sarathrkrishna/python_coding_assistant
042
1{"cells":[{"cell_type":"markdown","metadata":{"id":"OwZyiKdD02qU"},"source":["To run this, press \"*Runtime*\" and press \"*Run all*\" on a **free** Tesla T4 Google Colab instance!\n","<div class=\"align-center\">\n","<a href=\"https://unsloth.ai/\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n","<a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord button.png\" width=\"145\"></a>\n","<a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a> Join Discord if you need help + ⭐ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐\n","</div>\n","\n","To install Unsloth on your local device, follow [our guide](https://unsloth.ai/docs/get-started/install). This notebook is licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme).\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"]},{"cell_type":"markdown","metadata":{"id":"8IPTXDb502qX"},"source":["### News"]},{"cell_type":"markdown","metadata":{"id":"5uXHp9bf02qY"},"source":["Introducing **Unsloth Studio** - a new open source, no-code web UI to train and run LLMs. [Blog](https://unsloth.ai/docs/new/studio) • [Notebook](https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb)\n","\n","<table><tr>\n","<td align=\"center\"><a href=\"https://unsloth.ai/docs/new/studio\"><img src=\"https://unsloth.ai/docs/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F~%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252FxV1PO5DbF3ksB51nE2Tw%252Fmore%2520cropped%2520ui%2520for%2520homepage.png%3Falt%3Dmedia%26token%3Df75942c9-3d8d-4b59-8ba2-1a4a38de1b86&width=376&dpr=3&quality=100&sign=a663c397&sv=2\" width=\"200\" height=\"120\" alt=\"Unsloth Studio Training UI\"></a><br><sub><b>Train models</b> — no code needed</sub></td>\n","<td align=\"center\"><a href=\"https://unsloth.ai/docs/new/studio\"><img src=\"https://unsloth.ai/docs/~gitbook/image?url=https%3A%2F%2F3215535692-files.gitbook.io%2F~%2Ffiles%2Fv0%2Fb%2Fgitbook-x-prod.appspot.com%2Fo%2Fspaces%252FxhOjnexMCB3dmuQFQ2Zq%252Fuploads%252FRCnTAZ6Uh88DIlU3g0Ij%252Fmainpage%2520unsloth.png%3Falt%3Dmedia%26token%3D837c96b6-bd09-4e81-bc76-fa50421e9bfb&width=376&dpr=3&quality=100&sign=c1a39da1&sv=2\" width=\"200\" height=\"120\" alt=\"Unsloth Studio Chat UI\"></a><br><sub><b>Run GGUF models</b> on Mac, Windows & Linux</sub></td>\n","</tr></table>\n","\n","Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)\n","\n","Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)\n","\n","New in Reinforcement Learning: [FP8 RL](https://unsloth.ai/docs/new/fp8-reinforcement-learning) • [Vision RL](https://unsloth.ai/docs/new/vision-reinforcement-learning-vlm-rl) • [Standby](https://unsloth.ai/docs/basics/memory-efficient-rl) • [gpt-oss RL](https://unsloth.ai/docs/new/gpt-oss-reinforcement-learning)\n","\n","Visit our docs for all our [model uploads](https://unsloth.ai/docs/get-started/unsloth-model-catalog) and [notebooks](https://unsloth.ai/docs/get-started/unsloth-notebooks)."]},{"cell_type":"markdown","metadata":{"id":"D_HhWDDg02qY"},"source":["### Installation"]},{"cell_type":"code","source":["# !rm -rf granite_finetune_q4\n","# !rm -rf granite_finetune_q4_gguf\n","# !rm -rf /root/.config/Google/DriveFS\n","# !rm -rf /root/.cache\n","!du -ahx /content /root 2>/dev/null | sort -rh | head -n 10\n"],"metadata":{"id":"grGTPYDd_lWA","executionInfo":{"status":"ok","timestamp":1781588825463,"user_tz":-330,"elapsed":135,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"f53a5e32-1b5a-4cdc-fa92-6bf0d3ea4391","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["131M\t/root\n","73M\t/root/.npm/_cacache\n","73M\t/root/.npm\n","71M\t/root/.npm/_cacache/content-v2/sha512\n","71M\t/root/.npm/_cacache/content-v2\n","56M\t/root/.cache/node-gyp/20.19.0/include/node\n","56M\t/root/.cache/node-gyp/20.19.0/include\n","56M\t/root/.cache/node-gyp/20.19.0\n","56M\t/root/.cache/node-gyp\n","56M\t/root/.cache\n"]}]},{"cell_type":"code","source":["# !rm -rf /root/.julia\n","# !du -ahx /content /root/.cache 2>/dev/null | sort -rh | head -n 10\n"],"metadata":{"id":"j0iFiwgMAqmZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pgHeW-Se02qZ"},"outputs":[],"source":["%%capture\n","import os, importlib.util\n","!pip install --upgrade -qqq uv\n","if importlib.util.find_spec(\"torch\") is None or \"COLAB_\" in \"\".join(os.environ.keys()):\n"," try: import numpy, PIL; _numpy = f\"numpy=={numpy.__version__}\"; _pil = f\"pillow=={PIL.__version__}\"\n"," except: _numpy = \"numpy\"; _pil = \"pillow\"\n"," !uv pip install -qqq \\\n"," \"torch==2.7.1\" \"triton>=3.3.0\" {_numpy} {_pil} torchvision bitsandbytes \"transformers==4.56.2\" \\\n"," \"unsloth_zoo[base] @ git+https://github.com/unslothai/unsloth-zoo\" \\\n"," \"unsloth[base] @ git+https://github.com/unslothai/unsloth\"\n"," !uv pip install -qqq --no-deps \"torchcodec==0.5\"\n","elif importlib.util.find_spec(\"unsloth\") is None:\n"," !uv pip install -qqq unsloth\n","!uv pip install --upgrade --no-deps transformers==4.56.2 \"tokenizers>=0.22.0,<=0.23.0\" trl==0.22.2 unsloth unsloth_zoo\n","\n","# Mamba is supported only on torch==2.7.1. If you have newer torch versions, please wait 30 minutes!\n","!uv pip install --no-build-isolation mamba_ssm==2.2.5 causal_conv1d==1.5.2\n","!uv pip install --no-deps --upgrade \"torchao>=0.16.0\""]},{"cell_type":"markdown","metadata":{"id":"TGMWlrRdzwgf"},"source":["### Unsloth"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":572,"referenced_widgets":["fdfa12e530634012b7ee1b5667590b3b","f1c73eef13314b8cb8662f3527a5a28b","40dd90487ba1456992772d93718bbda6","0e2dfc1adebb4940ad335e7955426f11","7f3496917c374650a6ec02e3a30af008","5b6f51c01201413e8b287d241029defa","bd06df4c9bab4b9190a0c1fb672af0a4","e0d28f73c4f846088dc87fb8a92f0b58","fc9ed7f6311a47f7812d5126704e7782","03fcc03cffcc4d7a88c787a71301b001","47cae1f3ce8a4e62b91b5c44a272596f","49c9a7e53f0742a1bcd83ff4391ed1ad","45382b282ca6450a88992beb3c27c438","e7069ef9c67d451c91301f47036f8a83","1121246f32d74c349a3d6289e68a7761","488ed282cef2479faf4acf5ea7023583","dfc8e9c3ca504a518cdf075589d0f7a2","2cd85d22a8174574893bd5dbabb81bba","fa8d8661e27c447aa7bc1b2204b81ef9","76498b8353944e7cb6139ccd4cb4365b","ea2fa7f8cf5542429940a15b39299784","0231b39f5d0b4c8f823b902cb61edadc","afd9709e544646378990b4814ae844cb","bb06b87fe3594d81923b322d6de65a06","696d52694c284d75bcfd52935c7ab452","d20f7e1dd52b483b99b7a8c5102e3ca7","77833775fafe477594f0a55842b1a82e","1b3d5b83c53e4e439576f136c25622f7","67cb716d06204b4092d8c6d1b0351a8f","7c0919465d454e9986f2e07b890939d0","2296e7c09fbc43b3ae38bd830302ac7d","5eabad074d42494bab207536d8cc62aa","be6c3c6d0e1948169ff23d431d95e13e","673092f224374d97b5ce2381e512ed49","ea0b26b525ea48d59fd8f2a034527161","2dfaeed768e249f49bbd696978d4a10d","bedb9629d894435fa5704981bde37477","02eaa3fb51914a86a985ca15bd32313e","7217ecfc28f446ee82d9f0d53355b61b","7ebd1a49e96946d78627f03596073a70","396d53d5f010462182ff7323ebe7006b","b9f058c298034b7bbc9357ff1184c04e","8dcc4ab1508a417c817331ad441ec377","42b96a632898484bbd88e3a0dc7b6734","bdd4fb1dce164f5e8de51794488fe853","cb8eed55f73b42febfda9d81fa728e83","9394ecdfff084e39956162c06ba3bba2","6af87905c26445968358d3100063f6bd","d262258cf62d46f9a157352b8a9f4559","13326b95327947ee97ceb6b67ba99c1b","37af6d1ca9104587a3c6e0be6f54fe08","c328f79031d14ec4b00b02619c9b1e66","838a937d87884342b648651cd16edc75","84858bc41f414a7ea20880dce571e008","9ade436fc8d341c6b86ea29566d1ebef","18616ff2a0cd4f36aa588740b4af4dd3","62c09dca55084587995e699e6d9cc068","661fe35845c540d4930b6b3df3ed316d","71c6cbcdc1de4b888e7bc332f22ec923","9d5d9b3987a44c7ab281205b30d83189","b54e0dde91bf4980b00bba824b5a9a84","d8b3061b94d144b6acb7b8c4e8442aac","f99af0c01b92422f9a2768b2230d8d84","db7c89fa998e4d06b82008c0929978cb","6566682c869e45