diff --git "a/Gemma_2_2b_Unsloth_finetuning.ipynb" "b/Gemma_2_2b_Unsloth_finetuning.ipynb" new file mode 100644--- /dev/null +++ "b/Gemma_2_2b_Unsloth_finetuning.ipynb" @@ -0,0 +1,6862 @@ +{ + "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!" + ] + }, + { + "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\"\n", + "\n", + "# Install Flash Attention 2 for softcapping support\n", + "import torch\n", + "if torch.cuda.get_device_capability()[0] >= 8:\n", + " !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 331, + "referenced_widgets": [ + "b98deac402f94f32b982529906fdd059", + "3ff6801117ae4a8484ba5b441db6a622", + "7ec126f592914cb6bf22afc1624cf51d", + "2d13343db0a842d5ba00e94ae9ebcd0f", + "5319abc2828c4ab29453c84ed1d1f101", + "b9b5d4bd07eb49dcae4c78f90f065c59", + "dc99982081b84763be5779b328d8d74a", + "daec0bd9f5fd4dbe89a4b309b80828cb", + "ca42671e31114c218c98d1b80a0ab8b5", + "3e85e215583947a1a1ea4d31d4d046fa", + "d2586a6a8c55471faac9381585dc32da", + "02c9b28ba88d4e23863dc1b2c072becc", + "894d23fb562a45ffb986e22b3b19e3eb", + "afb183d59ecb4ac69e74a1e92aa9797a", + "28ece098deee41da820e28f81541e837", + "4ffeab0cb3c446c9b76f390f69ef775e", + "3e426520ba3a4475b1260b6991fafe6b", + "432faf95d3154d6eabe6dd68d0ff910d", + "bfa724bb20dc449a89cd56f15b33a490", + "dad9b5ed1ec94c97b4c1de069a608f5b", + "a887c7be6da84583aa79bcdb6509817e", + "8a8a7a84bdf6434db4c9dac503008062", + "823230c7f64f4fc5b61271ab9014b1fe", + "5cba789481864bc584f65c8a1f15b583", + "0b9703507d004f8ab23c58cfd53a21bd", + "29d935841e734155aaa1243bab3fb616", + "32c01cf0bea341ffa0acc6ac65820806", + "408f4a8ed2cf4d8589a063171ed17d7c", + "d241b67c2e794f0a878bdc3f0c04e308", + "4bd1e7bcf694462bb20d70645367f4fc", + "c8cdb91d2f0a4a308ab8adea0e94f7d7", + "5a7a036995c84940877213b94c905dbb", + "2dbfe2911050487e8ec5fa6f5daad0c5", + "e26b322930f24b92ab8cfeafc5751939", + "3c641afa9dd44470a7906a0588a3ca67", + "a36d44d16c944deb850103a33f4cdc6b", + "68f1b0a406134eaabee4ab1932d9739f", + "1f481be6624e463ca083e3e80f8fef1d", + "66be70eca39540c9b471b1f73bcac010", + "2a62c9e49d9a4a36b3c266479f780637", + "199088a9cbd14b79ad5e6d96e4ca3a8f", + "daeb4c06806c461ba1d7e5663ff129df", + "70c0a17b4e9a4ee19328ce750c30e790", + "752546010193451db8392e32a0a36417", + "16ca3f914d104fbeab198bc5f37904cc", + "2a35a35c7b9e4a67963b8d3aaaaf1e53", + "f02ecfd039ec47b1aed81cea46303fe3", + "3858fb08ec884c9f87113734ee3cbc9d", + "bf482fa43ab7478299d44f461f21c2e4", + "a49ecaf08fe74cdc80e76650c0a7d689", + "88300f63e24948f487846e9ce30be81f", + "d54c70aa11994073b0fd70cb1c0c5deb", + "03ce807cb7fa4727b7c027aaf839554d", + "9fd04745a2de47a0b73293be4007d503", + "7fcf255ea4174670aa3daf4f7f7dfb58", + "50a88e08676e4c378d68a0e34ea3f89d", + "707b692233f14b91b48dac15f0bb3923", + "b599319cfafe4948aa7f454ddf8b0f5a", + "cf0ce4efdc30421b824129574eebe7b2", + "b379f3423186498c8c82054ddbfd009d", + "dd4d3b973a4b4ed58e4f29359ff93b74", + "583faab9a5c3458e9f662e5903632425", + "9eee537fa1d84b36bfce481112150576", + "6a5c151f7ac04b489199c4d66b69d90f", + "ce1ac7f9b4364a4c962f09398fec6d3f", + "95cbc3010002406faf8154812b5a1a8c" + ] + }, + "id": "QmUBVEnvCDJv", + "outputId": "10436b44-7e7b-4b8d-8d55-59ea9f5830bd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "πŸ¦₯ Unsloth: Will patch your computer to enable 2x faster free finetuning.\n", + "==((====))== Unsloth 2024.9.post3: Fast Gemma2 patching. Transformers = 4.44.2.\n", + " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux.\n", + "O^O/ \\_/ \\ Pytorch: 2.4.1+cu121. CUDA = 7.5. CUDA Toolkit = 12.1.\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.28.