{ "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": 540, "referenced_widgets": [ "b449d3863ad546df87920cd81ed8371b", "b7e5888a16b243cbad337b32831942a3", "29ac4a137e51488a9a1ce07cbc22d913", "e6e58802baed40798ef8cb84cd3d556b", "3b8db6d3898b42f09e08a546ed9919cb", "846ea851cc6e46e1872a13c5d730c1ef", "ef39200ea3d64d8e8334203c1bac5fc1", "f89182f76b094a9b80d5afa54706b52b", "53da07e8eaa746a0835fdeb3012ed551", "2d749bebfb934d79b97a8e0d3860aeef", "b423bfd7a59947a1acaa2ca0f8a0c49c", "d9a77bb5a35245338759cd1360aa2242", "1fd57663a56445e6aeb32bcf8e8148c3", "f2a3a25f39cd497597d1e01af84227dc", "89e9949f160942dabcddc323e57a5a38", "23dc273f262247f09dbf5cca2aaaf341", "8a489214db174d31894c67c8ed191ae0", "726c9a0ccbe84cb49436c7fca4c2672c", "a8f4b5f4a4d34fe7872ac98f2fe2f74e", "a3b37b75ef19476fb4073f0ef4aa9468", 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"fa7a1a9b61f34196a0a4f2d7a064416c", "e352b5778a604566a4f60dcf58ce56fb", "6b9f70fb7980483999513b8c99b9a9f9", "a837692db1ad4b92b43eb7c66e13acd0" ] }, "id": "QmUBVEnvCDJv", "outputId": "54eddd3b-4098-486e-89b6-a8f6a003f762" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "🦥 Unsloth Zoo will now patch everything to make training faster!\n", "==((====))== Unsloth 2025.3.10: Fast Qwen2 patching. Transformers: 4.48.3.\n", " \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False]\n", " \"-____-\" Free license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors.index.json: 0%| | 0.00/106k [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": { "id": "LjY75GoYUCB8", "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "61d31cad4ed148feac4cf91d96758504", "3c500667cffc4124b8a610b44e38e7b5", "55530610f7e84ea2b42ffc027a37f284", "065a424c704049b58728def50a1084de", "a7207a150304426a863890dfb9f42804", "5e899553400a46d9a74950a70dcc4bf7", "a3d913c70c6a499b8fcbb4e3bfc55f29", "1437ca1986af4b29b33b8264149ddcf4", "ba8d7d0dc9c749c0b7f6dc9a3b884217", "b56012592dab420dac4a505bc8c95cc7", "733f8dfa7290491998992744a10626a9", "5f1100f1f3324d20b7b197ff485cd8e3", "ac19992640ef44ee9a50864ae59f08c5", "91d9b77fa527438186d6f8fdb5de61a5", "6842a21c0cae4ab5ae02bc4e7348c49b", "c5d9581aef05413682f8590861657dc9", "840026a463d945e4a5dff3e52048f8a0", "5e2d9e3753cc4895abf4060cb0fdb1d0", "9fb801fd14fd48bb90868f72a389afe5", "a9763ce9c2c542e7bb980418033c786f", "7c3156f581ce445b89bbc59516b01063", "9832cfe09f8d4de69737160023a0f1f0", "b660520d8cdf4a7eb40c5150ed74b740", "b0b0f76bffcc492585ba871fe24cd8a0", "ad975abf050f4ae5b20e6ad8e0389071", "78a3a685806c456892e1f14daba59e32", "69d41f2399584bcbaf66beba0279945b", "7342c87b8e1544429f6ae220acfaf150", "abb0261be3104ee8bbc7a3f8ac816ac5", "13d07d1b9e454d429dbddd61a8f08177", "712c5a50dd084a8e99cfcde46f786b24", "c812ba1e616843d2aaddb9c1f8e62890", "60a221280f214eaab80350b7c52c931f", "1626fec925ba48d084bb6faf5558d414", "3505785d69fb4fda98cdd374c88a72f1", "8c097817ab254212b1547115049e4135", "21481d52c6c74fdfa2b96fa2c9e99e78", "f542835810a44323b410444af741bb0b", "1d2e27c738324117aed2752e60472dfd", "cc304c8dc70a43f0a3e04bba9dc732c9", "d3d66a0326de4338aa27613c697774e4", "4d9830317bb940e9850cb9411bf7f090", "d456cde39fac428b8a143158e86f9bfa", "31167abf22cf49bda4512f8ec5b7fdab", "1683b0560df44119a246dd578551deaa", "768b1e1b165149afb7b3d55a7ac7a479", "e081c82480a14850831e6268e2697d8b", "69a8ed1df8234878b40d0beb9f9e649d", "a3a9660630534ac08cbae102a52a7669", "fae4f8cfc72042bab6ab650b521378fc", "e470760faf6c476985587b5dbd9e1a07", "7ae8a61e89284314b7cc77777e07560f", "85e1e562f1e5448ab2092c6a34396d98", "c2e5faf8e426441fa5a42bb50c286d9c", "aef36e680e2847c3b9ae040ed2b0920d" ] }, "outputId": "2d3ff6a3-5555-4051-c94b-811b61bcb1bf" }, "outputs": [ { "output_type": "display_data", "data": { "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": { "id": "95_Nn-89DhsL", "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "941c16695f5a4ce787b19902ba047a22", "edb01a7987aa453bada7aa1ef5f7e88e", "7c4a564dbb7142ee890de4d01ddfd893", "098cf8e21f224fda961a37549286a40f", "cbdad833ce1a459099cfbabc5efed123", "93f5ea33a4464d58a9e5d1e00dc51648", "f8a0b7e34d2045c8bb519d62e5eed5ff", "bc3ed91a6c684257b0ce959e9288f196", "3ada711e4f604a598f619f978d328a0e", "c8073d6d19ca4048af7d3a651b15ccb2", "c8dde25a6dd34200af849a30bba95f8c" ] }, "outputId": "c34a36df-65b3-4108-f45e-e5cee52dcb1f" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Unsloth: Tokenizing [\"text\"] (num_proc=2): 0%| | 0/172026 [00:00" ], "text/html": [ "\n", "
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StepTraining Loss
10.786800
20.653900
30.869400
40.690800
50.930800
60.878400
70.907800
80.861300
90.664800
100.708000
110.656900
120.667500
130.757500
140.868000
150.869800
160.689300
170.766400
180.790300
190.614500
200.603500
210.882200
220.755100
230.575300
240.617000
250.810800
260.772400
270.798600
280.663100
290.699900
300.777000
310.660400
320.583500
330.649100
340.777400
350.762900
360.822200
370.661800
380.648400
390.689800
400.560600
410.746500
420.777100
430.665200
440.788600
450.741900
460.836600
470.430300
480.658900
490.732400
500.623700
510.657900
520.664700
530.460700
540.531700
550.668200
560.633100
570.842100
580.686700
590.616700
600.742900
610.522000
620.695800
630.717600
640.666700
650.703900
660.894600
670.904200
680.759700
691.003300
700.895200
710.715100
720.649400
730.553300
740.719700
750.658200
760.670100
770.719500
780.574500
790.668400
800.607900
810.684600
820.859700
830.785100
840.640900
850.620600
860.456300
870.651100
880.516900
890.658800
900.560900
910.673800
920.332200
930.631800
940.569900
950.559000
960.771300
970.624600
980.697900
990.699400
1000.788500
1010.593300
1020.805300
1030.686200
1040.701000
1050.759500
1060.625200
1070.670900
1080.781100
1090.590300
1100.849600
1110.737200
1120.702000
1130.747000
1140.831000
1150.513800
1160.385900
1170.773800
1180.556800
1190.604400
1200.867800
1210.587400
1220.664300
1230.572100
1240.703000
1250.648600
1260.315500
1270.555700
1280.681600
1290.857800
1300.770500
1310.554800
1320.810800
1330.664300
1340.806200
1350.730200
1360.373300
1370.657100
1380.689800
1390.775600
1400.574500
1410.554700
1420.594000
1430.332200
1440.550400
1450.718700
1460.750600
1470.606100
1480.764700
1490.867200
1500.689800
1510.555000
1520.577700
1530.578400
1540.642600
1550.837800
1560.711700
1570.774500
1580.815800
1590.682900
1600.755100
1610.640000
1620.672700
1630.691400
1640.805700
1650.401400
1660.568200
1670.683400
1680.670800
1690.598800
1700.640100
1710.693600
1720.855200
1730.588100
1740.570400
1750.609700
1760.650200
1770.608300
1780.551200
1790.747700
1800.573300
1810.767300
1820.663300
1830.683200
1840.771500
1850.388200
1860.607200
1870.570400
1880.758700
1890.671800
1900.687100
1910.784200
1920.520400
1930.622400
1940.592000
1950.660800
1960.863100
1970.646600
1980.791000
1990.748800
