{ "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": null, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 403, "referenced_widgets": [ "51f1e28d282645a58c8b783f4f60cfc2", "7ca5730f63b4420cab8b124a17aaeb27", "64747677b60c489ebd3e769272533b3f", "5c5c498bd046409c860372110e523c7c", "ae087aee8e96402681d22d892d6cd476", "f340840186b4458dbff47afe987f1f59", "723b04a16f3a402589fdb9463834f3d1", "e50f33072d6d454b98c6607f9e847401", "3f97e7bd5fff420c80615d676e1648f0", "e7f6f37bf5b6483788e50c112b7ef007", "aa566decf6b4412caa2988fa623900ea", "b90fe47ddb6240bd90ddd1705a9f3fc9", "ca20885d9adc4815b5418073a7930f8f", "95aa990d762d428d93cef2834fb86c8a", "c2d228de02d14c6c8f780048b1ccc088", "c56ce88b9fab4475af5fafbc7a845010", "3e94165c4ab0471db8fb1fbd5b5bac0d", "74c3cbb850e44a4c9eb8283080ba075e", "20d356533da04942856986a33e7a99fb", "3cdf1d5b878b41838f0ec2b4d877e97a", "a2cde30a94d3462488bcb33693e3e274", "f04ed92a356c489a9877f82b05bb330f", "fe0cef5f02ca4e5e95b06356b8286fbe", "6db31893e3f84043b5abc6a24bac8228", "fd5eccb2370b40b58eea5c9f0d868e36", "90762bb3d5fd4f4db67f3a8a11434689", "004f3ec8f7a545c4bc54484dcb3022bb", "a6500ce74ca54e0ca650851502b14644", "de38fc3f3df348f29528d9acd6b9d981", "a3c7c2459ad14e9c81d7422d7e83393f", "fdd24808ed23442998104b5b28370aa6", "2e6a26fb12084d5487525f5e78ab5ac8", "8e082fef631b4eeab73c02e181f5690c", "b57e30ad94fa4b739d32c9553f5aee29", "4677d087bf6b40a3a4915ac7481f6e8d", "90c9301c342846729db7e3c6dfe5b849", "c7977b2008c2476596c5351012e710b6", "dcb2b3f1102a44429e62828b99ed39ab", "673da437f86a4371b7e3913a66de835a", "358035cd9f6943aeadc4cba1964109a6", "edb08520684f4f83a7094599ed55cb37", "c2185ae3f8aa4f3488e0bd7257664e26", "71f71606c101414bae187de7f145ea43", "bf1ea3ec39db442d91f74fdcfd1c5ac3", "62a6bc239405496ca1e451fbda8787f3", "7af5367620b64131a6fef8c2864d0d28", "d9f230d8474c40fc995c67c4f1eeb86a", "596156e7bb7346c1808ed960997a5159", "418ece30091b4d66aca4df6367e0bec5", "b1610596162844658f4ac1893f0fdd40", "87ccf938ee7641acb94c6050bb7c4b20", "d1462aa795714430bfee51674a619527", "cd98cb1f265448cf90adfd4fb3362b0d", "780d3c81c8e2461694df4d515d381d9d", "f5b0174aa23e432896d0dfe37387036b", "a80caed5f8af41ca99576d9daa68c6f6", "262070892253448793aba4d048f40c08", "e5486d352f314f45b663a6472d6ff885", "f033347d7cdb4f38a3eb3e05f546e438", "d1549b76e8ff4d69b17f9a0831b43551", "45d4f27475294750aff2487353c8105e", "3926bab2dbad4e5fb4362ee96d6fdd67", "e885fb98968949589006001c2f84a8eb", "1fa73eafabb14c73aaee39354c62477f", "2279b927aab74513aa1f6efb2c66c426", "b44759c58b284a5a950350a2cf82c4e6" ] }, "id": "QmUBVEnvCDJv", "outputId": "55acd488-9a43-4d68-8b55-0e3061ff247f" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.10.7: Fast Gemma2 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": "51f1e28d282645a58c8b783f4f60cfc2", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/6.13G [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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "3fddd29878ba408db098fb05db157710", "b542e0276854449d9ec4bed67279d037", "c3143dca63c6445fb7aa06d7d764d7a9", "e5c89397eb1a42bb895a4c540db2df1c", "ae243226e393499bac22c08d2b3d9570", "265fecfcffb44db580c066a04b5ea37b", "81b34a02e519484c965524acbe252807", "9179bf6bb49f477b9d9e5eb2f8015aaa", "d6dff4e305aa46fbaa9375133356378a", "e2844ec735d4420391b8ed1b9a932949", "d1ebb90d4a8e4656941f47d644013204", "902ea19651c546de8c19414daf6a053a", "4e40665bc93a414c87c089a3a0bb4008", "854179dbb6854ac5bb3d7240dcc3cb0b", "7b64ff6c0203472391e90ce30ed4165f", "17c55c44d870452c81d902b74c8cce79", "2b0e9c589f9848f2aeec3a97dacf2dc5", "a62221e4825a48caa9ef7f906fd43748", "f9ee25b240f74c21adfa24ce54659efd", "5b795861641e488fb6a47f88860a9ccd", "1f6d972a105a46438f51566f5a24cf84", "7c86fc2b3b0d4708bc2e55801894e37e", "f8a68ea30ebd4251931cf4d7b5be62a9", "f2f000db73f34f468b1c549c8422743a", "1ce1838f9eb34f74a615ad82cab78274", "0afe6c4f57a643159bc51aa36f099f61", "cc0bc8033830406a942b67c4cbbc5d28", "0bf1d81abe6f4a3493c29810857fc8dd", "f31165c434c7427fb4d26ea2af0feda9", "0a4506749df0400480090d3127285ed6", "641f767ab42e4190bdd3d0abfe851301", "e0211c2ff4fb46aaa85bf681c004a04c", "a593571b38dd4fd696e3d4778d2a9f03", "8c398b847644448e9d75135f3200f156", "63f16ab5e462454d88927b21df4427aa", "afb69e965b33472a8de2739c1cdff1e9", "5455311519604e9993d537555f372a0b", "a398a05038394c7b853237d751a0bbdd", "68b2f241810746b7973e2b94ba4c0122", "e4fd6646d36e4bce817e1e28dd99dc51", "c6ede16c623b49c7b4916e5ce4799125", "9a6fe19da592481bbe762912bc45bbed", "9439a307ffdd4705b7a1affb46d0fb71", "6a62779572c6495fb2594270742b6e58", "871336e6e4134fb28bd3b2fa606059cc", "0a8ca1638fe248d0856fd4c385f9a70b", "93a7d487abb0476383c7e57a3da1f851", "372011973a8e46a2888bf4299b042aa0", "2d5c3406ed7e4e03af04711751debd71", "afc36d622583404b942709b58027ffd2", "8b74367efcea4a50be0c0b205dc1dd47", "97d31d1c17d248f3b2dffe59143a9797", "c7fbd851c32746d3a2a0e69b411b2121", "cc0cb8826ef3428389ed6dfff6717d95", "57f60ec03bf14970b020302f317ea97a" ] }, "id": "LjY75GoYUCB8", "outputId": "062127be-de34-40cb-b112-d799a1873d64" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "3fddd29878ba408db098fb05db157710", "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 