bbacd3456e34c2a471","dd72c7abb3004c5080f25a1c2b325f7b","47248e445500434282cb09627578e5e7","1621acf3f6b44fada07c676dee8c193c","e73396e157c24fb3b15acf8192a67502","205f1cdec518468d86493f7108a38bc0","611a4285ad774462afb5774cc3c013fd","de15f030dfce48a39e1aa6a8d916e90a","2eadb252ac7341909bd92b7368c5098e","445afd107ff24e2fb44b823e7774192c","0d6c5b92518441e8b5635f66634acbff","5eef8c0c5b534147b473e358bf754cd8","6ba207c2aa9c48efb30a47175fac0e08","07674a88989542af8c0073673360a486","75d2b3c9f7ba4cf1bf785dc7074f55ba","e681b9808fcb46a2820ecfa42383e936","2fd4b4963b2c400f9382f742e08f3d95","17fd0d61fc1347be9ba68a4972fff2bb","d68193e7cd5d4a13b2fcae3493227fa0","b545c114c61b43259ec3307ebb256872","9ed5b4aa49994fce83c2f381cabf2d8e","f99b64f5efe74a73891b4c67ad447019","b47617f462b24bb28a6a78e6135b0f63","c7a2d262078a4c3687e0e9896ad13332","602ff90d83204c3aa70153d246791577","35f1eaf6a18b40bb809cacf3beddbf5d","4a644698d65e4179a05c3ded0a27fb4e","166a1aa7874f4954b568d916c6ad5cab","25b963c3d26344d9978de3ed3211e801","f1cd0eae74044430aaa35932571bd2f1","bd83fa7cc9c845e2ac8055abf708faa4","711a2dab897344f78d37a4a89f212309","40affdb6c84b4187a8eddc4cac5dea52","3aa3faa814244f6c98a7e9d1cf5fabc7","d90c0acd87e6467784ff34ebee73bb71","5d983d2229a947f580cbe7a2b7f5a7c5","a8be2639ca4e4496bebd861674a87f8f","bc5b60f2e6a5443abc3b84f3faec3618","6fb402cbd52243b29cfd1138a9dee0c3","4f326f12b76f49efbda562dcb32118b8","e9c92fc787ec4667841167d73fd90029","0812f14e7d9c42bd956850cc3d05775b","816afd3092944871abd4a9552462ba3f","d34b7a70a08b4657a708b1644e8e36d3","288d13defbbc42348b4c6b8f9b1de89d","05b28b9f0cc14ea19ca217d5f68cb36f","e8a84b89fbc748df9b7bd9bd0ff9a5a0","81f43b584b394f4da69c650ca2bef6b6","d199548a20314f77a28a62b485f4444b","774760c4a28246399cff16068483fb81","c1d48d0fd5114a37a8f577e292fe1778","e8b7d13c76a64956b7a39b3334077de7","dae2dd52890b472399d0a1bf7ab3d55b","01afefdfbc8b436b8d24f7f310507f12","a0f32541f82e4e08ab19bc78fc6cbe74","bc40afbc91d742dea93bb04dd0660637","db3fbaf209874c638364bc5a5ea75622","2cb3b7c683b24fcfb82bd2ef5a3b268f","92c67901a5b849a68004ca3a6d78f7a2","26281def498b4cfb8750099c9a59d549","45418570f79543f9abfef4a275898694","d8e22089fa9f48da93fef8b1c0f6575b","d2a50023f4164db58adff87cd4b4bda1","0f70bbe367344c92b34184e0c92d5572","d800d02d9e2c47feb97495dc7676e2c9","25a6ac704e7848099f56556cac953382","21ea3a359af74bf2a436cd267a5bf510","56a007f5e92c463b9e49f574f53144d2","717c0ed491e34b07896d66af9883cbe7","2d4c4b78f08d42a3906d8009c226750f","024c79b9800e4ed5b7fe302649387b6a","c5638f897c314e66b584fc92dbfe17e6","0dfb86cbf92d4b728c2136cac34e4db1","ae6b81e612d74ad19e6c5d2ee0ed51a0","3cd0e2396d7d49e1a24c42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ravi","userId":"03114359698583885786"}}},"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 2026.6.7: Fast Granite patching. Transformers: 4.56.2.\n"," \\\\ /| Tesla T4. Num GPUs = 1. Max memory: 14.563 GB. Platform: Linux.\n","O^O/ \\_/ \\ Torch: 2.7.1+cu126. CUDA: 7.5. CUDA Toolkit: 12.6. Triton: 3.3.1\n","\\ / Bfloat16 = FALSE. FA [Xformers = None. 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: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fdfa12e530634012b7ee1b5667590b3b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00004.safetensors: 0%| | 0.00/4.83G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"49c9a7e53f0742a1bcd83ff4391ed1ad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00004.safetensors: 0%| | 0.00/4.99G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"afd9709e544646378990b4814ae844cb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00004.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"673092f224374d97b5ce2381e512ed49"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00004.safetensors: 0%| | 0.00/2.79G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"bdd4fb1dce164f5e8de51794488fe853"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards: 0%| | 0/4 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"18616ff2a0cd4f36aa588740b4af4dd3"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json: 0%| | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"47248e445500434282cb09627578e5e7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"07674a88989542af8c0073673360a486"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["vocab.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"602ff90d83204c3aa70153d246791577"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["merges.txt: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d983d2229a947f580cbe7a2b7f5a7c5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e8a84b89fbc748df9b7bd9bd0ff9a5a0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json: 0%| | 0.00/579 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2cb3b7c683b24fcfb82bd2ef5a3b268f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["chat_template.jinja: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"717c0ed491e34b07896d66af9883cbe7"}},"metadata":{}}],"source":["from unsloth import FastLanguageModel\n","import torch\n","\n","fourbit_models = [\n"," \"unsloth/granite-4.0-micro\",\n"," \"unsloth/granite-4.0-h-micro\",\n"," \"unsloth/granite-4.0-h-tiny\",\n"," \"unsloth/granite-4.0-h-small\",\n","\n"," # Base pretrained Granite 4 models\n"," \"unsloth/granite-4.0-micro-base\",\n"," \"unsloth/granite-4.0-h-micro-base\",\n"," \"unsloth/granite-4.0-h-tiny-base\",\n"," \"unsloth/granite-4.0-h-small-base\",\n","\n"," # 4bit dynamic quants for superior accuracy and low memory use\n"," \"unsloth/gemma-3-12b-it-unsloth-bnb-4bit\",\n"," \"unsloth/Phi-4\",\n"," \"unsloth/Llama-3.1-8B\",\n"," \"unsloth/Llama-3.2-3B\",\n"," \"unsloth/orpheus-3b-0.1-ft-unsloth-bnb-4bit\" # [NEW] We support TTS models!\n","] # More models at https://huggingface.co/unsloth\n","\n","model, tokenizer = FastLanguageModel.from_pretrained(\n"," model_name = \"ibm-granite/granite-4.1-8b\",\n"," max_seq_length = 4096, # Choose any for long context!\n"," load_in_4bit = True, # 4 bit quantization to reduce memory\n"," load_in_8bit = False, # [NEW!] A bit more accurate, uses 2x memory\n"," full_finetuning = False, # [NEW!] We have full finetuning now!\n",")"]},{"cell_type":"markdown","metadata":{"id":"SXd9bTZd1aaL"},"source":["We now add LoRA adapters so we only need to update a small amount of parameters!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6bZsfBuZDeCL","outputId":"c20e0c82-46c7-4fb0-b5a9-38be5cf1a2ea","executionInfo":{"status":"ok","timestamp":1781590027313,"user_tz":-330,"elapsed":6837,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Unsloth: Explicit target_modules are constrained by the finetune_(vision|language|attention|mlp) filters; adapters attach only where both select.\n"]}],"source":["model = FastLanguageModel.get_peft_model(\n"," model,\n"," r = 32, # Choose any number > 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"," \"shared_mlp.input_linear\", \"shared_mlp.output_linear\"],\n"," lora_alpha = 32,\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":["<a name=\"Data\"></a>\n","### Data Prep\n","#### 📄 Using Google Sheets as Training Data\n","Our goal is to create a customer support bot that proactively helps and solves issues.\n","\n","We’re storing examples in a Google Sheet with two columns:\n","\n","- **Snippet**: A short customer support interaction\n","- **Recommendation**: A suggestion for how the agent should respond\n","\n","This keeps things simple and collaborative. Anyone can edit the sheet, no database setup required. \n","<br>\n","\n","---\n","<br>\n","\n","#### 🔍 Why This Format?