2000.683100

" ] }, "metadata": {} } ], "source": [ "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "pCqnaKmlO1U9", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "32cbd5ec-aa6b-4d8b-ef6e-5f4098b744ce" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "2361.6394 seconds used for training.\n", "39.36 minutes used for training.\n", "Peak reserved memory = 9.52 GB.\n", "Peak reserved memory for training = 2.274 GB.\n", "Peak reserved memory % of max memory = 64.582 %.\n", "Peak reserved memory for training % of max memory = 15.426 %.\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": { "id": "kR3gIAX-SM2q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "11bf9a11-fe8e-48ff-f2bd-3c341bbbf21e" }, "outputs": [ { "output_type": "execute_result", "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:\\nকিভাবে অর্থ উপার্জন করতে হয়?\\n\\n### Input:\\n\\n\\n### Response:\\nঅর্থ উপার্জন করার জন্য একটি সাধারণ পদ্ধতি হল একটি সংগঠিত অর্থ �']" ] }, "metadata": {}, "execution_count": 9 } ], "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)\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is life?\", # 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)\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"What is the capital of Austrelia?\", # 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)\n", "\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"কিভাবে অর্থ উপার্জন করতে হয়?\", # 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": { "id": "e2pEuRb1r2Vg", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "558b5c20-527d-47dc-a22f-0584286e863b" }, "outputs": [ { "output_type": "stream", "name": "stdout", "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", "একটি ক্রমাগত ফিবোনাচি সিকোয়েন্স হল একটি সিরিজ যেখানে প্রতিটি সংখ্যা পূর্বের দুটি সংখ্যার যোগফল হয়। এই সিরিজের প্রথম দ\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", "colab": { "base_uri": "https://localhost:8080/", "height": 194, "referenced_widgets": [ "e0663eed5205417eb07707ffaebb2721", "cd8faa6765f843f29e28cae8b177f028", "c85351eb641f454e80fd482299eb4eca", "0c7cdd261ec14e1a9230a0e642467790", "a271a8caf3c647169bd9c5e243231594", "cc7282a2e6fc4b2394d2cbda155673b1", "608931c165ad48b5a6e3d8749cf980ab", "ba4c2f3be9a34d94ae316a9841a08045", "9374c5d0411846b6abd05346e39c8075", "2d819fa0112f4817a95d252095bd34a0", "38739a1ee63143adbcd355c33b9eee93", "f01d2b8b529d46d3aee76fc634a61e6e", "67e4997020cf4ba095f7818d7dc53193", "dbb55c09950a48c68708225f2999db87", "f0e200bfccb949dd897c88bea5f21bc0", "0b7c9345b4f641cebc8bb8b24d8dfc67", "25ffb4a56c0f43018b6236ca2972e23c", "a7cb143fcb164ceaa96ca9efa091698d", "94b544328d0344b9bfab5574ccc08f28", "8ebd1e6fc59a4fd1b3310d58cf4eed7c", "13844b3d1dd24b03a6d785f9829fcf2e", "245f6466f03e4e99bd74a88901e6be56", "6e07a0cb617b4d4ea095b44479eccbac", "789961519f01444cb91e2e9f503103db", "32881767f34c4f31b4bbe8cf0a0cac89", "22ffec3c1ead4ae3b602e8e0f3067ecd", "2e3cc5fac068446296f4b3a7d5eb790b", "2522b787cc9b4600b1e042f977e1604c", "10879c481f9446f58579932803fa311d", "9fce0080c58740c6a3d28a94d982dbfa", "74dd85e85e094f7e8f5b8d867aa73293", "33577d04200d4ea4960019625ac977be", "2c9f3875329046e99c998f37b9dda14e", "e5f49d65af344f50a87cc82d86b52ee4", "8e892b6d469d45ad996177cd25c48610", "2e1438f709064150aab5109a8e03f7d6", "49f8a868af334ad6a179796f69ed326e", "0e5d09a5ac144896a00542e8c5e60431", "aa63c1939a8a4b2bbc7da50eebcbfed5", "cddf1c9f6f6f42b6b471337fb49567c5", "9afa0625675041b0bec2714501ce4a69", "3995a98bdee544edbec8a9167d9e1ea3", "261d4904005c4265bef139ec79b54b64", "ed218444586b483fbe61c7f512d97f81", "15f8246494294f2f80f6563578ed1909", "bb92e62d80e041949ad5b4dc4998e4d0", "75d720c302ee4b9c968735d9d68a0320", "3461c445b5f44ae0b24b07fa23b63011", 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Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.12.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.12.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.12.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.12.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.12.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.12.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.12.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.12.attn_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.13.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.13.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.13.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.13.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.13.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.13.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.13.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.13.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.13.attn_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.14.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.14.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.14.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.14.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.14.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.14.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.14.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.14.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.14.attn_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.15.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.15.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.15.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.15.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.15.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.15.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.15.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.15.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.15.attn_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.16.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.16.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.16.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.16.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.16.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.16.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.16.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.16.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.16.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.16.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.16.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.17.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.17.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.17.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.17.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.17.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.17.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.17.