66, "referenced_widgets": [ "dd252c6d1d59418aa0f5b7c469351dee", "1949ce4af8c94c0ba7e3ac9d8df6b332", "e096cf56562a4e7281681be173d51b09", "89202b77af2a47b196cc8723c846e891", "3cd94a9a96894e51a652076762478155", "bac3b0bec13d492b86a5c65a0bb5b96f", "8adaf5cc36a3456094e077eca79c8b7e", "98cc2108542f443eb242a45fc671afef", "90620de3bb6a467d92b92622e5dfb0c5", "6f7ed19f4b77411c88223d59fa50d13a", "9d319b570bd64f0e9176817d577bc020" ] }, "id": "95_Nn-89DhsL", "outputId": "d644b905-6f99-42e2-8539-1bf9173a04bd" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd252c6d1d59418aa0f5b7c469351dee", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/172026 [00:00\n", " \n", " \n", " [200/200 35:17, Epoch 0/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", 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StepTraining Loss
11.701200
21.496500
31.836600
41.300100
51.385700
61.406400
71.361900
81.255600
91.073400
101.055400
110.924000
120.660100
131.054300
140.642100
151.163400
161.049700
171.200700
180.638300
190.920000
200.508700
211.129800
220.805900
230.588500
240.876600
250.920100
261.080800
271.081600
280.944700
290.940600
300.942900
310.718600
320.577500
330.764700
341.111100
351.084600
360.978700
370.765000
380.895000
390.792800
400.727800
410.849400
420.775200
430.710300
441.014700
451.042400
461.225500
470.571200
481.098000
490.872600
500.741700
510.979600
520.999200
530.556200
540.660700
550.784900
560.940400
570.701900
580.968700
590.682900
600.840300
610.526800
620.961600
630.754700
641.092100
650.929000
660.804800
671.272900
681.062800
691.383400
701.233700
711.016000
720.744300
730.800700
741.008500
750.906300
760.766700
771.090200
780.807400
790.550700
800.553800
810.999900
821.292100
831.061900
841.047400
850.734200
860.391800
870.702700
880.687700
890.822200
900.705000
910.763900
920.236300
930.749500
940.445200
950.500800
960.877400
970.884400
980.887000
990.889900
1000.895900
1011.042100
1021.052900
1030.953700
1040.752700
1050.921000
1060.897100
1070.784500
1080.712600
1090.716700
1101.199900
1110.844600
1120.810800
1130.704900
1141.119300
1150.408600
1160.431300
1171.093200
1180.649600
1190.685300
1201.326500
1210.722300
1220.580700
1230.890100
1240.722200
1250.901900
1260.383200
1270.765700
1281.099800
1291.230900
1301.045700
1310.643400
1321.044200
1330.984500
1341.070600
1351.073700
1360.388500
1370.962500
1381.048300
1390.661400
1400.906000
1410.725700
1420.888300
1430.254600
1440.824500
1450.814300
1460.965900
1470.719700
1481.137200
1490.745100
1500.972400
1510.530900
1520.816800
1530.740300
1540.808000
1551.164000
1560.523100
1571.065800
1581.191600
1590.865600
1600.839400
1610.975000
1620.614300
1631.052100
1640.889800
1650.402000
1660.633400
1670.800300
1680.973800
1690.466100
1700.877100
1710.752700
1721.166300
1730.919500
1740.701400
1750.902800
1760.895900
1770.808900
1780.631700
1790.588300
1800.901700
1811.015800
1820.893900
1830.726100
1840.814900
1850.589000
1860.728600
1870.884300
1880.791000
1890.917300
1900.954500
1911.196100
1920.870400
1930.949800
1940.982200
1950.965000
1961.317000
1970.497100
1980.655100
1991.060100
2000.994400

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "colab": { "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", "outputId": "fb7f67f1-f97e-4cef-b87f-ac93b53950c4" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "2194.7755 seconds used for training.\n", "36.58 minutes used for training.\n", "Peak reserved memory = 10.35 GB.\n", "Peak reserved memory for training = 3.774 GB.\n", "Peak reserved memory % of max memory = 70.179 %.\n", "Peak reserved memory for training % of max memory = 25.59 %.