{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "2eSvM9zX_2d3" }, "outputs": [], "source": [ "%%capture\n", "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n", "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", "\n", "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n", "from torch import __version__; from packaging.version import Version as V\n", "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n", "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton" ] }, { "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", "* With [PR 26037](https://github.com/huggingface/transformers/pull/26037), we support downloading 4bit models **4x faster**! [Our repo](https://huggingface.co/unsloth) has Llama, Mistral 4bit models.\n", "* [**NEW**] We make Phi-3 Medium / Mini **2x faster**! See our [Phi-3 Medium notebook](https://colab.research.google.com/drive/1hhdhBa1j_hsymiW9m-WzxQtgqTH_NHqi?usp=sharing)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 331, "referenced_widgets": [ "678374910a65405a8d0fad306fccc934", "70cb67b1a6f64b81a1c15231f181c7f5", "2ba8f80a90c1448ca39e1976cd93c351", "f058256b9892475caf2d74f1605c4ce2", "54def8f425f54876a5bde6f66d63629d", "365a03957dc441108fc176c5a614f31c", "e3a04b4a2b074779af4815841cecca39", "b9e6e7a9f19042b281e56781a5d22830", "e5eb3cb7ca72444c90ec0e72226b04ff", "8419a72c6f3645368cb97f7f2ed6cea1", "bf4b87f750434c519c27ecc65e1ee09d", "7edfaa85ba904a8a9b52dac4b008d27a", "d7586f8cd53340fab8476e5ff48e3362", "0dffec13261e4bf7aeeaae63826a233f", "d9086669a7234a5f850d3f5be84ee676", "05a1c7419b5549fbaf77f3adcde12bf4", "c5c9ec51972845ef948fef17255ce888", "40ec0224839647ca845472b43d015372", "6b915e7093d5479d8a84d6c5504abecf", "810a8dd6fe6d4d74b692cbe1c7ceda12", "c0af25b8612243f4bade23465eacdc4b", "05d3c577cff248ac9ff936e0ba469957", "03c771a0fd7f4f78b6c4428fe53eb928", "7f41f7d6008d4d1d88f773b05a3657b4", "3e8df03a267b4a88919eff5325d1bb65", "8ea3e08688c84d6ab8fdf1dfc903738e", "c881789cdabd4bcd8251d3250e4abe3a", "778a01b5778c4487a3187181c1c575e7", "c130b8a942364e6fa99c4f641e9bfc67", "36739a0f98294195836d5475809c3743", "e96cd5ee23b649d18de99fe8e835b837", "401e41bbc13f42a08391c8c0a6d90076", "6f2ed6b670aa43559e50b6411cfa6a13", "0a906be814484ff1950ba52e76f69da3", "fb33ed2a86094149b4dbf55a99dfc5c1", "734ee1f019db4443a02f50fe227b9e3f", "387ed2dd78a94293adeb79a0ccad0e6a", "94cc02dd099847628d666cca89f9319a", "e6d78e15a0c04baa8afb8c7a3d18fad7", "a3a7d13efc0a4c739f8d751ab5e7dcd4", "269083dd443e4802a6f12f743866f4da", "6ebe214f4ec4442a9829684aeefceb00", "342e822e575b437ebd6f8d17efcc497f", "f477ebc74b394daa898963c28312936b", "07791a6e30574cbe92283ddea21bd944", "2e0adfd1a6f74835aef62b270ac2e9aa", "daf1537134e64d478969812869f3d29d", "c32502463f07437885d3a51f31409e44", "2ef9d56652004e63baea4673522f6c89", "1fc6430cb4fd4d1ebcc6f7b81feea3ec", "6ea1b90667764395a122a9fd76e2bc63", "d3c828f5d2984860a8a24adb5d4f1166", "fb19e29935114144861a8f1462157a05", "673986359dc9427a98a4fb7a688ac887", "2fde72d7018f49319c22b84d37a327f4", "3640fd0dabf24eaba9154f8442463d1a", "d96134b2c4124330b934e08f9af4c824", "0836e47500b54b27b5fc4f8b39835842", "c8219032f7ed4d63a50ebcd37e0b39f0", "461419716e4e4b2caf926516f24cdc86", "8b3f4f9f92e14a558b3d4f0cab126bea", "b413d96cf0a044a2b01f01d4947f3a31", "3e06fb4c42c0403da3b440121a475618", "f2f7348548a64198a94c6b59374008fa", "497d2efd3c8b4bb88e0015bdc1dfe46e", "139198796298496db36f3577d390d7e8" ] }, "id": "QmUBVEnvCDJv", "outputId": "27084327-f4c5-4d6c-86c1-1358186a511c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.8: Fast Gemma2 patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.4.