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\"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n", + "if major_version >= 8:\n", + " # Use this for new GPUs like Ampere, Hopper GPUs (RTX 30xx, RTX 40xx, A100, H100, L40)\n", + " !pip install --no-deps packaging ninja einops flash-attn xformers trl peft accelerate bitsandbytes\n", + "else:\n", + " # Use this for older GPUs (V100, Tesla T4, RTX 20xx)\n", + " !pip install --no-deps xformers trl peft accelerate bitsandbytes\n", + "pass" + ], + "metadata": { + "id": "eiR1YOEdqPlq" + }, + "execution_count": 1, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from unsloth import FastLanguageModel\n", + "import torch\n", + "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n", + "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n", + "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n", + "\n", + "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n", + "fourbit_models = [\n", + " \"unsloth/mistral-7b-bnb-4bit\",\n", + " \"unsloth/mistral-7b-instruct-v0.2-bnb-4bit\",\n", + " \"unsloth/llama-2-7b-bnb-4bit\",\n", + " \"unsloth/gemma-7b-bnb-4bit\",\n", + " \"unsloth/gemma-7b-it-bnb-4bit\", # Instruct version of Gemma 7b\n", + " \"unsloth/gemma-2b-bnb-4bit\",\n", + " \"unsloth/gemma-2b-it-bnb-4bit\", # Instruct version of Gemma 2b\n", + " \"unsloth/llama-3-8b-bnb-4bit\", # [NEW] 15 Trillion token Llama-3\n", + "] # More models at https://huggingface.co/unsloth\n", + "\n", + "model, tokenizer = FastLanguageModel.from_pretrained(\n", + " model_name = \"unsloth/llama-3-8b-bnb-4bit\",\n", + " max_seq_length = max_seq_length,\n", + " dtype = dtype,\n", + " load_in_4bit = load_in_4bit,\n", + " # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 350, + "referenced_widgets": [ + "6ea331cd2e62434c9aaecd76db553d99", + "2ed600e5e1bc464ba8f208eaa50e734b", + "a1bd203747144a0fb94907d656e0d1f5", + "288023bd54fa4e4d9fb932b9b3459023", + "1570abeef4534b2a81558a4bdf545a7e", + "6a9386666d834f588cd86750e645e50c", + "f43a27ba718a46f1b49e8ba793982061", + "820995f7f9b34eca81ea82c09acf8668", + "3d1f7660bb874fada72925e043bf0031", + "d718f946b8a14c638111f358d833107a", + "f7ee8e4304244835858d01e0a708e48c", + "1a138f38ebb54dcb8f2f0eb1d9df947c", + "d5739da7d9f246b9a082af78ecd6073f", + "2d898145922244efb409392399ba2fdc", + "02e1892619d849c0ae49faa7714b9c19", + "512cfe0b870a45c2b8eb5c41a0a6eaa3", + "17c430bd03764cefa42b1d04f3c362e1", + "0fff066fd58f44fd9029c0222a3122c6", + "e06c26fa2b4c483f8cfb6acc8d20dfbe", + "0a614688cb994c6183b8b1bb9571f0bb", + "77c4d669a4994c219f57621d64ffc6c8", + "bf8e82f9fcd1435e870a3e0a5c1ec504", + "cb4f29d09826430fb21e2d42509bbc29", + "ef2f14e229aa4186acb47ec5ff8dc83d", + "1b94fccd4f9a4c76840a486167ee72b8", + "465fe2ec7f0d451e86f0e685926dab44", + "1113ddda0c214c97b0ad2628dc1b9fd9", + "f090324ac5e9426285d98d948cc75373", + "3ccd3cd5740b4881a0e294d1e64e5e55", + "a0b82b6b11a140a6be9654239f7211c5", + "2101428e33f041a188fe8b02432cd4f2", + "a4a7cd7517324f6ba778fd9be52f9986", + "441fb5cd0ee34541a1b88b1c93ed44e6", + "679a137849a44a9eabf2d267cb887a4b", + "28e92a092ab54df9a412e37d179f95f5", + "31a0fef3b6fd4c8bb596ceaaaa63d32b", + "bc44abb9e23d4b59b207f510c8ddc9af", + "f379bad