\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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "1c987413-c7c4-4c8d-a807-c3793139f58e" }, "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 ইয়ারা টাওয়ার। এটি প্যারিসের 16তম জেলায় অবস্থিত এবং এটি প্যারিসের সবচে']" ] }, "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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", "outputId": "6a62a532-f947-4063-d9be-fe381711bfa4" }, "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", "ক্রমাগত ফিবোনাচি সিকোয়েন্স হল একটি সিকোয়েন্স যা প্রতিটি সংখ্যাটি এর আগের দুটি সংখ্যার যোগফলের সমান। প্রদত্ত সিকোয়েন্সে, প্রথম দুটি সংখ্যা 1 এবং 1। পরবর্তী সংখ্যাটি 1 এবং 1 এর যোগফল, যা 2। তৃতীয় সংখ্যাটি 1 এবং 2 এর যোগফল, যা \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": null, "metadata": { "id": "upcOlWe7A1vc" }, "outputs": [], "source": [ "# # model.save_pretrained(\"BanglaGemma9b_GGUF\") # Local saving\n", "# # tokenizer.save_pretrained(\"BanglaGemma9b_GGUF\")\n", "# model.push_to_hub(\"vaugheu/BanglaGemma9b_GGUF\", token = \"hf_\") # Online saving\n", "# tokenizer.push_to_hub(\"vaugheu/BanglaGemma9b_GGUF\", token = \"hf_\") # Online saving" ] }, { "cell_type": "markdown", "metadata": { "id": "AEEcJ4qfC7Lp" }, "source": [ "Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", "outputId": "77981de7-6c2c-46ea-e978-64d59285a969" }, "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", "প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার কি?\n", "\n", "### Input:\n", "\n", "\n", "### Response:\n", "প্যারিসের একটি বিখ্যাত লম্বা টাওয়ার হল ইয়ারা টাওয়ার। এটি প্যারিসের 16তম জেলায় অবস্থিত এবং এটি প্যারিসের সবচেয়ে উঁচু ভবন। এটি 1973 সালে নির্মিত হয়েছিল এবং এটি 187 মিটার (614 ফুট) উঁচু।\n" ] } ], "source": [ "if False:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"BanglaGemma9b_GGUF\", # 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": null, "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", " \"BanglaGemma9b_GGUF\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"BanglaGemma9b_GGUF\")" ] }, { "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": null, "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/BanglaGemma9b_GGUF\", 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/BanglaGemma9b_GGUF\", 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/BanglaGemma9b_GGUF\", 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": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "57125af4f34d49d0b58888ad9e21415d", "bbf22f44362b4805a884fc3d8b2d9d17", "65d84eea4ef64e1ab9267c5f8fc25600", "58fdbca58f604e949f2b079dae3a33c6", "69593167eac74b069ba9ea11b5a2df98", "7b57b77c678c489b8fd28bf9ecf220b8", "5bcff80df0554cac936d1cd5a12c7330", "8100ac5e848d465b82435f46917e89b9", "75e4ed63347846dab807a972dbbd8f4b", "23c50152d387453cbb40d588ccdba734", "14db196d277e48a7bde44205351d354d", "45c4bfa78e31480d890c5ccd2d05cd73", "67188a4a909a4ea8a48cb3919507daa7", "1b6dfa7096fc4a13845b76a861e0fbf3", "7a4f7546d7d846f69cea34560da96a6f", "485ed3f8011e4fdebeeb180db45df130", "0335fd59b20f40039e53bfafd4a9f014", "fd276879b0c7416caffb6ca0b87f7079", "368ddc19d093459daa15702474b7e9f3", "6121fa09c08d40f1ac39f58900b453f8", "369758bdc0ec4a9ba753c5164c83e4d5", "d4dcefae7523463bb2d836869e89bd69" ] }, "id": "FqfebeAdT073", "outputId": "60525780-d801-48ca-845d-dff01a3f2c81" }, "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 6.1G\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", "Unsloth: Will use up to 5.37 out of 12.67 RAM for saving.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 31%|███ | 13/42 [00:01<00:02, 10.60it/s]We will save to Disk and not RAM now.\n", "100%|██████████| 42/42 [03:53<00:00, 5.57s/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-00004.bin...\n", "Unsloth: Saving model/pytorch_model-00002-of-00004.bin...\n", "Unsloth: Saving model/pytorch_model-00003-of-00004.bin...\n", "Unsloth: Saving model/pytorch_model-00004-of-00004.bin...\n", "Done.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Unsloth: Converting gemma2 model. Can use fast conversion = False.\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-00004.bin'\n", "INFO:hf-to-gguf:token_embd.weight, torch.float16 --> Q8_0, shape = {3584, 256000}\n", "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.0.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.1.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.2.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.3.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.4.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.5.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.6.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.6.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.6.