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.27.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": "678374910a65405a8d0fad306fccc934", "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 `llama-3` template for ShareGPT datasets, try our conversational [notebook](https://colab.research.google.com/drive/1XamvWYinY6FOSX9GLvnqSjjsNflxdhNc?usp=sharing).\n", "\n", "For text completions like novel writing, try this [notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 177, "referenced_widgets": [ "8e3e3f1f39df42ec821d1b928564da09", "0358a94fbe204627823216b048bf68a5", "0e94c4a67ef7485fb5f56798415e0d3d", "827a99f8939d4f98be38c3b4fa6610ef", "6f0ee04758e249ad8a809f6587bfddb8", "b632a7d91c394689abb6e5e7904c0181", "2663539cda94465ca2f1e778b93e7ced", "83aa33671151496c82ca8b51318d3b13", "2410fda0ccaa4724b07e0d831ddd1e0a", "5e5b630f628845f496fe25dcac7ab1af", "c379c5ed71454632a6aee8dc13439507", "8fd25d3a45fc4238a5b6a1349ded3d3a", "4cdb9a601cbe4cbb8532453713d92c5f", "e76baf1c15864c0ba7a915b2fb466011", "6b18c64fac6145aea1ede78aa0ae5ad2", "b1b4fb6e26cb4123ba0c2503e5e97138", "881176dcdf6341169b72473cadd78c2a", "0a936e7ccf1e42278e7bffacf77835a8", "f6d293c0a1b34d4eb0ae830ca1e095a8", "5ca5e52ef3c84415a8beb785767c0cc3", "6944241baee64180aaa73a0764f15d25", "6b5fef8e89da41ffac0a1a566be5d6c7", "c611a12429b142f9be9d205f5ecbba8c", "51e71bead0e24c53b89c2269fc885e2f", "c6068bf2a9384f59968f9b5a733989d1", "850851391a094dffa489dbb326297f82", "9fa63e5129f04a579035eba7599fa87b", "5be7433df7c2482ba79eee336d0ea303", "6808e2d30a634a00a92a58260ea3a837", "f86517d5f95c45969a1b443d35d32876", "fe9d4e6150794e55b20e07cd97aacbb4", "ae8b8478245242f58ac23640fd078052", "6e2f5febc19d4cd8bab9351a8c7a4d54", "b832c981fa8c4e98b377aa32f16742f5", "5a01274f4adf4c0994ae8966445ea1af", "baabf5a5422a4046873853bac7aedc81", "fb05d0eb082e45e6b634595a484fd750", "8ceb560415db41498c557e1115e5f50b", "8c98c1ec39e142a6bcfc14cc1e21e0cd", "196a70176d2149959f79916994ff63e7", "e7af7ea26840451c8b69767a3c90e0cd", "0d0fc89c1cdc4977a251d49e8ea8a578", "6eb712bcfe4844fea213bddef56bbf73", "0317cd59f03e46b4a6c0e903428a4b99", "ea9b0e0d120e41d28ae40d15fa41c2be", "c68ea312f5cf496796550d47e41769dc", "ebbcbf688e594ec8beb672c71c372e6b", "8e08b3b48754473499f6cc42c5107856", "1bafa0b2762d4494b3b2ece57ed20447", "1ef0645d0c8749369f82ba23da1c74fa", "6823b8cd31a449d29c8f5781b95b0597", "ba72d0797dba49a7b50f6c499d8db39d", "6e661a4b9fa3477cbf2acfe8a649aea9", "a04dc7854ed54837be388ee661759131", "4b0dded7e7b444339ca7d69939fb9bea" ] }, "id": "LjY75GoYUCB8", "outputId": "7da24b8a-80b7-44f7-eafa-7964295db683" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "8e3e3f1f39df42ec821d1b928564da09", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Downloading readme: 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": [ "a112ce9cda9844978d756cf2c0992c4f", "3a263ba2e5c746008906215c6b9a9e77", "6d50cb4bab66462a8a63a5fe39607e3a", "30cc22cc8b1e4d1d8e6b5d3255257cca", "b001536f1c3046aab8d82bfaaeda77e9", "fa5b1da560114424be8322096e4962f2", "c2367cfce08a40e2ae924502a259affa", "bf3d34b1175b4eb08151a88051326994", "0354c2a3792e41188f4ee5df779273a3", "e449f5ddb6264049b0c19c23c689847d", "9e3b03487e34439aba797e2c5bb2c768" ] }, "id": "95_Nn-89DhsL", "outputId": "c4932606-6f39-431a-df86-6b9696552d4c" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a112ce9cda9844978d756cf2c0992c4f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Map (num_proc=2): 0%| | 0/172026 [00:00\n", " \n", " \n", " [160/160 1:05:37, Epoch 0/1]\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " 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StepTraining Loss