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": "b98deac402f94f32b982529906fdd059", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "model.safetensors: 0%| | 0.00/2.22G [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 = 3157,\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" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "EbUIVm1jLt6P" + }, + "outputs": [], + "source": [ + "from datasets import Dataset\n", + "import pandas as pd\n", + "dataset = Dataset.from_pandas(pd.read_csv('train.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 49, + "referenced_widgets": [ + "17c04d54063249cbb7ca949fce48442b", + "8a4b784bbcf54fa69c69f71228e2eab7", + "1d1ce5c649f1457494e90e140532dd9e", + "f1d0cb9c3b2a45f08d4115db9abb50ca", + "2c17d5e9b5da49de95327e12ea6c613d", + "7083a705495a4e7a83b8ef1c71ffdca2", + "41edab5d78f6466e83cfa339cd94c924", + "ee8083c8429c442ca161901385c868a0", + "a8beca360e244523b592feae6e5a0fe3", + "de020cd0340b491fad4737067d7de827", + "ed85db3b61ab457eadc7d5cb04a2403e" + ] + }, + "id": "LjY75GoYUCB8", + "outputId": "c3722c4f-6f58-4c56-f36f-6233dc92ae3b" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "17c04d54063249cbb7ca949fce48442b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map: 0%| | 0/2700 [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": [ + "a6001deecd1b441cad4fb9ef73dea593", + "4719bb75e6754b8fbc260c3f413a018f", + "c57687e753c141cf8f897c06643fe6f6", + "48680faf594742299b3663fe36b21612", + "67d5e68310854e96a86156eeaf96c4e2", + "34a00fe6fbe64d06969c29a14ac0794d", + "0411f0e428b4435b976e22df6b8790a0", + "41a585c2b6e4440dbc88f28ddeb1c7c2", + "a0ffa168614740c89074ec0aa2f76493", + "0e39a5ac20d9435ea186ccf715203dba", + "a9a9a824579a456c8feae80de7d58481" + ] + }, + "id": "95_Nn-89DhsL", + "outputId": "1d9946b8-fabb-49b1-d08c-9be1df0aff4a" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a6001deecd1b441cad4fb9ef73dea593", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Map (num_proc=2): 0%| | 0/2700 [00:00\n", + " \n", + " \n", + " [3000/3000 1:27:56, Epoch 8/9]\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
StepTraining Loss
1000.809900
2000.471200
3000.452000
4000.419900
5000.389400
6000.396300
7000.376600
8000.331700
9000.338700
10000.335900
11000.282800
12000.281600
13000.283500
14000.259900
15000.230700
16000.235800
17000.232200
18000.197000
19000.199700
20000.203000
21000.175900
22000.171800
23000.171600
24000.160500
25000.144100
26000.145600
27000.146000
28000.119600
29000.118700
30000.118800

" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "trainer_stats = trainer.train()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pCqnaKmlO1U9", + "outputId": "21875fd4-df3b-4ca0-904c-0164cfd40d37" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5340.1034 seconds used for training.\n", + "89.0 minutes used for training.\n", + "Peak reserved memory = 5.273 GB.\n", + "Peak reserved memory for training = 2.576 GB.\n", + "Peak reserved memory % of max memory = 35.754 %.\n", + "Peak reserved memory for training % of max memory = 17.467 %.\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": "db3058a9-3435-4d14-932f-e6ae4587033d" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['\\n### Instruction:\\nμ„œμšΈμ‹œ 20λŒ€ 남성, 여행지 μΆ”μ²œ\\n\\n### Response:\\nμœ μ‚¬ν•œ μ‚¬λžŒλ“€μ˜ λ§Œμ‘±λ„λ₯Ό κ³ λ €ν•˜μ˜€μ„ λ•Œ μΆ”μ²œλ“œλ¦¬λŠ” μ—¬ν–‰μ§€λŠ” λ§ˆλ‹ˆ μ£Όλ„€μž…λ‹ˆλ‹€.']" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " my_prompt.format(\n", + " \"μ„œμšΈμ‹œ 20λŒ€ 남성, 여행지 μΆ”μ²œ\", # 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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lg60IgwoD4xD", + "outputId": "1768ab35-e93b-4f66-a9b1-66dadf93f1ff" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['\\n### Instruction:\\n30λŒ€ 남성을 μœ„ν•œ 역사 κ΄€λ ¨ 여행지 μΆ”μ²œ ν•΄μ€˜.