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.17.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.18.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.18.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.18.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.18.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.18.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.18.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.18.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n", "INFO:hf-to-gguf:blk.18.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.18.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.19.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.19.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.19.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.19.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.19.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.19.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.19.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.19.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.19.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.20.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.20.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.20.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.20.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.20.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.20.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.20.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.20.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.20.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.21.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.21.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.21.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.21.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.21.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.21.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.21.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.21.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.21.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.22.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.22.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.22.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.22.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.22.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.22.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.22.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.22.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.23.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.23.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.23.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.23.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.23.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.23.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.23.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.23.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.23.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.24.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.24.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.24.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.24.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.24.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.24.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.24.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.24.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.24.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.25.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.25.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.25.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.25.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.25.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.25.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.25.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.25.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.25.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.26.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.26.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.26.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.26.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.26.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.26.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.26.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.26.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.26.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.27.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.27.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.27.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.27.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.27.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.27.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.27.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.27.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.27.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.ffn_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:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n", "INFO:hf-to-gguf:output.weight, torch.float16 --> Q8_0, shape = {3584, 152064}\n", "INFO:hf-to-gguf:Set meta model\n", "INFO:hf-to-gguf:Set model parameters\n", "INFO:hf-to-gguf:gguf: context length = 131072\n", "INFO:hf-to-gguf:gguf: embedding length = 3584\n", "INFO:hf-to-gguf:gguf: feed forward length = 18944\n", "INFO:hf-to-gguf:gguf: head count = 28\n", "INFO:hf-to-gguf:gguf: key-value head count = 4\n", "INFO:hf-to-gguf:gguf: rope theta = 1000000.0\n", "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06\n", "INFO:hf-to-gguf:gguf: file type = 7\n", "INFO:hf-to-gguf:Set model tokenizer\n", "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n", "2025-03-14 06:56:12.806127: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1741935372.840383 18070 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1741935372.852689 18070 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "INFO:gguf.vocab:Adding 151387 merge(s).\n", "INFO:gguf.vocab:Setting special token type eos to 151643\n", "INFO:gguf.vocab:Setting special token type pad to 151654\n", "INFO:gguf.vocab:Setting add_bos_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 = 339, total_size = 8.1G\n", "Writing: 100%|██████████| 8.09G/8.09G [03:02<00:00, 44.4Mbyte/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 4.64 out of 12.67 RAM for saving.\n", "Unsloth: Saving model... This might take 5 minutes ...