11.305600
21.398000
31.628300
41.467200
51.355900
61.228100
71.216800
81.187800
91.186500
100.937900
111.217700
120.845000
131.157800
141.055500
151.130600
160.845500
171.042000
181.155200
190.957200
200.869600
210.906400
220.936200
231.197900
240.865900
251.002400
260.984800
270.722000
280.958100
291.146100
300.927100
310.751400
320.900500
331.009300
341.208600
351.342200
360.991500
370.985200
380.932700
390.927500
400.563600
411.149100
421.057900
430.627900
440.741800
450.959800
460.629700
470.702000
480.900500
490.974500
501.075500
511.070200
520.974300
530.981700
540.940200
551.078200
561.051200
570.968800
580.613100
590.976200
601.094600
610.859800
620.871000
630.638400
640.889400
651.186200
660.889500
671.040800
680.751800
691.011200
700.772000
710.820700
720.567300
731.165600
740.999100
751.157300
760.675600
770.923300
780.834000
791.128300
801.036600
810.800700
821.117500
830.665600
840.979200
850.681100
861.115600
870.807500
880.928100
890.883100
900.797900
910.940200
921.062500
930.770600
941.002400
950.971400
961.028100
970.908500
981.131300
990.845000
1001.052600
1010.897500
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" ], "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": "f1be108f-fc9b-4e9c-a1d3-0a536e38162e" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4041.3928 seconds used for training.\n", "67.36 minutes used for training.\n", "Peak reserved memory = 13.279 GB.\n", "Peak reserved memory for training = 6.703 GB.\n", "Peak reserved memory % of max memory = 90.039 %.\n", "Peak reserved memory for training % of max memory = 45.45 %.\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!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "bb8c37ed-4e81-471f-9b93-f92e7e4912b2" }, "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:\\nআমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\\n\\n### Input:\\n১, ২, ৩, ৪, ৫\\n\\n### Response:\\nআমি 3 বেছে নেব।']" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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": "af0a8577-2bd5-44fa-d239-c278faefd341" }, "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", "আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\n", "\n", "### Input:\n", "১, ২, ৩, ৪, ৫\n", "\n", "### Response:\n", "আমি 3 বেছে নেব।\n" ] } ], "source": [ "# alpaca_prompt = Copied from above\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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": { "colab": { "base_uri": "https://localhost:8080/", "height": 226, "referenced_widgets": [ "a1c2128e6ef14ac0b61b35e7980641a7", "54339062d36e4d80b371ff41f86467af", "1834ff069a3040de9b19c6a4a4d91985", "bf6dd62524a7460c839542a10f784285", "4b985d71566c488fb2dd521b8f523344", "6053e141396d4eee9f8e21b3c5ce09cc", "91393d1bcf894a24a92e1e85ca4f517e", "9ec43604a26541a39e902c5ae1c3cdcf", "70949e3f60f84470bfa4f8dc8894f3bd", "bbe15269fbd14211b92b799249b6788b", "fb69b3f26d434763ac351c39a71ffe5e", "a6ff34a5a4394edc8aad4c6ff8cfbac3", "47364618be5e4ae1b956cd91a7b86ab8", "435576166c754b2d94bf85a5a50811ae", "9e2f94a5d5f44851920996efe02a4d4d", "ce64c8a4b2434522