.\n", " \"-____-\" In total, you will have to wait at least 16 minutes.\n", "\n", "Unsloth: [0] Installing llama.cpp. This will take 3 minutes...\n", "Unsloth: [1] Converting model at vaugheu/MistralBangla3v7b into q8_0 GGUF format.\n", "The output location will be /content/vaugheu/MistralBangla3v7b/unsloth.Q8_0.gguf\n", "This will take 3 minutes...\n", "INFO:hf-to-gguf:Loading model: MistralBangla3v7b\n", "INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only\n", "INFO:hf-to-gguf:Exporting model...\n", "INFO:hf-to-gguf:gguf: loading model weight map from 'pytorch_model.bin.index.json'\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00001-of-00003.bin'\n", "INFO:hf-to-gguf:token_embd.weight, torch.float16 --> Q8_0, shape = {4096, 32768}\n", "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.0.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.1.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.2.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.3.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.4.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.5.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.6.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.6.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.6.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.6.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.6.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.6.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.6.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.7.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.7.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.7.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.7.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.7.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.8.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.8.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.9.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.9.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.9.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.9.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.9.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", 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"INFO:hf-to-gguf:blk.10.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.10.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.10.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.11.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.11.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.11.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.11.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.11.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.11.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.12.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.12.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.12.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.12.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.12.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.12.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.12.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.12.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.13.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.13.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.13.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.13.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.13.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.13.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.14.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.14.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.14.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.14.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.14.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.14.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.14.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.14.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.15.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.15.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.15.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.15.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.15.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.15.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.15.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.15.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.16.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.16.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.16.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.16.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.16.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.16.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.16.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.16.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.17.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.17.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.17.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.17.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.17.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.17.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.17.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.17.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.18.attn_q.weight, 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"INFO:hf-to-gguf:blk.21.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.21.ffn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.22.attn_q.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:blk.22.attn_k.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.22.attn_v.weight, torch.float16 --> Q8_0, shape = {4096, 1024}\n", "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 4096}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00003.bin'\n", "INFO:hf-to-gguf:blk.22.ffn_gate.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.22.ffn_up.weight, torch.float16 --> Q8_0, shape = {4096, 14336}\n", "INFO:hf-to-gguf:blk.22.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 4096}\n", "INFO:hf-to-gguf:blk.22.attn_norm.weight, torch.float16 --> F32, shape = {4096}\n", "INFO:hf-to-gguf:blk.22.ffn_norm.weight, 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