\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "100%|██████████| 28/28 [01:58<00:00, 4.22s/it]\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Unsloth: Saving tokenizer... Done.\n", "Unsloth: Saving vaugheu/QwenBangla2.5v7B/pytorch_model-00001-of-00004.bin...\n", "Unsloth: Saving vaugheu/QwenBangla2.5v7B/pytorch_model-00002-of-00004.bin...\n", "Unsloth: Saving vaugheu/QwenBangla2.5v7B/pytorch_model-00003-of-00004.bin...\n", "Unsloth: Saving vaugheu/QwenBangla2.5v7B/pytorch_model-00004-of-00004.bin...\n", "Done.\n", "==((====))== Unsloth: Conversion from QLoRA to GGUF information\n", " \\\\ /| [0] Installing llama.cpp might take 3 minutes.\n", "O^O/ \\_/ \\ [1] Converting HF to GGUF 16bits might take 3 minutes.\n", "\\ / [2] Converting GGUF 16bits to ['q8_0'] might take 10 minutes each.\n", " \"-____-\" In total, you will have to wait at least 16 minutes.\n", "\n", "Unsloth: Installing llama.cpp. This might take 3 minutes...\n", "Unsloth: [1] Converting model at vaugheu/QwenBangla2.5v7B into q8_0 GGUF format.\n", "The output location will be /content/vaugheu/QwenBangla2.5v7B/unsloth.Q8_0.gguf\n", "This might take 3 minutes...\n", "INFO:hf-to-gguf:Loading model: QwenBangla2.5v7B\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, 152064}\n", "INFO:hf-to-gguf:blk.0.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.0.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.0.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.0.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.0.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.0.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.0.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.0.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.0.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.0.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.0.attn_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.1.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.1.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.1.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.1.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.1.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.1.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.1.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.1.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.1.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.1.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.1.attn_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.2.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.2.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.2.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.2.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.2.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.2.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.2.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.2.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.2.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.2.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.2.attn_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.3.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.3.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.3.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.3.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.3.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.3.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.3.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.3.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.3.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.3.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.3.attn_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.4.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.4.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.4.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.4.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.4.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.4.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.4.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.4.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.4.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.4.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.4.attn_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.5.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.5.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.5.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.5.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.5.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.5.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.5.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.5.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.5.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.5.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.5.attn_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.6.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.6.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.6.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.6.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.6.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.6.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.6.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.6.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.6.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.6.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.6.attn_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.7.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.7.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.7.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.7.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.7.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.7.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.7.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.7.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.7.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.7.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.7.attn_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.8.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.8.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.8.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.8.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.8.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.8.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.8.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00002-of-00004.bin'\n", "INFO:hf-to-gguf:blk.8.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.8.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.8.