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.6.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.6.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.7.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.7.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n", "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.7.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.8.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.9.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.9.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.9.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.9.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.9.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.9.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.9.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.10.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.10.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.10.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.10.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.10.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.10.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.10.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.11.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.11.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.11.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.11.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", 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"INFO:hf-to-gguf:blk.39.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.39.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.39.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.39.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.40.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.40.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.40.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.40.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.40.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.40.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.40.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.40.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.40.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.40.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.40.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.41.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.41.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.41.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.41.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.41.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.41.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.41.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:output_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:Set meta model\n", "INFO:hf-to-gguf:Set model parameters\n", "INFO:hf-to-gguf:Set model tokenizer\n", "INFO:gguf.vocab:Setting special token type bos to 2\n", "INFO:gguf.vocab:Setting special token type eos to 1\n", "INFO:gguf.vocab:Setting special token type unk to 3\n", "INFO:gguf.vocab:Setting special token type pad to 0\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 = 464, total_size = 9.8G\n", "Writing: 100%|██████████| 9.82G/9.82G [03:34<00:00, 45.8Mbyte/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 5.48 out of 12.67 RAM for saving.\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 42/42 [02:17<00:00, 3.28s/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/BanglaGemma9b_GGUF/pytorch_model-00001-of-00004.bin...\n", "Unsloth: Saving vaugheu/BanglaGemma9b_GGUF/pytorch_model-00002-of-00004.bin...\n", "Unsloth: Saving vaugheu/BanglaGemma9b_GGUF/pytorch_model-00003-of-00004.bin...\n", "Unsloth: Saving vaugheu/BanglaGemma9b_GGUF/pytorch_model-00004-of-00004.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/BanglaGemma9b_GGUF into q8_0 GGUF format.\n", "The output location will be /content/vaugheu/BanglaGemma9b_GGUF/unsloth.Q8_0.gguf\n", "This will take 3 minutes...\n", "INFO:hf-to-gguf:Loading model: BanglaGemma9b_GGUF\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-00004.bin'\n", "INFO:hf-to-gguf:token_embd.weight, torch.float16 --> Q8_0, shape = {3584, 256000}\n", "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.0.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.1.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.2.