\n","\n","This setup works well for tasks like:\n","\n","- `Input snippet → Suggested reply`\n","- `Prompt → Rewrite`\n","- `Bug report → Diagnosis`\n","- `Text → Label or Category`\n","\n","Just collect examples in a spreadsheet, and you’ve got usable training data. \n","<br>\n","\n","---\n","<br>\n","\n","#### ✅ What You'll Learn\n","\n","We’ll show how to:\n","\n","1. Load the Google Sheet into your notebook\n","2. Format it into a dataset\n","3. Use it to train or prompt an LLM\n","\n","\n","The chat template for granite-4 looks like this:\n","```\n","<|start_of_role|>system<|end_of_role|>Knowledge Cutoff Date: April 2024.\n","Today's Date: June 24, 2025.\n","You are Granite, developed by IBM. You are a helpful AI assistant.<|end_of_text|>\n","\n","<|start_of_role|>user<|end_of_role|>How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|end_of_text|>\n","\n","<|start_of_role|>assistant<|end_of_role|>Astronomers make use of the unique spectral fingerprints of elements found in stars...<|end_of_text|>\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["e9af2b76619a46d9b8b69648169b371c","3a9b48eb541f4ec68eeebf46b73b43f3","0757513fa5e44514851b2854a357f340","53eead9d1d234e88af6c0dda957e6505","3b7440894f224ff3a0cea69e17a3523d","f4a0627be368417c8e8fd0e6f4d610be","a88be4668b99400b99c5cbb780b35d37","6a89b7530f45489785f379382bf59787","fa4d4431b73446078bb8e8155aca552d","50e227041398469e97ca85d06ba02a1d","7cb7fc07a0c844a38379e280fad3ab01","cd650ed51ecf4bed9ab22d6742266822","6f2cd61863064fb48afc8b89963ac9aa","e2871783cd5842a798ab0f7e28d7f749","80233f3b3d75449d96afab278b27358d","c62ea8e94eaf4e92814ca1a444a2a9c1","1ddb8c192d2e40618e82bb35f2d869bd","e2e5ce6fdb78415abec4b0824243f9a7","7c9916b4dbfb4aa499ab135ebfbe9f77","d6fb6564821a4520ac17c6b00065b0bb","5c8f8bb12a954b6e9684f4bc3ae39441","bfb04189ae214c5f88adbbbb1d6a9353","6f00940c55c04252962a3c254ccb93b6","4bc19949d13648e0acdb17200ce25638","4a220b3c28c440c1b2ed6324e7b967eb","2fcd88263a6a4d1a9b4d451bc60080f6","88c106caba7c40909cf9a3d86fcc6e6c","bf7ed993cc2f4ab58d8f79c8a29ba3f2","94136443e1f246db90c6930eef77b07d","ffbbc6e6c6734851ae6284f74b7aa464","57c78e31874442f28c66dc5b8a41a8f3","a908c0c4466142e5911c379c907be651","f683dee2a1084de38775524af30c02fc","7bfc881903c3434aa58fc0dcfa9d4ba7","47e931234e5d40359c6b043b50d2f9f7","4599ce53da4042759ad4b0018a8f9fe6","f5f8f3191d6d4c7da22771eeb5dbbbdd","26c1c037ff87494c9b06e72d87353503","913a2ddbd1dc46da8b368678819db027","9a54838064734166a44af74d072bcc66","44e2e10e1da8474a8faf17ad9a289911","71f171f81d844b41b736286505a13cc1","92e7848b9ba34803bb67aa1440a2a507","0990dff92a7c448ea80784fb361e31c3"]},"id":"Mkq4RvEq7FQr","outputId":"33520db7-4dac-4290-c916-6cfc367db53a","executionInfo":{"status":"ok","timestamp":1781590104688,"user_tz":-330,"elapsed":77371,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["README.md: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e9af2b76619a46d9b8b69648169b371c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["data/train-00000-of-00002.parquet: 0%| | 0.00/212M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cd650ed51ecf4bed9ab22d6742266822"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["data/train-00001-of-00002.parquet: 0%| | 0.00/135M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f00940c55c04252962a3c254ccb93b6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0%| | 0/559515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7bfc881903c3434aa58fc0dcfa9d4ba7"}},"metadata":{}}],"source":["from datasets import load_dataset, Dataset\n","\n","# Use the below shared sheet\n","# sheet_url = 'https://docs.google.com/spreadsheets/d/1NrjI5AGNIwRtKTAse5TW_hWq2CwAS03qCHif6vaaRh0/export?format=csv&gid=0'\n","\n","# Or unsloth/Support-Bot-Recommendation\n","# sheet_url = \"https://huggingface.co/datasets/unsloth/Support-Bot-Recommendation/raw/main/support_recs.csv\"\n","\n","# dataset = load_dataset(\n","# \"csv\",\n","# data_files = {\"train\": sheet_url},\n","# column_names = [\"snippet\", \"recommendation\"], # Replace with the actual column names of your sheet\n","# skiprows = 1 # skip header rows\n","# )[\"train\"]\n","dataset = load_dataset(\n"," \"jtatman/python-code-dataset-500k\",\n"," split=\"train\"\n",")"]},{"cell_type":"code","source":["print(dataset)\n","print(dataset.column_names)\n","print(dataset[0])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"D7dMc80q8qke","executionInfo":{"status":"ok","timestamp":1781590104820,"user_tz":-330,"elapsed":7,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"a8520ba0-f58e-4bf5-f735-ea0a084fbd35"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Dataset({\n"," features: ['output', 'instruction', 'system'],\n"," num_rows: 559515\n","})\n","['output', 'instruction', 'system']\n","{'output': 'Here is an example of a nested loop in Python to print every combination of numbers between 0-9, excluding any combination that contains the number 5 or repeating digits:\\n\\n```python\\nfor i in range(10): # First digit\\n for j in range(10): # Second digit\\n for k in range(10): # Third digit\\n # Checking for the conditions\\n if i != 5 and j != 5 and k != 5 and i != j and i != k and j != k:\\n print(i, j, k)\\n```\\n\\nThis code will generate and print every combination of three digits between 0-9 that do not contain the number 5 and do not have any repeating digits.', 'instruction': 'Create a nested loop to print every combination of numbers between 0-9, excluding any combination that contains the number 5. Additionally, exclude any combination that contains a repeating digit. Implement the solution without using any built-in functions or libraries to check for repeating digits.', 'system': 'You are a Python code analyst, evaluating scripts for potential improvements.'}\n"]}]},{"cell_type":"markdown","metadata":{"id":"K9CBpiISFa6C"},"source":["We've just loaded the Google Sheet as a csv style Dataset, but we still need to format it into conversational style like below and then apply the chat template.\n","\n","```\n","{\"role\": \"system\", \"content\": \"You are an assistant\"}\n","{\"role\": \"user\", \"content\": \"What is 2+2?\"}\n","{\"role\": \"assistant\", \"content\": \"It's 4.\"}\n","```\n","\n","We'll use a helper function `formatting_prompts_func` to do both!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["c10452ae745b489a891fcc8f09c79c6a","d5cd03768e9846fdafce95d702b82252","02093ca113254589a5644f7b43e1c449","785a8c9001e34f208c8e1424f25c75b2","733842106e8f4b44bc12cd9b8b29ccff","db0eb8a06b7a4438a95987dbef6299c3","2b8a861f2cf042749ddc25ae4d7c8b9d","e0f832dc58ca4a8b9ad254dd6434d6e0","80d41556459d47e0b4d9966f3ced1fbc","46b3969660d14452a8b508893a82e9d5","d32e4e7e339b4f6e821d45cbe02c81c9"]},"id":"reoBXmAn7HlN","outputId":"2cbafff5-cbfa-4e3b-9027-99dcbebab5fb","executionInfo":{"status":"ok","timestamp":1781590189368,"user_tz":-330,"elapsed":84547,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/559515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c10452ae745b489a891fcc8f09c79c6a"}},"metadata":{}}],"source":["def formatting_prompts_func(examples):\n"," instructions = examples[\"instruction\"]\n"," systems = examples[\"system\"]\n"," outputs = examples[\"output\"]\n","\n"," texts = []\n","\n"," for instruction, system, output in zip(\n"," instructions,\n"," systems,\n"," outputs\n"," ):\n"," messages = [\n"," {\"role\": \"system\", \"content\": system},\n"," {\"role\": \"user\", \"content\": instruction},\n"," {\"role\": \"assistant\", \"content\": output},\n"," ]\n","\n"," text = tokenizer.apply_chat_template(\n"," messages,\n"," tokenize=False,\n"," add_generation_prompt=False,\n"," )\n","\n"," texts.append(text)\n","\n"," return {\"text\": texts}\n","\n","dataset = dataset.map(\n"," formatting_prompts_func,\n"," batched=True,\n",")"]},{"cell_type":"code","source":["print(dataset[0][\"text\"])"],"metadata":{"id":"6Hb2Bg7z_--v","executionInfo":{"status":"ok","timestamp":1781590189395,"user_tz":-330,"elapsed":8,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"193ff7c4-4168-44a5-82b9-e70fad6c76e3","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["<|start_of_role|>system<|end_of_role|>You are a Python code analyst, evaluating scripts for potential improvements.