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.8.attn_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.9.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.9.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.9.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.9.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.9.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.9.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.9.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.9.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.9.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.9.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.9.attn_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.10.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.10.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.10.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.10.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.10.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.10.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.10.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.10.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.10.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.10.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.10.attn_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.11.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.11.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.11.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.11.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.11.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.11.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.11.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.11.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.11.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.11.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.11.attn_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.12.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.12.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.12.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.12.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.12.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.12.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.12.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.12.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.12.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.12.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.12.attn_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.13.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.13.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.13.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.13.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.13.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.13.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.13.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.13.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.13.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.13.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.13.attn_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.14.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.14.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.14.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.14.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.14.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.14.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.14.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.14.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.14.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.14.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.14.attn_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.15.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.15.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.15.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.15.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.15.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.15.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.15.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.15.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.15.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.15.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.15.attn_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.16.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.16.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.16.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.16.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.16.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.16.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.16.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.16.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.16.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.16.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.16.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.16.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.17.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.17.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.17.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.17.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.17.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.17.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.17.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.17.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.17.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.17.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.18.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.18.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.18.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.18.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.18.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.18.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.18.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:gguf: loading model part 'pytorch_model-00003-of-00004.bin'\n", "INFO:hf-to-gguf:blk.18.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.18.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.18.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.19.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.19.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.19.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.19.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.19.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.19.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.19.