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.3.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.4.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.5.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.6.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.6.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.6.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.6.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.6.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.6.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.7.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.7.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n", "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.7.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.8.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.8.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.9.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.9.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.9.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.9.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.9.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", 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"INFO:hf-to-gguf:blk.10.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.10.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.11.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.11.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.11.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.11.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.11.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.12.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.12.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.12.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.12.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.12.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.12.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.12.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.12.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.12.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.12.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.13.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.13.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.13.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.13.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.13.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.14.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.14.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.14.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.14.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.14.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.14.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.15.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.15.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.15.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.15.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.15.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.15.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.16.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 4096}\n", "INFO:hf-to-gguf:blk.16.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.16.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 2048}\n", "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> Q8_0, shape = {4096, 3584}\n", "INFO:hf-to-gguf:blk.16.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.16.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", 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"INFO:hf-to-gguf:blk.41.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.41.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 14336}\n", "INFO:hf-to-gguf:blk.41.ffn_down.weight, torch.float16 --> Q8_0, shape = {14336, 3584}\n", "INFO:hf-to-gguf:blk.41.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.post_attention_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.41.post_ffw_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:output_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:Set meta model\n", "INFO:hf-to-gguf:Set model parameters\n", "INFO:hf-to-gguf:Set model tokenizer\n", "INFO:gguf.vocab:Setting special token type bos to 2\n", "INFO:gguf.vocab:Setting special token type eos to 1\n", "INFO:gguf.vocab:Setting special token type unk to 3\n", 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