a115f314e90691f6", "eaaae37df5f040fc91981e809508e508", "c11a6b367bef4bb0a6cfcb20fe48631c", "bf2dbf0d8677485594fabe658edb34b0", "1a7afa1ee09346bb9da6fcc5cd2ef2ba", "3bee40896a254e6991d52e2f0114d351", "5b405bc09df445989c7225f84dea2e82", "e0d7ffe6bb7c4d968443d3f53afb76bc", "6cd8c3972ce84f3791cc854da946880a", "264308ba7a2043b8b17b556accd1441f", "ece27053c5ae4148944f0b957387a23a", "ebdf3fa4b1344280ada654dae39cf572", "cb689aa381da400ca940c0a683da5dce", "949a6c6d34354dca9727ea4b867e8365", "9228ca19ad044b799dfde414dff1c68f", "8493fd67d27d40f6966a4250d76f8a27", "9d2a7c7f29e548c389e3616f4ba5c31f", "50cfa223ed1a4c4888ead751f33536af", "009a86168e014bccaeb098b31b78d07d", "63a82254ad1b4a7b85e7322ee73985b9", "94ea1100a33a492c970de4c2a922849c", "b2fb4aee35c5491ba839fefae8e6af14", "146995c7c69a4125a9de9016dbcd5c65", "f4ca2874d32f47cdba460d7734f8a59c", "bb6f737c6c6d48bda5003896dd9e66c4", "8848057633f2430b9df3741b8b165d95", "7eedff5072c74c9b935b6f6cd058191c", "d99124180c924d85a42ab2ffd30e94b6", "f1aba35909b8478bb08e05008b55ddb0", "317a612240f6476eacad142c36d332cc", "17d654521bfb4012b834160163b6ec25", "d4691a00135f4d099c4bf2c349f7110a", "c3ff8fcb25394429b8927951de0de1df", "24245ce56f6e4edab1ac592af69298f4", "cec76bc8e1eb44ef8c94144a4b14848a", "92124fa18dcd47d59018ebe29a83aec2", "76b707295ed24de4b57587cbdcbdfe6a", "c55f4df042a84a60baa321a9869dd296", "1dbd41bead2545b18fd0d64b4878b218", "df9f755f3c7d47fd847c43f45567121e", "04bf66c2889a45cea510907468b5e4f6", "c74b7612b8934b5ca58eae7cce552dec", "f6a1fcc3c8d74cba9bf066fb77792546", "b852fdd7e26147cd9f734a7d2bb15d05", "0d9a73b68d3f4f9e9177383bba2a7a31", "21cd7cfffaa9463ba4eee152f88ad011", "462efcc5e8b44cd3a76b491e215f091c", "de004e7fba8345d796e4f56f3349d60d", "22ac5071416f4c40aea83148299d8cd9", "add813d665d2472ea1d9b4e4bd63afa8", "33a5c1e4041f4263ade5fd63ac57655b" ] }, "id": "upcOlWe7A1vc", "outputId": "70261d19-c189-4ac9-8060-7a42734f7ef4" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "a1c2128e6ef14ac0b61b35e7980641a7", "version_major": 2, "version_minor": 0 }, "text/plain": [ "README.md: 0%| | 0.00/576 [00:00\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mif\u001b[0m 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load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, trust_remote_code, use_gradient_checkpointing, resize_model_vocab, revision, *args, **kwargs)\u001b[0m\n\u001b[1;32m 299\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 300\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 301\u001b[0;31m model, tokenizer = dispatch_model.from_pretrained(\n\u001b[0m\u001b[1;32m 302\u001b[0m \u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 303\u001b[0m \u001b[0mmax_seq_length\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax_seq_length\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/unsloth/models/llama.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(model_name, max_seq_length, dtype, load_in_4bit, token, device_map, rope_scaling, fix_tokenizer, model_patcher, tokenizer_name, trust_remote_code, **kwargs)\u001b[0m\n\u001b[1;32m 1578\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mload_in_4bit\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"quantization_config\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbnb_config\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1580\u001b[0;31m model = AutoModelForCausalLM.from_pretrained(\n\u001b[0m\u001b[1;32m 1581\u001b[0m \u001b[0mmodel_name\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1582\u001b[0m \u001b[0mdevice_map\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdevice_map\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/auto/auto_factory.