\\n\\n### Response:\\nκ΄‘κ΅ν˜Έμˆ˜ 곡원에 κ°€λ³΄μ…¨λ‚˜μš”? κ°€λ³΄μ‹œμ§€ μ•ŠμœΌμ…¨λ‹€λ©΄, κ΄‘κ΅ν˜Έμˆ˜ 곡원을 μΆ”μ²œλ“œλ¦½λ‹ˆλ‹€.']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " my_prompt.format(\n", + " \"30λŒ€ 남성을 μœ„ν•œ 역사 κ΄€λ ¨ 여행지 μΆ”μ²œ ν•΄μ€˜.\", # 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)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "T88NmfQ4EABS", + "outputId": "c1775675-1e4b-4521-9035-505fae76c497" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "['\\n### Instruction:\\n20, 30λŒ€ 여성이 μ’‹μ•„ν•  λ§Œν•œ 인생샷 여행지 ν•˜λ‚˜ 골라쀘\\n\\n### Response:\\nμœ μ‚¬ν•œ μ‚¬λžŒλ“€μ˜ λ§Œμ‘±λ„λ₯Ό κ³ λ €ν•˜μ˜€μ„ λ•Œ μΆ”μ²œλ“œλ¦¬λŠ” μ—¬ν–‰μ§€λŠ” 솑도 ν˜„λŒ€ μ•„μšΈλ › + λ“œλ¦Ό consentimientoμž…λ‹ˆλ‹€.']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# alpaca_prompt = Copied from above\n", + "FastLanguageModel.for_inference(model) # Enable native 2x faster inference\n", + "inputs = tokenizer(\n", + "[\n", + " my_prompt.format(\n", + " \"20, 30λŒ€ 여성이 μ’‹μ•„ν•  λ§Œν•œ 인생샷 여행지 ν•˜λ‚˜ 골라쀘\", # 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)" + ] + }, + { + "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/" + }, + "id": "upcOlWe7A1vc", + "outputId": "9d4a2e07-72f9-44db-a429-160419f40c70" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('lora_model/tokenizer_config.json',\n", + " 'lora_model/special_tokens_map.json',\n", + " 'lora_model/tokenizer.model',\n", + " 'lora_model/added_tokens.json',\n", + " 'lora_model/tokenizer.json')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.save_pretrained(\"lora_model\") # Local saving\n", + "tokenizer.save_pretrained(\"lora_model\")\n", + "# model.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving\n", + "# tokenizer.push_to_hub(\"your_name/lora_model\", token = \"...\") # Online saving" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 145, + "referenced_widgets": [ + "f8b7b32766314e96a758cf4f145cdea8", + "932f663d06bb4578b56104d6d985f855", + "02ca8f66af5641fe8814e874aba86ab2", + "bd46a7dc2ba8460992e732c84618f4e4", + "8b3e0c0b7d8741eb9b5b0d7a6c0a34f9", + "aa6c42694a144e60b6a44596aee73fe5", + "481a74bb83704a469152edfff619ba24", + "c669ce471c974262a1294dd75ab07f52", + "781a67ef3e914879bc96effcd8b41697", + "c2bd4556856d468e815c9ad12283e5e1", + "9e3bdb36d2234935a05f06909e7c70cc", + "f61ac1152f6d411980bfc12e944b7d79", + "98051bd6e5ff45638a2e265f6158cc82", + "82cc5d76f5b54e9abefe1ad48518b8c9", + "deb14741904c405a95075171d0e4fab1", + "98e7ad48d0b04236a17f41d21bcb8fda", + "b01a4056819641b58c9a7a9b2438fb07", + "1a1fc8509bbc425abd23b118ccc4d8e8", + "0944ff904bed41c1b3c8951327073fbf", + "80be92ccc68f4aa58dbc5a6c3cb6c307", + "251fe4a4727d452aa51651f4b5a09010", + "2fd937ed61c84c4e8fe8f50f07b1b0d4", + "be39eff76599457d9c593fdce251e0f4", + "22649e48d0bb4a6cbb3af69721713518", + "3c9203dd50444b09925a44f7401706e1", + "98fa156bb57d4094b54c11a74c206a3b", + "3867c9d7eff34ed6afd8dd9b0142f1aa", + "a171da9a74a448a699cb8107fd26ed9e", + "f35f11dc7cd34a25850d6606e48a8cf8", + "ae6a7e18501f42808e1e5d0aed95f713", + "3d5bfd8c487b4a63b7daec6aea3d3f11", + "76b03b919b67422baa3e361860525592" + ] + }, + "id": "fcJeUBxka83E", + "outputId": "a41aa2f3-952b-483d-9663-9bc0d53448f3" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f8b7b32766314e96a758cf4f145cdea8", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "VBox(children=(HTML(value='


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