0a96243dba2a30a5881eb2fd2", + "f0c5f25a957a42fd8769d74ca35cb820", + "82d7755c30b74116917d385bac2b47ab", + "e3e3f44055ae424db09914dd8cb3f473", + "3cd603e325a349b2a223c2707783eed1", + "510b0763bb4f4219aa8b3f5998811bac", + "63be0b7fe39844e0a15a113d8e4b831b", + "d6c40a3a675143659294729c30e62f74", + "0c2d9d2b5f8a42b1abf59c7d645e3686", + "989dc5928e4a4c33a4a5216e449edc78", + "615fb166d86d4acba35c0914f1aa23b1", + "abc40afd4f684d168b86b3a0521159ce", + "7187aa7250f9489dbc7b3f289e0ecc50", + "6ad167a09c2640f5aac6c4ce8af9e22f", + "60e74d12620e44ef91697a816a3b561c", + "1e92ce9c3e73427d9d1942279a912d9f", + "0c9fd5484e0d4056a804425d2a2a9171", + "13de38dd3b9f430d8994e77c78cda4a3", + "2a3c0ec025ff474ca913ffb4947c3800", + "5098b668638b409b9c52dd15bc688781", + "e76cf1d803834d8086b9e115d891ffcd", + "d9e7b0bbd2db40618a0248212ddd2fbc", + "799e6f4826844b0f9af4c5f2b93783ec", + "85a3bd27f4144d49bf38b6343dfff473", + "f24b889675f74be598e77fe9ca2d900e", + "1d01b89293954f40a41033d0d16c2721", + "25fad6781da8481dbe89bb67c1722f39", + "54cee5b8a02a4adf9cb54ee4e6de47f7", + "d7d6e29263644c04aee7ae239bc48720" + ] + }, + "id": "0QN_um_2qPjp", + "outputId": "7a0f7527-d31a-4a66-8b11-baf6456fdb14" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/1.14k [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", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "9iv6ey7NqPhJ", + "outputId": "6d335090-7ff4-4d8a-c6bd-81180f592e7a" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Unsloth 2024.4 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "{}\n", + "\n", + "### Input:\n", + "{}\n", + "\n", + "### Response:\n", + "{}\"\"\"\n", + "\n", + "EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN\n", + "def formatting_prompts_func(examples):\n", + " instructions = examples[\"instruction\"]\n", + " inputs = examples[\"input\"]\n", + " outputs = examples[\"output\"]\n", + " texts = []\n", + " for instruction, input, output in zip(instructions, inputs, outputs):\n", + " # Must add EOS_TOKEN, otherwise your generation will go on forever!\n", + " text = alpaca_prompt.format(instruction, input, output) + EOS_TOKEN\n", + " texts.append(text)\n", + " return { \"text\" : texts, }\n", + "pass\n", + "\n", + "from datasets import load_dataset\n", + "datasets.load_dataset( 'math_dataset/arithmetic__mul', split=['train', 'test'], as_supervised=True)\n", + "dataset = dataset.map(formatting_prompts_func, batched = True,)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "e72e7a727e99478093269223ce2672d8", + "e2c600d9b33142e2ab59edfd53472b16", + "ca037700dc3f4d47a3c742c00f1f9018", + "c7bed15a38254301a8d1766b29e01ec6", + "4d856765082f455cbae4a8d5942ad22d", + "ca0ea8d51dbb4bd2a6428661b22d618e", + "792990151d9e40fb84d0ff84b6b31195", + "818bcac17c404783af7279ced61f88ff", + "a003047daead4d1c8b599339e48f8213", + "ecb6636bdd3b421a832ba4aec644384e", + "b4ce331935184d4d928a8dd3191ec731", + "0981b0a72a054417aea49a5eae131aaa", + "adadfab658de4eaf9834b123180c2bd5", + "5813f5c0b26744108b74c36f25f8ba69", + "7aca13c230174f1a9aa3b06799920116", + "a7be51fe5a774cc787f82a22e4eb6099", + "83e3abf8af5f45f8a4d84ef8deca7d6b", + "91934571b1a649378ba95e42818360bb", + "b6d52dff55c94fda86f38fa796dd08e8", + "e8fdeb7e4cc44f1ea90d511156a77443", + "f1dee5f80b6a44228c0c9e7fe8268412", + "e0e7ecff9501444aac7f3967ce7666d0", + "787f95b3b15d4ab0a7fa0d99d543e03a", + "ef09e3ae3f9a49e082f5660ec43d529f", + "5dc0660a5cf64573b42200375036d723", + "532bdb49cf474734a5b23d0166cb9d74", + "34cea3e5ad4b4bcc9eb3bb323620afbc", + "1b3fd895dbee47bc85208fc91e28eb8d", + "996b50e0c1b44f11a0c7e56c0f52d7ae", + "1bf2d73ff9d041f8a29daf4fa5069d76", + "abfcbd4b6008451384c806ae20cf547d", + "1dab9d07592243328a5330741ab386de", + "feb7d832db0745f683dd8cdad82fa92a", + "3a2e91717b13487baa4b9d08e95434a7", + "2edfa4e9118645c8aeaa605734f37c70", + "f06047b17df04f3e980743b1f6a4dcfd", + "d9a27f148e6a42bc8b16be5a7b399a74", + "08c20281d5094ebf92019af4d9969dec", + "e47c34f4f8574ff5b60e199205f719ea", + "02746f0092424a359ae8e83b94492993", + "3fa4a233da0a4ca9ab0326a5a825d9c7", + "c0ed9d74b2884f4a9e06dfa07e2a2020", + "9b7ed44f9e0943088554ebede4fd6e0c", + "9bafd2b8b9b54515b2e5d874ab118058" + ] + }, + "id": "x1NzQy2rA_at", + "outputId": "17fb9eb9-6ca2-4472-fd10-be377ddfa0a0" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "Downloading readme: 0%| | 0.00/11.6k [00:00" + ], + "text/html": [ + "\n", + "
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StepTraining Loss
11.823800
22.305700
31.692300
41.947700
51.644000
61.606900
71.193300
81.259600
91.109400
101.165600
110.965400
121.003300
130.935300
141.060800
150.909900
160.911000
171.023900
181.285800
191.012700
200.900200
210.948100
221.020000
230.878700
240.995400
251.059700
261.009600
271.042100
280.868600
290.838100
300.903600
310.863300
320.869000
330.989600
340.871400
350.961300
360.863600
370.893200
380.766200
391.087300
401.161900
410.905700
420.990900
430.962200
440.904800
450.921000
461.002200
470.867700
481.220900
490.915000
501.049000
511.025800
520.927400
531.008100
541.169400
550.798900
561.029200
570.887000
580.830200
590.862500
600.905400

" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "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} %.\")" + ], + "metadata": { + "id": "t8sgWPflqPbr", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "91cb1d70-2fba-4381-d8cf-ce7fd744f4ee" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "440.4525 seconds used for training.\n", + "7.34 minutes used for training.\n", + "Peak reserved memory = 8.984 GB.\n", + "Peak reserved memory for training = 3.316 GB.\n", + "Peak reserved memory % of max memory = 60.917 %.\n", + "Peak reserved memory for training % of max memory = 22.484 %.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the fibonnaci sequence.\", # instruction\n", + " \"1, 1, 2, 3, 5, 8\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "outputs = model.generate(**inputs, max_new_tokens = 64, use_cache = True)\n", + "tokenizer.batch_decode(outputs)" + ], + "metadata": { + "id": "R2bt9cqbqPYu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "66ea4543-2839-482e-ac33-ae5820460865" + }, + "execution_count": 14, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\\n\\n### Instruction:\\nContinue the fibonnaci sequence.