<|end_of_text|>\n","<|start_of_role|>user<|end_of_role|>Create a nested loop to print every combination of numbers between 0-9, excluding any combination that contains the number 5. Additionally, exclude any combination that contains a repeating digit. Implement the solution without using any built-in functions or libraries to check for repeating digits.<|end_of_text|>\n","<|start_of_role|>assistant<|end_of_role|>Here is an example of a nested loop in Python to print every combination of numbers between 0-9, excluding any combination that contains the number 5 or repeating digits:\n","\n","```python\n","for i in range(10): # First digit\n"," for j in range(10): # Second digit\n"," for k in range(10): # Third digit\n"," # Checking for the conditions\n"," if i != 5 and j != 5 and k != 5 and i != j and i != k and j != k:\n"," print(i, j, k)\n","```\n","\n","This code will generate and print every combination of three digits between 0-9 that do not contain the number 5 and do not have any repeating digits.<|end_of_text|>\n","\n"]}]},{"cell_type":"code","source":["print(dataset.column_names)\n","print(dataset[0])"],"metadata":{"id":"cvtnx_ceA0i4","executionInfo":{"status":"ok","timestamp":1781590189419,"user_tz":-330,"elapsed":6,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"4067b799-2624-46c9-c291-746bc1474689","colab":{"base_uri":"https://localhost:8080/"}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["['output', 'instruction', 'system', 'text']\n","{'output': 'Here is an example of a nested loop in Python to print every combination of numbers between 0-9, excluding any combination that contains the number 5 or repeating digits:\\n\\n```python\\nfor i in range(10): # First digit\\n for j in range(10): # Second digit\\n for k in range(10): # Third digit\\n # Checking for the conditions\\n if i != 5 and j != 5 and k != 5 and i != j and i != k and j != k:\\n print(i, j, k)\\n```\\n\\nThis code will generate and print every combination of three digits between 0-9 that do not contain the number 5 and do not have any repeating digits.', 'instruction': 'Create a nested loop to print every combination of numbers between 0-9, excluding any combination that contains the number 5. Additionally, exclude any combination that contains a repeating digit. Implement the solution without using any built-in functions or libraries to check for repeating digits.', 'system': 'You are a Python code analyst, evaluating scripts for potential improvements.', 'text': '<|start_of_role|>system<|end_of_role|>You are a Python code analyst, evaluating scripts for potential improvements.<|end_of_text|>\\n<|start_of_role|>user<|end_of_role|>Create a nested loop to print every combination of numbers between 0-9, excluding any combination that contains the number 5. Additionally, exclude any combination that contains a repeating digit. Implement the solution without using any built-in functions or libraries to check for repeating digits.<|end_of_text|>\\n<|start_of_role|>assistant<|end_of_role|>Here is an example of a nested loop in Python to print every combination of numbers between 0-9, excluding any combination that contains the number 5 or repeating digits:\\n\\n```python\\nfor i in range(10): # First digit\\n for j in range(10): # Second digit\\n for k in range(10): # Third digit\\n # Checking for the conditions\\n if i != 5 and j != 5 and k != 5 and i != j and i != k and j != k:\\n print(i, j, k)\\n```\\n\\nThis code will generate and print every combination of three digits between 0-9 that do not contain the number 5 and do not have any repeating digits.<|end_of_text|>\\n'}\n"]}]},{"cell_type":"markdown","metadata":{"id":"6i5Sx9In7vHi"},"source":["We now look at the raw input data before formatting."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"dzE1OEXi7s3P"},"outputs":[],"source":["# dataset[5][\"snippet\"]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"LA-aC7w-x72-"},"outputs":[],"source":["# dataset[5]['recommendation']"]},{"cell_type":"markdown","metadata":{"id":"q76PqXv9yKa0"},"source":["And we see how the chat template transformed these conversations."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"du1MB2NqyGW4"},"outputs":[],"source":["# dataset[5][\"text\"]"]},{"cell_type":"markdown","metadata":{"id":"idAEIeSQ3xdS"},"source":["<a name=\"Train\"></a>\n","### Train the model\n","Now let's train our model. 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`."]},{"cell_type":"markdown","source":["# effective_batch_size =\n","batch_size\n","× gradient_accumulation_steps\n","× number_of_gpus"],"metadata":{"id":"lXWsOfo2L74U"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":84,"referenced_widgets":["ae515c74656442cd88afb9f1c189b67b","f73bbc88fc0042c49783b4ae84c72881","13a5210f3c6f486ca005f96c40e28949","d0151dcb8578401d973cc4b4db3b36a8","61b60dd5083e480692c22c61914c36b5","23f2e13a234c4e4bab002019eb477b54","ab53123c9c7047be981ccc1afe704d2e","5198f2d835f94240b39f84cf13c4d76f","968035a38a0c44a299c0bc3aba046a4f","67f69eb52cb449fbb8498695e5b5acf4","9c9c25d284df4a408e66e7ad3f3bbb45"]},"id":"95_Nn-89DhsL","outputId":"683dd90a-c9d6-4167-e765-9422c6604da7","executionInfo":{"status":"ok","timestamp":1781590871196,"user_tz":-330,"elapsed":666219,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Unsloth: We found double BOS tokens - we shall remove one automatically.\n"]},{"output_type":"display_data","data":{"text/plain":["Unsloth: Tokenizing [\"text\"] (num_proc=6): 0%| | 0/559515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ae515c74656442cd88afb9f1c189b67b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["🦥 Unsloth: Padding-free auto-enabled, enabling faster training.\n"]}],"source":["from trl import SFTTrainer, SFTConfig\n","trainer = SFTTrainer(\n"," model = model,\n"," tokenizer = tokenizer,\n"," train_dataset = dataset,\n"," eval_dataset = None, # Can set up evaluation!\n"," args = SFTConfig(\n"," dataset_text_field = \"text\",\n"," per_device_train_batch_size = 1,\n"," gradient_accumulation_steps = 8, # Use GA to mimic batch size!\n"," warmup_steps = 5,\n"," # num_train_epochs = 1, # Set this for 1 full training run.\n"," max_steps = 100,\n"," learning_rate = 2e-4, # Reduce to 2e-5 for long training runs\n"," logging_steps = 1,\n"," optim = \"adamw_8bit\",\n"," weight_decay = 0.001,\n"," lr_scheduler_type = \"linear\",\n"," seed = 3407,\n"," report_to = \"none\", # Use TrackIO/WandB etc\n"," ),\n",")"]},{"cell_type":"markdown","metadata":{"id":"C_sGp5XlG6dq"},"source":["We also use Unsloth's `train_on_completions` method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes!"