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[0m\n\u001b[1;32m 562\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 563\u001b[0m \u001b[0mmodel_class\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_model_class\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_model_mapping\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 564\u001b[0;31m return model_class.from_pretrained(\n\u001b[0m\u001b[1;32m 565\u001b[0m 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Make sure you have enough GPU RAM to fit the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 88\u001b[0m \u001b[0;34m\"quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit the quantized model. If you want to dispatch the model on the CPU or the disk while keeping these modules in 32-bit, you need to set `load_in_8bit_fp32_cpu_offload=True` and pass a custom `device_map` to `from_pretrained`. Check https://huggingface.co/docs/transformers/main/en/main_classes/quantization#offload-between-cpu-and-gpu for more details. " ] } ], "source": [ "if True:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = \"BanglaGemma9b\", # 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) # Unsloth has 2x faster inference!\n", "\n", "# alpaca_prompt = You MUST copy from above!\n", "FastLanguageModel.for_inference(model) # Unsloth has 2x faster inference!\n", "inputs = tokenizer(\n", "[\n", " alpaca_prompt.format(\n", " \"আমি জানি না তুমি কি চাও, তবে নিচে থেকে একটি বিজোড় নম্বর বেছে নেও\", # instruction\n", " \"১, ২, ৩, ৪, ৫\", # 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": "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\", # YOUR MODEL YOU USED FOR TRAINING\n", " load_in_4bit = load_in_4bit,\n", " )\n", " tokenizer = AutoTokenizer.from_pretrained(\"BanglaGemma9b\")" ] }, { "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(\"hf/model\", tokenizer, save_method = \"merged_16bit\", token = \"\")\n", "\n", "# Merge to 4bit\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"merged_4bit\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"merged_4bit\", token = \"\")\n", "\n", "# Just LoRA adapters\n", "if False: model.save_pretrained_merged(\"model\", tokenizer, save_method = \"lora\",)\n", "if False: model.push_to_hub_merged(\"hf/model\", tokenizer, save_method = \"lora\", token = \"\")" ] }, { "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." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "FqfebeAdT073" }, "outputs": [], "source": [ "# Save to 8bit Q8_0\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer,)\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, token = \"\")\n", "\n", "# Save to 16bit GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"f16\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"f16\", token = \"\")\n", "\n", "# Save to q4_k_m GGUF\n", "if False: model.save_pretrained_gguf(\"model\", tokenizer, quantization_method = \"q4_k_m\")\n", "if False: model.push_to_hub_gguf(\"hf/model\", tokenizer, quantization_method = \"q4_k_m\", token = \"\")" ] }, { "cell_type": "markdown", "metadata": { "id": "bDp0zNpwe6U_" }, "source": [ "Now, use the `model-unsloth.gguf` file or `model-unsloth-Q4_K_M.gguf` file in `llama.cpp` or a UI based system like `GPT4All`. You can install GPT4All by going [here](https://gpt4all.io/index.html)." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BUge_oKXybms", "outputId": "51651cd1-9872-4a2f-c40f-e4f6bf693c1c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Collecting git+https://github.com/unslothai/unsloth.git\n", " Cloning https://github.com/unslothai/unsloth.git to /tmp/pip-req-build-om550pjs\n", " Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-req-build-om550pjs\n", " Resolved https://github.com/unslothai/unsloth.git to commit d91d40a7b6b556f2d1fdd3e1e430f7a76a799627\n", " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "Collecting xformers<0.0.27\n", " Downloading 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/tmp/pip-ephem-wheel-cache-8ukgq8s_/wheels/ed/d4/e9/76fb290ee3df0a5fc21ce5c2c788e29e9607a2353d8342fd0d\n", "Successfully built unsloth\n", "Installing collected packages: sentencepiece, xxhash, unsloth, triton, shtab, pyarrow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, hf-transfer, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, tyro, nvidia-cusolver-cu12, torch, datasets, xformers, bitsandbytes, trl, peft\n", " Attempting uninstall: sentencepiece\n", " Found existing installation: sentencepiece 0.1.99\n", " Uninstalling sentencepiece-0.1.99:\n", " Successfully uninstalled sentencepiece-0.1.99\n", " Attempting uninstall: pyarrow\n", " Found existing installation: pyarrow 14.0.2\n", " Uninstalling pyarrow-14.0.2:\n", " Successfully uninstalled pyarrow-14.0.2\n", " Attempting uninstall: nvidia-nccl-cu12\n", " Found existing installation: nvidia-nccl-cu12 2.22.3\n", " Uninstalling nvidia-nccl-cu12-2.22.3:\n", " Successfully uninstalled nvidia-nccl-cu12-2.22.3\n", " Attempting uninstall: torch\n", " Found existing installation: torch 2.4.0+cu121\n", " Uninstalling torch-2.4.0+cu121:\n", " Successfully uninstalled torch-2.4.0+cu121\n", "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n", "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\n", "torchaudio 2.4.0+cu121 requires torch==2.4.0, but you have torch 2.3.0 which is incompatible.\n", "torchvision 0.19.0+cu121 requires torch==2.4.0, but you have torch 2.3.0 which is incompatible.\u001b[0m\u001b[31m\n", "\u001b[0mSuccessfully installed bitsandbytes-0.43.3 datasets-2.21.0 dill-0.3.8 hf-transfer-0.1.8 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.68 nvidia-nvtx-cu12-12.1.105 peft-0.12.0 pyarrow-17.0.0 sentencepiece-0.2.0 shtab-1.7.1 torch-2.3.0 triton-2.3.0 trl-0.10.1 tyro-0.8.10 unsloth-2024.8 xformers-0.0.26.post1 xxhash-3.5.0\n" ] } ], "source": [ "!pip install git+https://github.com/unslothai/unsloth.git \"unsloth[colab-new]\" \"xformers<0.0.27\" trl peft accelerate bitsandbytes\n" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "c343e4101ce8421d92ff36d9523b8390", "38deee1e766b4f3c9eaf039e3e9a17af", "78e97d6b933f4799916a3eaca807f5c3", "4fbae776caf542e499ec8e172226ef1f", "3b66373e6c88452b81ba16b3c6487c40", "c2d2eb8bbb9647028753dc547b84c0c5", "8e48c8347c214b4cac56d765c38e14c3", "e61cfc6a5ae94a928d86dca6503f4c00", "ce598d630ad744c2aa29ba3b286abac7", "9e2e67dc137842ed90e3897d500471f5", "53a3e03d29a94b2e8678be4e5915cb5c", "31f078386b9349b1a0e6c0e0db009256", "da66c4e5df5c4549b6ee4024863265c8", "3092d50379314bbbb415883e01ff0fe6", "8fa60834592e4694bcac6f43a8997fb2", "01477e58708d45ea9ae7ea6d09bd7bfc", "5671247440874e4898d18ced1e7e55f0", "debfb6fa808447a097711a92a523f010", "bf66b6b6719549c78287e6be9a67e879", "e7be927cb49842b8811f606c72690a24", "8499a2e2442b4bc3828b851e18d84f7b", "a9d501482635407b8be1ab3f6856bcfc", "bd20d6424f0b44509f60925db05c95c9", "7ccaa06a70b04f33a6690a94764163fb", "83fc0e5b6bc047ddb19f72f40103f51a", "f5456ae444bc45e3a64211240a8d40bc", "e75752994b9e49d492ee6334c3200841", "6b39c42837a247d19ab6598cba81eadb", "aa5daba1696146fb93e21ea6fee9df42", "f6bbee4d643349b780fca75b40f533b0", "edc4ad30131d4d07ab904b316192763a", "0e11d519835b4b9daab711fd1b2cc813", "e19a30bc3ec84f4a99326d5009ae1298", "23c315e5f5f642c88b7cc2283f0e423e", "cbe55f159aed4dce9b9462456a3573f0", "d38c8ecae0f244409b588fb361792c17", "bb10a4ddb3de46dcb0d9bdf80f430b10", "00a8feaea88f4cf5b05e787c9a942630", "ebd7fde0b37a4db09880e7dabb2620fc", "faf1855274b144dd9f9145d6f897c54c", "899a20a2c271471897ea307117a687c5", "8022d2b23d7f4ce5802aac34015f62f1", "505ec358b1fe4cc5a8f2553a89b55d61", "81effc584b1c446889ac980dfdeea56c", "4724f78404e4494f92dbb7753f9eb1bf", "f879d4d072a343df8a376fba8a42ebbc", "47693f49ad7a4f568f3fa74cc2585d0b", "7666e13b055d410c9592bf7c19bed2f4", "f9f3ef37cb96422794e4ebf54ee64eed", "70f20e74432c40ceb9bd8630fd4a9234", "b1454d7d521e4b3a8d8d66f444159bbb", "6df9d0106128409a88a3fd58e55d4530", "e1641fb50f4d4b03b9d9df441254534b", "89719520c1214b119783ea20403132cd", "4321dd2435fd42f885095680a18f96a6", "d41c91128ca74c1f9fd57ddf1bbba197", "3a73f5eb3c6b46cc8f3d049d78573eb8", "ed0f1ea15ba74ec3bc602f239501f48d", "040a1337765844619a68e555a38937b0", "76fbd9545c3643bea1134f956bbbea37", "31dee0aa078e41038c407769c46ae3a7", "d62c4226c0fa4b6b8f282d175bb83ba3", "1312c65ff7df4d3493fc1126d12d7684", "c3116b0615de446c9c05cf44c4cbac7f", "af8ff56d53034b0190d8bcbd7da2a077", "f512778ea1ed4399a9d84da98f484e3e", "92b0041f180d4028be71556c598d7028", "ef4394cb55bc41cfa527d7e22d38ad7c", "36d386639b0442428b001f7865bc4e03", "510752f03d2d485fbff81664ceed869d", "fff1a66195284929823392fb9f0dbf98", "898f2c75fb814733bba261d1fe46f7a5", "5de2d659981b4269ba09e8abf59eb538", "0010ff3ed1d340cfb1be59ce88c81b7c", "c4ea4851359946d5933057091d5dc274", "dacec78dce27458fa6c1b93881354851", "c5f6a3afb92b448e90cb7e1cdbc99838" ] }, "collapsed": true, "id": "PDTgImXTygdd", "outputId": "5ceddb04-c32d-434f-86f9-678601cf69bd" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", "==((====))== Unsloth 2024.8: Fast Gemma2 patching. Transformers = 4.44.2.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", "O^O/ \\_/ \\ Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.26.post1. 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": "c343e4101ce8421d92ff36d9523b8390", "version_major": 2, "version_minor": 0 }, "text/plain": [ "model.safetensors: 0%| | 0.00/6.13G [00:00Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n### Instruction:\\nStart a casual conversation.\\n### Input:\\nHello, how are you today?\\n### Response:\\nআমি ভালোই আছি, ধন্যবাদ! আপনি কি ভালো আছেন?']\n" ] } ], "source": [ "from unsloth import FastLanguageModel\n", "from transformers import AutoTokenizer\n", "\n", "# Load the model and tokenizer\n", "model_name = \"vaugheu/BanglaGemma9b\"\n", "model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# Set up for inference\n", "FastLanguageModel.for_inference(model)\n", "\n", "# Define the prompt and input\n", "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "### Instruction:\n", "{}\n", "### Input:\n", "{}\n", "### Response:\n", "{}\"\"\"\n", "\n", "# Specify the chat prompt and input\n", "chat_instruction = \"Start a casual conversation.