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.19.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.19.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.19.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.20.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.20.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.20.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.20.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.20.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.20.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.20.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.20.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.20.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.20.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.21.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.21.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.21.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.21.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.21.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.21.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.21.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.21.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.21.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.21.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.22.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.22.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.22.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.22.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.22.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.22.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.22.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.22.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.22.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.22.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.23.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.23.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.23.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.23.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.23.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.23.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.23.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.23.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.23.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.23.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.24.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.24.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.24.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.24.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.24.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.24.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.24.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.24.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.24.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.24.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.25.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.25.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.25.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.25.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.25.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.25.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.25.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.25.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.25.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.25.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.26.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.26.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.26.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.26.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.26.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.26.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.26.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.26.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.26.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.26.ffn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.attn_q.bias, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.attn_q.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.27.attn_k.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.27.attn_k.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.27.attn_v.bias, torch.float16 --> F32, shape = {512}\n", "INFO:hf-to-gguf:blk.27.attn_v.weight, torch.float16 --> Q8_0, shape = {3584, 512}\n", "INFO:hf-to-gguf:blk.27.attn_output.weight, torch.float16 --> Q8_0, shape = {3584, 3584}\n", "INFO:hf-to-gguf:blk.27.ffn_gate.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.27.ffn_up.weight, torch.float16 --> Q8_0, shape = {3584, 18944}\n", "INFO:hf-to-gguf:blk.27.ffn_down.weight, torch.float16 --> Q8_0, shape = {18944, 3584}\n", "INFO:hf-to-gguf:blk.27.attn_norm.weight, torch.float16 --> F32, shape = {3584}\n", "INFO:hf-to-gguf:blk.27.ffn_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:gguf: loading model part 'pytorch_model-00004-of-00004.bin'\n", "INFO:hf-to-gguf:output.weight, torch.float16 --> Q8_0, shape = {3584, 152064}\n", "INFO:hf-to-gguf:Set meta model\n", "INFO:hf-to-gguf:Set model parameters\n", "INFO:hf-to-gguf:gguf: context length = 131072\n", "INFO:hf-to-gguf:gguf: embedding length = 3584\n", "INFO:hf-to-gguf:gguf: feed forward length = 18944\n", "INFO:hf-to-gguf:gguf: head count = 28\n", "INFO:hf-to-gguf:gguf: key-value head count = 4\n", "INFO:hf-to-gguf:gguf: rope theta = 1000000.0\n", "INFO:hf-to-gguf:gguf: rms norm epsilon = 1e-06\n", "INFO:hf-to-gguf:gguf: file type = 7\n", "INFO:hf-to-gguf:Set model tokenizer\n", "INFO:numexpr.utils:NumExpr defaulting to 2 threads.\n", "2025-03-14 07:07:29.157415: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n", "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n", "E0000 00:00:1741936049.215711 20948 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n", "E0000 00:00:1741936049.230767 20948 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n", "INFO:gguf.vocab:Adding 151387 merge(s).\n", "INFO:gguf.vocab:Setting special token type eos to 151643\n", "INFO:gguf.vocab:Setting special token type pad to 151654\n", "INFO:gguf.vocab:Setting add_bos_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/vaugheu/QwenBangla2.5v7B/unsloth.Q8_0.gguf: n_tensors = 339, total_size = 8.1G\n", "Writing: 100%|██████████| 8.09G/8.09G [03:17<00:00, 41.0Mbyte/s]\n", "INFO:hf-to-gguf:Model successfully exported to /content/vaugheu/QwenBangla2.5v7B/unsloth.Q8_0.gguf\n", "Unsloth: Conversion completed! 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