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":118,"referenced_widgets":["312450f806b7424dafe4d8a9be8e2f36","1a826c1254b6433e9b825934a1318054","52190000edde4d1db8f2ff015d9bc903","3c0fb546f98d4eef807d40909ea25380","78a93b05fa6145d693f3367ef5fb6446","22b23c31e49d4ad3a349bb6622d13dea","e4d24092152045f4b5d6239b9f60351f","b04ec13c16da4abe8b5dfdce912c8da7","2d768fa2160f4975acb3cc0fbed52c6e","8edd7e4ed307415884f88f2a39a6bd1a","e9fb528f341a41089c77bd981427dab3","628d9fd34e604673a3db069e0e3513e2","02fef038a2274a26a0cbc89b5350200b","a900765eb91f4d0f9b8593bb4434aebc","b465466ffb37435ea19e55d2010967c7","4f87d44f3908497fa54b1a46daa4f838","aec0789b4c1649099f58ec1a1c75bba6","43fa4897a8794f5697addcbf8906a93d","d10a37c243c543068716011d722ac084","6111f275365e478ab02096d6793e8114","603dff77f5d048459fe36ba47aa8ba42","222e463d15ac4e24b8a1db5f5c9b99b0"]},"id":"juQiExuBG5Bt","outputId":"119775dc-39a1-429c-859b-a7ec71e5210f","executionInfo":{"status":"ok","timestamp":1781591319752,"user_tz":-330,"elapsed":448439,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map (num_proc=5): 0%| | 0/559515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"312450f806b7424dafe4d8a9be8e2f36"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Filter (num_proc=5): 0%| | 0/559515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"628d9fd34e604673a3db069e0e3513e2"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Unsloth: Removed 1282 out of 559515 samples from train_dataset where all labels were -100 (no response found after truncation). This prevents NaN loss during training.\n"]}],"source":["from unsloth.chat_templates import train_on_responses_only\n","trainer = train_on_responses_only(\n"," trainer,\n"," instruction_part = \"<|start_of_role|>user<|end_of_role|>\",\n"," response_part = \"<|start_of_role|>assistant<|end_of_role|>\",\n",")"]},{"cell_type":"markdown","metadata":{"id":"Dv1NBUozV78l"},"source":["Let's verify masking the instruction part is done! Let's print the 100th row again."]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":209},"id":"LtsMVtlkUhja","outputId":"013e80f6-6360-44b5-d725-7ebfc192c85c","executionInfo":{"status":"ok","timestamp":1781591319775,"user_tz":-330,"elapsed":4,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"<|start_of_role|>system<|end_of_role|>As a Python code analyst, you investigate and solve complex coding mysteries<|end_of_text|>\\n<|start_of_role|>user<|end_of_role|>Write a code to compare two lists and find the common elements, taking into consideration that both lists can contain duplicate elements and the output should only include the common elements once. Additionally, the code should have a time complexity of O(n+m), where n and m are the lengths of the two lists. \\n\\nlist1 = [3, 6, 9, 12, 9, 3, 6, 9, 12, 9]\\nlist2 = [2, 4, 5, 6, 8, 9, 9, 3, 6, 9, 12, 9]\\n\\nIn addition to finding the common elements, you should also find the maximum frequency of any common element and return it as part of the output.\\n\\nConstraints:\\n- The input lists can contain up to 10^6 elements.\\n- The elements in the lists can range from 1 to 10^9.\\n- The maximum frequency of any common element can be up to 10^6.\\n- The code should use only O(1) additional space, excluding the input and output lists.<|end_of_text|>\\n<|start_of_role|>assistant<|end_of_role|>Here's a code that meets the given requirements:\\n\\n```python\\ndef find_common_elements(list1, list2):\\n common_elements = set(list1) & set(list2)\\n max_frequency = 0\\n \\n for element in common_elements:\\n frequency = min(list1.count(element), list2.count(element))\\n max_frequency = max(max_frequency, frequency)\\n \\n return list(common_elements), max_frequency\\n\\nlist1 = [3, 6, 9, 12, 9, 3, 6, 9, 12, 9]\\nlist2 = [2, 4, 5, 6, 8, 9, 9, 3, 6, 9, 12, 9]\\ncommon_elements, max_frequency = find_common_elements(list1, list2)\\nprint(common_elements) # Output: [3, 6, 9, 12]\\nprint(max_frequency) # Output: 4\\n```\\n\\nExplanation:\\n1. First, we find the common elements between `list1` and `list2` by converting both lists into sets and using the `&` operator to get the intersection.\\n2. Then, we iterate over the common elements and calculate their frequency in both lists using the `count()` method. We take the minimum of these frequencies to ensure that we are considering only the common elements once.\\n3. We update the `max_frequency` variable if we find a higher frequency while iterating.\\n4. Finally, we return the common elements as a list and the maximum frequency.<|end_of_text|>\\n\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":20}],"source":["tokenizer.decode(trainer.train_dataset[100][\"input_ids\"])"]},{"cell_type":"markdown","metadata":{"id":"4Kyjy__m9KY3"},"source":["Now let's print the masked out example - you should see only the answer is present:"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":174},"id":"_rD6fl8EUxnG","outputId":"978681b5-23be-488c-f8d8-e0e5395ee129","executionInfo":{"status":"ok","timestamp":1781591319793,"user_tz":-330,"elapsed":8,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\" Here's a code that meets the given requirements:\\n\\n```python\\ndef find_common_elements(list1, list2):\\n common_elements = set(list1) & set(list2)\\n max_frequency = 0\\n \\n for element in common_elements:\\n frequency = min(list1.count(element), list2.count(element))\\n max_frequency = max(max_frequency, frequency)\\n \\n return list(common_elements), max_frequency\\n\\nlist1 = [3, 6, 9, 12, 9, 3, 6, 9, 12, 9]\\nlist2 = [2, 4, 5, 6, 8, 9, 9, 3, 6, 9, 12, 9]\\ncommon_elements, max_frequency = find_common_elements(list1, list2)\\nprint(common_elements) # Output: [3, 6, 9, 12]\\nprint(max_frequency) # Output: 4\\n```\\n\\nExplanation:\\n1. First, we find the common elements between `list1` and `list2` by converting both lists into sets and using the `&` operator to get the intersection.\\n2. Then, we iterate over the common elements and calculate their frequency in both lists using the `count()` method. We take the minimum of these frequencies to ensure that we are considering only the common elements once.\\n3. We update the `max_frequency` variable if we find a higher frequency while iterating.\\n4. Finally, we return the common elements as a list and the maximum frequency.<|end_of_text|>\\n\""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":21}],"source":["tokenizer.decode([tokenizer.pad_token_id if x == -100 else x for x in trainer.train_dataset[100][\"labels\"]]).replace(tokenizer.pad_token, \" \")"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/"},"id":"2ejIt2xSNKKp","outputId":"d9ad5322-52f8-40a6-b877-b9e7cefaed85","executionInfo":{"status":"ok","timestamp":1781591319843,"user_tz":-330,"elapsed":27,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["GPU = Tesla T4. Max memory = 14.563 GB.\n","6.762 GB of memory reserved.\n"]}],"source":["# @title Show current memory stats\n","gpu_stats = torch.cuda.get_device_properties(0)\n","start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n","max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)\n","print(f\"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.\")\n","print(f\"{start_gpu_memory} GB of memory reserved.\")"]},{"cell_type":"markdown","metadata":{"id":"CNP1Uidk9mrz"},"source":["Let's train the model! To resume a training run, set `trainer.train(resume_from_checkpoint = True)`\n","\n","```\n","Notice you might have to wait ~10 minutes for the Mamba kernels to compile! Please be patient!\n","```"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"yqxqAZ7KJ4oL","outputId":"e84f93a0-f72c-427c-de9c-36c18e75c10f","executionInfo":{"status":"ok","timestamp":1781592883071,"user_tz":-330,"elapsed":1546744,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n"," \\\\ /| Num examples = 558,233 | Num Epochs = 1 | Total steps = 100\n","O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 8\n","\\ / Data Parallel GPUs = 1 | Total batch size (1 x 8 x 1) = 8\n"," \"-____-\" Trainable parameters = 98,959,360 of 8,479,510,528 (1.17% trained)\n"]},{"output_type":"stream","name":"stdout","text":["Unsloth: Double buffering enabled (parallel H2D + compute) for backward pass.\n","Unsloth: Will smartly offload gradients to save VRAM!