\"\n", "user_input = \"Hello, how are you today?\"\n", "\n", "# Format the prompt and input\n", "chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", "chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", "# Generate the chat response\n", "chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", "chat_response = tokenizer.batch_decode(chat_output)\n", "\n", "# Display the chat response\n", "print(chat_response)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "YW_yXAYDypjW", "outputId": "00a291e9-5cd5-4f86-c3ab-c0b6b4ed4849" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Input: hello how are you\n", "আমি ভালোই আছি, আপনি কি?\n", "Input: do you want to eat?\n", "আমি কি খাওয়া চাই?\n", "Input: yes\n", "আমি কিছুটা ক্লান্ত হয়েছি, আপনি কি?\n", "Input: no\n", "একটি কথোপকথন শুরু করার জন্য, আপনি আপনার দিনের একটি উত্তেজনাপূর্ণ ঘটনা সম্পর্কে কথা বলতে পারেন বা আপনার দিনের একটি মজাদার ঘটনা সম\n", "Input: i want to talk about politics\n", "আমিও রাজনীতি সম্পর্কে কথা বলতে চাই, কিন্তু আমি জানি এটি একটি বিষয় যা অনেক মানুষের মধ্যে বিভেদ তৈরি করে। আমি কিছু অন্য বিষ\n", "Input: should we stand against captalism \n", "এটিকে ঘিরে একটি আলোচনা শুরু করুন:\n", "\n", "ক্যাপিটালিজমের বিরুদ্ধে দাঁড়ানোর বিষয়ে আপনার মতামত কি? আপনি কি মনে করেন যে এটি একটি\n", "Input: what do you know about Bangladesh\n", "Bangladesh is a country located in South Asia, bordered by India to the west, north, and east, and Myanmar to the southeast. It is known for its rich cultural heritage, vibrant festivals, and delicious cuisine. The country is home to over 160 million people and is one of the most densely populated countries\n", "Input: ek plate vat khabo\n", "একটি প্লেটে কি খাওয়া যায়?\n", "Input: ami school e jabona\n", "আমি শিক্ষা প্রতিষ্ঠানে যাচ্ছি।\n", "Input: bye\n", "আমি আশা করি আপনি একটি দিনের শেষে একটি দিনের শেষে একটি দিনের শেষে একটি দিনের শেষে একটি দিনের শেষে একটি দিনের শেষে একটি দিনের শেষ\n" ] } ], "source": [ "# from unsloth import FastLanguageModel\n", "# from transformers import AutoTokenizer\n", "\n", "# # Load the model and tokenizer\n", "# model_name = \"vaugheu/BanglaGemma9b\"\n", "# model, tokenizer = FastLanguageModel.from_pretrained(model_name)\n", "\n", "# # Set up for inference\n", "# FastLanguageModel.for_inference(model)\n", "\n", "# # Define the prompt and input\n", "# alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", "# ### Instruction:\n", "# {}\n", "# ### Input:\n", "# {}\n", "# ### Response:\n", "# {}\"\"\"\n", "\n", "# Start the chat loop\n", "bye = 0\n", "while bye == 0:\n", " # Get user input for instruction and input\n", " # chat_instruction = input(\"Instruction: \")\n", " chat_instruction = \"Start a casual conversation.\"\n", " user_input = input(\"Input: \")\n", "\n", " # Format the prompt and input\n", " chat_text = alpaca_prompt.format(chat_instruction, user_input, \"\")\n", " chat_tokens = tokenizer([chat_text], return_tensors=\"pt\").to(\"cuda\")\n", "\n", " # Generate the chat response\n", " chat_output = model.generate(**chat_tokens, max_new_tokens=64, use_cache=True)\n", " chat_response = tokenizer.batch_decode(chat_output)\n", "\n", " # Display the chat response\n", " # print(\"Response:\", chat_response)\n", "\n", " # Since chat_response is a list, extract the first item and then find the response part\n", " 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