\\n\\n### Input:\\n1, 1, 2, 3, 5, 8\\n\\n### Response:\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6765, 10946, 17711, 28657, 46368, 75025']" + ] + }, + "metadata": {}, + "execution_count": 14 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " alpaca_prompt.format(\n", + " \"Continue the square sequence.\", # instruction\n", + " \"1, 4, 9, 16, 25\", # input\n", + " \"\", # output - leave this blank for generation!\n", + " )\n", + "], return_tensors = \"pt\").to(\"cuda\")\n", + "\n", + "from transformers import TextStreamer\n", + "text_streamer = TextStreamer(tokenizer)\n", + "_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)" + ], + "metadata": { + "id": "V31sLCXXqPQ1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8b8d76b6-c5fc-41ae-9ee6-31dad0ae85c9" + }, + "execution_count": 20, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n", + "\n", + "### Instruction:\n", + "Continue the square sequence.\n", + "\n", + "### Input:\n", + "1, 4, 9, 16, 25\n", + "\n", + "### Response:\n", + "36, 49, 64, 81, 100<|end_of_text|>\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "###model.push_to_hub(\"dreamthehacker/alpacafinetunedllama3\", token = HF_TOKEN ) # Online saving" + ], + "metadata": { + "id": "EUqicb-bqPEq", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 98, + "referenced_widgets": [ + "7a72fa9ded3e49c1acbc6d60226b323d", + "32f5c82da9574d63aa24c4dd54c2c270", + "74c04aaf28b54e7ba105a0448761d0a6", + "8e9c9a043e5c47138d3a693b62c981fd", + "95bed6a14a3d45059be27a3c72752f90", + "ae2617377c35450ea1ac65e9c4d5eb56", + "c529b0cf465641bbb7db997aa25c77ea", + "a1453ef5ff5d4a62abb94f5a65ac3db2", + "f896e55708504b308baac2ce3e3454c6", + "b8ddb7d6a92644c4ae8e6cad7fa73444", + "7f0859bec9d44d50937bc72aee90bca2", + "2ca1ca6e2b3941dd99f47ee391e3c9ef", + "777f181dc8fb4b0a83b323f1fb47ec90", + "a16363f7443043858b3481bae33678ab", + "4927889f52b84c9fb4b6ba58b537eecc", + "a36bcf46a8054bf2982ecf4fb1d9ffcc", + "d06952295d6f4d37a0b20f4a5bcda78e", + "fc99359a9c2c4d548a543468d96035a3", + "3f93010dfa5c4008ba98f9c36a10d704", + "ca0d9b3ea6684841a5e800cfe19bf259", + "ddd53f34b73f4ee3a7e04f1a0cf2f591", + "428ddee408ee435290521fceefe27367" + ] + }, + "outputId": "f1918e53-4f50-4be5-95be-1288424e51d6" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "README.md: 0%| | 0.00/581 [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\\n### Instruction:\\nWhat is a famous tall tower in Canada?\\n\\n### Input:\\n\\n\\n### Response:\\nOne of the most famous tall towers in Canada is the CN Tower in Toronto. Standing at a height of 553.33 meters (1,815.4 feet), it is the tallest free-standing structure in the Western Hemisphere and the 9th-tallest in the world. The tower was completed in 1976']" + ] + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141 + }, + "id": "SBSvXue6GKlQ", + "outputId": "420c151e-f5d9-4c7c-c70d-ccf37c06d5ef" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "error", + "ename": "NameError", + "evalue": "name 'captured_output' is not defined", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcaptured_output\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mNameError\u001b[0m: name 'captured_output' is not defined" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "3c8ISmdlGOSZ" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file