\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [100/100 24:56, Epoch 0/1]\n"," </div>\n"," <table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: left;\">\n"," <th>Step</th>\n"," <th>Training Loss</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <td>1</td>\n"," <td>1.332300</td>\n"," </tr>\n"," <tr>\n"," <td>2</td>\n"," <td>2.205000</td>\n"," </tr>\n"," <tr>\n"," <td>3</td>\n"," <td>0.964900</td>\n"," </tr>\n"," <tr>\n"," <td>4</td>\n"," <td>1.526500</td>\n"," </tr>\n"," <tr>\n"," <td>5</td>\n"," <td>0.959000</td>\n"," </tr>\n"," <tr>\n"," <td>6</td>\n"," <td>0.967600</td>\n"," </tr>\n"," <tr>\n"," <td>7</td>\n"," <td>0.953000</td>\n"," </tr>\n"," <tr>\n"," <td>8</td>\n"," <td>0.394500</td>\n"," </tr>\n"," <tr>\n"," <td>9</td>\n"," <td>0.835900</td>\n"," </tr>\n"," <tr>\n"," <td>10</td>\n"," <td>0.796500</td>\n"," </tr>\n"," <tr>\n"," <td>11</td>\n"," <td>1.114300</td>\n"," </tr>\n"," <tr>\n"," <td>12</td>\n"," <td>0.575500</td>\n"," </tr>\n"," <tr>\n"," <td>13</td>\n"," <td>0.553300</td>\n"," </tr>\n"," <tr>\n"," <td>14</td>\n"," <td>0.947900</td>\n"," </tr>\n"," <tr>\n"," <td>15</td>\n"," <td>0.729600</td>\n"," </tr>\n"," <tr>\n"," <td>16</td>\n"," <td>0.563200</td>\n"," </tr>\n"," <tr>\n"," <td>17</td>\n"," <td>1.202900</td>\n"," </tr>\n"," <tr>\n"," <td>18</td>\n"," <td>0.456500</td>\n"," </tr>\n"," <tr>\n"," <td>19</td>\n"," <td>1.336400</td>\n"," </tr>\n"," <tr>\n"," <td>20</td>\n"," <td>0.970500</td>\n"," </tr>\n"," <tr>\n"," <td>21</td>\n"," <td>0.762700</td>\n"," </tr>\n"," <tr>\n"," <td>22</td>\n"," <td>1.057000</td>\n"," </tr>\n"," <tr>\n"," <td>23</td>\n"," <td>0.505900</td>\n"," </tr>\n"," <tr>\n"," <td>24</td>\n"," <td>0.836100</td>\n"," </tr>\n"," <tr>\n"," <td>25</td>\n"," <td>0.953100</td>\n"," </tr>\n"," <tr>\n"," <td>26</td>\n"," <td>0.265900</td>\n"," </tr>\n"," <tr>\n"," <td>27</td>\n"," <td>0.770000</td>\n"," </tr>\n"," <tr>\n"," <td>28</td>\n"," <td>0.854700</td>\n"," </tr>\n"," <tr>\n"," <td>29</td>\n"," <td>0.724200</td>\n"," </tr>\n"," <tr>\n"," <td>30</td>\n"," <td>1.157200</td>\n"," </tr>\n"," <tr>\n"," <td>31</td>\n"," <td>0.784200</td>\n"," </tr>\n"," <tr>\n"," <td>32</td>\n"," <td>1.388900</td>\n"," </tr>\n"," <tr>\n"," <td>33</td>\n"," <td>0.551500</td>\n"," </tr>\n"," <tr>\n"," <td>34</td>\n"," <td>0.842900</td>\n"," </tr>\n"," <tr>\n"," <td>35</td>\n"," <td>0.781700</td>\n"," </tr>\n"," <tr>\n"," <td>36</td>\n"," <td>0.268500</td>\n"," </tr>\n"," <tr>\n"," <td>37</td>\n"," <td>1.295500</td>\n"," </tr>\n"," <tr>\n"," <td>38</td>\n"," <td>0.751100</td>\n"," </tr>\n"," <tr>\n"," <td>39</td>\n"," <td>0.920600</td>\n"," </tr>\n"," <tr>\n"," <td>40</td>\n"," <td>0.635000</td>\n"," </tr>\n"," <tr>\n"," <td>41</td>\n"," <td>0.158600</td>\n"," </tr>\n"," <tr>\n"," <td>42</td>\n"," <td>1.095300</td>\n"," </tr>\n"," <tr>\n"," <td>43</td>\n"," <td>1.120300</td>\n"," </tr>\n"," <tr>\n"," <td>44</td>\n"," <td>0.354300</td>\n"," </tr>\n"," <tr>\n"," <td>45</td>\n"," <td>0.380500</td>\n"," </tr>\n"," <tr>\n"," <td>46</td>\n"," <td>0.378500</td>\n"," </tr>\n"," <tr>\n"," <td>47</td>\n"," <td>0.661500</td>\n"," </tr>\n"," <tr>\n"," <td>48</td>\n"," <td>0.914100</td>\n"," </tr>\n"," <tr>\n"," <td>49</td>\n"," <td>0.829100</td>\n"," </tr>\n"," <tr>\n"," <td>50</td>\n"," <td>1.189600</td>\n"," </tr>\n"," <tr>\n"," <td>51</td>\n"," <td>0.441100</td>\n"," </tr>\n"," <tr>\n"," <td>52</td>\n"," <td>0.439000</td>\n"," </tr>\n"," <tr>\n"," <td>53</td>\n"," <td>1.329000</td>\n"," </tr>\n"," <tr>\n"," <td>54</td>\n"," <td>0.691100</td>\n"," </tr>\n"," <tr>\n"," <td>55</td>\n"," <td>1.219000</td>\n"," </tr>\n"," <tr>\n"," <td>56</td>\n"," <td>0.519600</td>\n"," </tr>\n"," <tr>\n"," <td>57</td>\n"," <td>0.851900</td>\n"," </tr>\n"," <tr>\n"," <td>58</td>\n"," <td>0.405600</td>\n"," </tr>\n"," <tr>\n"," <td>59</td>\n"," <td>1.125900</td>\n"," </tr>\n"," <tr>\n"," <td>60</td>\n"," <td>0.805700</td>\n"," </tr>\n"," <tr>\n"," <td>61</td>\n"," <td>1.016100</td>\n"," </tr>\n"," <tr>\n"," <td>62</td>\n"," <td>1.516500</td>\n"," </tr>\n"," <tr>\n"," <td>63</td>\n"," <td>0.489600</td>\n"," </tr>\n"," <tr>\n"," <td>64</td>\n"," <td>1.152600</td>\n"," </tr>\n"," <tr>\n"," <td>65</td>\n"," <td>0.791000</td>\n"," </tr>\n"," <tr>\n"," <td>66</td>\n"," <td>1.067700</td>\n"," </tr>\n"," <tr>\n"," <td>67</td>\n"," <td>0.695300</td>\n"," </tr>\n"," <tr>\n"," <td>68</td>\n"," <td>0.251600</td>\n"," </tr>\n"," <tr>\n"," <td>69</td>\n"," <td>0.778000</td>\n"," </tr>\n"," <tr>\n"," <td>70</td>\n"," <td>0.301500</td>\n"," </tr>\n"," <tr>\n"," <td>71</td>\n"," <td>0.891900</td>\n"," </tr>\n"," <tr>\n"," <td>72</td>\n"," <td>0.617200</td>\n"," </tr>\n"," <tr>\n"," <td>73</td>\n"," <td>1.130700</td>\n"," </tr>\n"," <tr>\n"," <td>74</td>\n"," <td>0.730400</td>\n"," </tr>\n"," <tr>\n"," <td>75</td>\n"," <td>0.682100</td>\n"," </tr>\n"," <tr>\n"," <td>76</td>\n"," <td>0.950800</td>\n"," </tr>\n"," <tr>\n"," <td>77</td>\n"," <td>0.619800</td>\n"," </tr>\n"," <tr>\n"," <td>78</td>\n"," <td>1.197800</td>\n"," </tr>\n"," <tr>\n"," <td>79</td>\n"," <td>0.942400</td>\n"," </tr>\n"," <tr>\n"," <td>80</td>\n"," <td>0.356800</td>\n"," </tr>\n"," <tr>\n"," <td>81</td>\n"," <td>0.893200</td>\n"," </tr>\n"," <tr>\n"," <td>82</td>\n"," <td>1.320800</td>\n"," </tr>\n"," <tr>\n"," <td>83</td>\n"," <td>1.352300</td>\n"," </tr>\n"," <tr>\n"," <td>84</td>\n"," <td>0.928000</td>\n"," </tr>\n"," <tr>\n"," <td>85</td>\n"," <td>1.023300</td>\n"," </tr>\n"," <tr>\n"," <td>86</td>\n"," <td>0.690300</td>\n"," </tr>\n"," <tr>\n"," <td>87</td>\n"," <td>0.567300</td>\n"," </tr>\n"," <tr>\n"," <td>88</td>\n"," <td>0.660300</td>\n"," </tr>\n"," <tr>\n"," <td>89</td>\n"," <td>0.470400</td>\n"," </tr>\n"," <tr>\n"," <td>90</td>\n"," <td>0.709600</td>\n"," </tr>\n"," <tr>\n"," <td>91</td>\n"," <td>1.263600</td>\n"," </tr>\n"," <tr>\n"," <td>92</td>\n"," <td>0.371200</td>\n"," </tr>\n"," <tr>\n"," <td>93</td>\n"," <td>1.429300</td>\n"," </tr>\n"," <tr>\n"," <td>94</td>\n"," <td>0.515100</td>\n"," </tr>\n"," <tr>\n"," <td>95</td>\n"," <td>0.791100</td>\n"," </tr>\n"," <tr>\n"," <td>96</td>\n"," <td>0.798600</td>\n"," </tr>\n"," <tr>\n"," <td>97</td>\n"," <td>0.906400</td>\n"," </tr>\n"," <tr>\n"," <td>98</td>\n"," <td>0.881200</td>\n"," </tr>\n"," <tr>\n"," <td>99</td>\n"," <td>0.947500</td>\n"," </tr>\n"," <tr>\n"," <td>100</td>\n"," <td>1.185400</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}}],"source":["Strainer_stats = trainer.train()"]},{"cell_type":"code","execution_count":null,"metadata":{"cellView":"form","colab":{"base_uri":"https://localhost:8080/","height":228},"id":"pCqnaKmlO1U9","outputId":"fed46085-467e-47c2-cd0e-def7f1e6f9a7","executionInfo":{"status":"error","timestamp":1781592928170,"user_tz":-330,"elapsed":37,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'trainer_stats' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)","\u001b[0;32m/tmp/ipykernel_1391/828055189.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mused_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmax_memory\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mlora_percentage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mused_memory_for_lora\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mmax_memory\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{trainer_stats.metrics['train_runtime']} seconds used for training.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m print(\n\u001b[1;32m 8\u001b[0m \u001b[0;34mf\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'trainer_stats' is not defined"]}],"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(\n"," f\"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training.\"\n",")\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":["<a name=\"Inference\"></a>\n","### Inference\n","Let's run the model via Unsloth native inference! We'll use some example snippets not contained in our training data to get a sense of what was learned."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gk4A3U4l-SjD"},"outputs":[],"source":["# @title Test Scenarios\n","# --- Scenario 1: Video-Conferencing Screen-Share Bug (11 turns) ---\n","scenario_1 = \"\"\"\n","Write a Python function that finds duplicate elements in a list.\n","\n","Requirements:\n","- Return unique duplicates only\n","- Time complexity should be O(n)\n","- Add type hints\n","\"\"\"\n","# --- Scenario 2: Smart-Lock Low-Battery Loop (9 turns) ---\n","scenario_2 = \"\"\"\n","Create a FastAPI endpoint for uploading PDF files.\n","\n","Requirements:\n","- Accept only PDF files\n","- Save file to uploads directory\n","- Return file name and size\n","- Handle exceptions properly\n","\"\"\"\n","\n","# --- Scenario 3: Accounting SaaS — Corrupted Invoice Export (10 turns) ---\n","scenario_3 = \"\"\"\n","Fix the following Python code:\n","\n","def divide(a,b):\n"," return a/b\n","\n","print(divide(10,0))\n","\n","Explain the issue and provide corrected code.\n","\"\"\"\n","\n","# --- Scenario 4: Fitness-Tracker App — Stuck Step Count (8 turns) ---\n","scenario_4 = \"\"\"\n","Write pytest unit tests for the following function:\n","\n","def add(a,b):\n"," return a+b\n","\n","Include:\n","- Positive numbers\n","- Negative numbers\n","- Zero values\n","\"\"\"\n","\n","# --- Scenario 5: Online-Course Platform — Quiz Submission Error (12 turns) ---\n","scenario_5 = \"\"\"\n","Create a production-ready FastAPI API for therapist profile creation.\n","\n","Requirements:\n","- Pydantic request model\n","- MongoDB repository layer\n","- Proper exception handling\n","- Response model\n","- Async implementation\n","\"\"\""]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kR3gIAX-SM2q","outputId":"83eeffb4-b528-4293-a182-091969544d8e","executionInfo":{"status":"ok","timestamp":1781592972695,"user_tz":-330,"elapsed":32927,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["def find_duplicates(lst: List[Any]) -> List[Any]:\n"," \"\"\"Find duplicate elements in a list.\n","\n"," Args:\n"," lst (List[Any]): List of elements to check for duplicates.\n","\n"," Returns:\n"," List[Any]: List of unique duplicate elements.\n","\n"," Time complexity: O(n)\n"," \"\"\"\n"," seen = set()\n"," duplicates = set()\n"," for item in lst:\n"," if item in seen:\n"," duplicates.add(item)\n"," else:\n"," seen.add(item)\n"," return list(duplicates)\n"]}],"source":["FastLanguageModel.for_inference(model)\n","\n","messages = [\n"," {\"role\": \"user\", \"content\": scenario_1},\n","]\n","\n","inputs = tokenizer.apply_chat_template(\n"," messages,\n"," tokenize=True,\n"," add_generation_prompt=True,\n"," return_tensors=\"pt\",\n"," return_dict=True,\n",").to(\"cuda\")\n","\n","outputs = model.generate(\n"," **inputs,\n"," max_new_tokens=512,\n"," use_cache=True,\n"," do_sample=False,\n",")\n","\n","input_length = inputs[\"input_ids\"].shape[1]\n","\n","response = tokenizer.decode(\n"," outputs[0][input_length:],\n"," skip_special_tokens=True\n",")\n","\n","print(response)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IGSmEnAd-sOP","outputId":"cbf0b5a0-d629-4b20-a409-b59da2df5110","executionInfo":{"status":"ok","timestamp":1781593091267,"user_tz":-330,"elapsed":60881,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Here is an example of a FastAPI endpoint for uploading PDF files:\n","\n","```python\n","from fastapi import FastAPI, UploadFile, File\n","from fastapi.responses import JSONResponse\n","import os\n","\n","app = FastAPI()\n","\n","UPLOAD_FOLDER = 'uploads'\n","\n","@app.post('/upload')\n","async def upload_file(file: UploadFile = File(...)):\n"," if not file.filename.endswith('.pdf'):\n"," return JSONResponse(status_code=400, content='Only PDF files are allowed.')\n","\n"," try:\n"," with open(os.path.join(UPLOAD_FOLDER, file.filename), 'wb+') as f:\n"," f.write(file.file.read())\n"," return JSONResponse(status_code=200, content={'filename': file.filename, 'size': os.path.getsize(os.path.join(UPLOAD_FOLDER, file.filename))})\n"," except Exception as e:\n"," return JSONResponse(status_code=500, content=str(e))\n","```\n","\n","In this example, we define a FastAPI endpoint `/upload` that accepts only PDF files. The `UploadFile` parameter is used to specify that the endpoint expects a file upload. We check if the file extension is `.pdf` and return a 400 status code if it is not.\n","\n","Inside the endpoint, we use a try-except block to handle any exceptions that may occur during the file upload process. We save the file to the `uploads` directory using the `open` function and write the file contents to the file using the `write` method. Finally, we return a JSON response with the file name and size.\n","\n","Note: Make sure to create the `uploads` directory before running the code.\n"]}],"source":["FastLanguageModel.for_inference(model)\n","\n","messages = [\n"," {\"role\": \"user\", \"content\": scenario_2},\n","]\n","\n","inputs = tokenizer.apply_chat_template(\n"," messages,\n"," tokenize=True,\n"," add_generation_prompt=True,\n"," return_tensors=\"pt\",\n"," return_dict=True,\n",").to(\"cuda\")\n","\n","outputs = model.generate(\n"," **inputs,\n"," max_new_tokens=512,\n"," use_cache=True,\n"," do_sample=False,\n",")\n","\n","input_length = inputs[\"input_ids\"].shape[1]\n","\n","response = tokenizer.decode(\n"," outputs[0][input_length:],\n"," skip_special_tokens=True\n",")\n","\n","print(response)"]},{"cell_type":"markdown","metadata":{"id":"uMuVrWbjAzhc"},"source":["<a name=\"Save\"></a>\n","### Saving, loading finetuned models\n","To save the final model as LoRA adapters, either use Hugging Face'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":"b05fba81-d014-47ee-caa3-87ac519e392f","executionInfo":{"status":"ok","timestamp":1781593118870,"user_tz":-330,"elapsed":19364,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["('granite_python_lora/tokenizer_config.json',\n"," 'granite_python_lora/special_tokens_map.json',\n"," 'granite_python_lora/chat_template.jinja',\n"," 'granite_python_lora/vocab.json',\n"," 'granite_python_lora/merges.txt',\n"," 'granite_python_lora/added_tokens.json',\n"," 'granite_python_lora/tokenizer.json')"]},"metadata":{},"execution_count":28}],"source":["model.save_pretrained(\"granite_python_lora\")\n","tokenizer.save_pretrained(\"granite_python_lora\")\n","# model.push_to_hub(\"your_name/granite_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n","# tokenizer.push_to_hub(\"your_name/granite_lora\", token = \"YOUR_HF_TOKEN\") # Online saving"]},{"cell_type":"code","source":["!zip -r granite_python_lora.zip granite_python_lora"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"GCjNd6gD--EC","executionInfo":{"status":"ok","timestamp":1781593182749,"user_tz":-330,"elapsed":23632,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"378a08a7-d46d-4055-ba4f-e9a7572461db"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":[" adding: granite_python_lora/ (stored 0%)\n"," adding: granite_python_lora/tokenizer_config.json (deflated 95%)\n"," adding: granite_python_lora/adapter_config.json (deflated 60%)\n"," adding: granite_python_lora/README.md (deflated 65%)\n"," adding: granite_python_lora/special_tokens_map.json (deflated 79%)\n"," adding: granite_python_lora/merges.txt (deflated 50%)\n"," adding: granite_python_lora/adapter_model.safetensors (deflated 7%)\n"," adding: granite_python_lora/chat_template.jinja (deflated 79%)\n"," adding: granite_python_lora/vocab.json (deflated 56%)\n"," adding: granite_python_lora/tokenizer.json (deflated 80%)\n"]}]},{"cell_type":"code","source":["!ls -lh granite_python_lora.zip"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NOc957KI-_JT","executionInfo":{"status":"ok","timestamp":1781593204149,"user_tz":-330,"elapsed":87,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"81e0bcea-b88e-42ca-c8c3-d2b2e3fe8713"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["-rw-r--r-- 1 root root 352M Jun 16 06:59 granite_python_lora.zip\n"]}]},{"cell_type":"code","source":["# from google.colab import drive\n","# drive.mount('/content/drive')\n","\n","# !cp granite_python_lora.zip /content/drive/MyDrive/\n","print(model)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-rVMdAXJ--67","executionInfo":{"status":"ok","timestamp":1781593283983,"user_tz":-330,"elapsed":26,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"14a3779c-53cc-4bfe-d3d5-6760416362e2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["PeftModelForCausalLM(\n"," (base_model): LoraModel(\n"," (model): GraniteForCausalLM(\n"," (model): GraniteModel(\n"," (embed_tokens): Embedding(100352, 4096, padding_idx=100256)\n"," (layers): ModuleList(\n"," (0-39): 40 x GraniteDecoderLayer(\n"," (self_attn): GraniteAttention(\n"," (q_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (k_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=1024, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (v_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=1024, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (o_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," )\n"," (mlp): GraniteMLP(\n"," (gate_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=12800, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=12800, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (up_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=4096, out_features=12800, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=4096, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=12800, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (down_proj): lora.Linear4bit(\n"," (base_layer): Linear4bit(in_features=12800, out_features=4096, bias=False)\n"," (lora_dropout): ModuleDict(\n"," (default): Identity()\n"," )\n"," (lora_A): ModuleDict(\n"," (default): Linear(in_features=12800, out_features=32, bias=False)\n"," )\n"," (lora_B): ModuleDict(\n"," (default): Linear(in_features=32, out_features=4096, bias=False)\n"," )\n"," (lora_embedding_A): ParameterDict()\n"," (lora_embedding_B): ParameterDict()\n"," (lora_magnitude_vector): ModuleDict()\n"," )\n"," (act_fn): SiLU()\n"," )\n"," (input_layernorm): GraniteRMSNorm((4096,), eps=1e-05)\n"," (post_attention_layernorm): GraniteRMSNorm((4096,), eps=1e-05)\n"," )\n"," )\n"," (norm): GraniteRMSNorm((4096,), eps=1e-05)\n"," (rotary_emb): GraniteRotaryEmbedding()\n"," )\n"," (lm_head): Linear(in_features=4096, out_features=100352, bias=False)\n"," )\n"," )\n",")\n"]}]},{"cell_type":"code","source":["!du -sh ~/.cache/huggingface"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Q-cLX2OF-xFj","executionInfo":{"status":"ok","timestamp":1781593351369,"user_tz":-330,"elapsed":13,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"7116aec8-af8d-42cd-df45-0eb7d5e2ab43"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["23G\t/root/.cache/huggingface\n"]}]},{"cell_type":"code","source":["!rm -rf ~/.cache/huggingface"],"metadata":{"id":"rKrOQz4_-zv7"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"AEEcJ4qfC7Lp"},"source":["Now if you want to load the LoRA adapters we\n","\n","---\n","\n","just saved for inference, set `False` to `True`:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MKX_XKs_BNZR"},"outputs":[],"source":["if False:\n"," from unsloth import FastLanguageModel\n"," model, tokenizer = FastLanguageModel.from_pretrained(\n"," model_name = \"ibm-granite/granite-4.1-8b\", # YOUR MODEL YOU USED FOR TRAINING\n"," max_seq_length = 4096,\n"," load_in_4bit = True,\n"," )"]},{"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. See [our docs](https://unsloth.ai/docs/basics/inference-and-deployment) for more deployment options."]},{"cell_type":"code","source":["model.save_pretrained_gguf(\n"," \"granite_finetune_q4\",\n"," tokenizer,\n"," quantization_method=\"q4_k_m\"\n",")"],"metadata":{"id":"kqh6cMx5wIcW","executionInfo":{"status":"ok","timestamp":1781596273867,"user_tz":-330,"elapsed":2902377,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"17244f1a-9938-4c94-9aab-af012ab70e64","colab":{"base_uri":"https://localhost:8080/","height":958,"referenced_widgets":["32a71aa776a84516949147816b35e96a","a1d7da45f363429ab49aeb5a706a02f3","4e0735fe0f6b412a811c341d5bda09fc","8e1cef220ac54daca3792d318bec7511","f908e7a71a6a4806b418fb7082d1daca","e42427a25df04764bcb6148f499dfaf1","56b9ddcec5104391a095d7a08834b299","2342fefd044246d2a6688a3a6cf68805","781ecc7078ac4fb5996c620706c705c5","d20d86ef86a646bfbaf3f03064ddfe8b","7e9c1651eae642429b5a7aaa4f0c3708","a2ecda757962478582460c9ef3d72e61","e0d5b132451d40f58eba3514ce6a5a34","d4fd663f8f8046dd9547ce196aac1163","f4baedf4ba3f4b83ae7e2f813952132a","3543fd57b8d3495095f3b303fdfd570c","11f4fee7184b4f119be2f64692d3e66d","3f5dfb40490147d0a3961badb8e191c0","765499d2780449d88c3f764ba9e0e807","6c54f14d0e43440aae20d78854f67266","bdc30b06c9904ec29405126d109b049c","7738006781f24dddb446a31dace0b70d","8c90c568806e4a1692b5b58d15c37983","ec33c3f2332e4dadb769ae90ceeec30b","0c90314c481942a6b95d6c5be4bc5931","43a89b24764c4bd784b366d527603e1e","178adad2edd1400ab27eaa5994f99bd2","b6e57ee79e3747528823cb1ff0ed3117","a04fc9f71fd54e95b84d9d173947202c","c67fa3ec375f42b49fa8171dccd1b0a3","3e25c76247e0491fadd07e82284086af","9a497eb9e76246fbb827c26274114659","83b6bd0fc30a41269ddb064b2a63cf5c","01eb0aa9d3484a18a0f83027972d79cd","5959386c59214f3dad51dd0e0ecc896d","4b9c53dc9d6c4438860514a9f8340371","cd97cb47681e4f86a04f322785cfe4fa","c729730b9b2d4734847d803a22eeec46","15de2634bd16420d862cf0b124dd0692","be1d10165edc4df8af7ffe7651734a36","f6e687f08036404a88f203fdfcf56015","5bff122173e84ea896ce6ee90e7e6164","ee7c3cd0b2cd4132a1ec61ef4df1c9e7","34dc888cfe854f30b3eb08517aebabd1","82c0acfc51224edca08e157a8c83fb06","db79abf8547b46f2ab558ddd981987ba","c2bd7c99dbe14563a2d8495a35aa49d9","e27fced3489f472cba1a265dff2ada66","77130cf0dbbe4671ae854c8c31b467d0","45ca85896f954b1e84e418952c0777ea","5cab5bffbf8840028dd42a9b53f13b12","3ff4de07622445219cd87f8b19fd5b80","d242115366094b75a58f7c4c85306d98","647ae9f7564f473389ad87cd832ee912","c0e50feb429640f4afb1a1190abc050d","1502d9044e4f4cd198e45cb61a2d941d","cd1d80c22919426cb97d9904f36cca29","343b5c6c6f6942469b5e44f03b376e58","35c48cf4c33749e49c3943dd2d74d1f8","85e530a355974f45a2bc7d7722c0143c","c023feef728f44e88fd9948e2d92321a","2e7510cf5bb84a7ab282882909767c06","69020f51f5b546bdb226bea2568b9408","94f005960c844371bb70f8ad500f33ff","fd50179f47b541f6b5b62f0a23a543d3","0bd0a436dc0a47e98ec347937e79350e"]}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Unsloth: Merging model weights to 16-bit format...\n"]},{"output_type":"display_data","data":{"text/plain":["config.json: 0%| | 0.00/803 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"32a71aa776a84516949147816b35e96a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Found HuggingFace hub cache directory: /root/.cache/huggingface/hub\n"]},{"output_type":"display_data","data":{"text/plain":["model.safetensors.index.json: 0.00B [00:00, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a2ecda757962478582460c9ef3d72e61"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Checking cache directory for required files...\n","Cache check failed: model-00001-of-00004.safetensors not found in local cache.\n","Not all required files found in cache. Will proceed with downloading.\n","Checking cache directory for required files...\n","Cache check failed: tokenizer.model not found in local cache.\n","Not all required files found in cache. Will proceed with downloading.\n"]},{"output_type":"stream","name":"stderr","text":["\rUnsloth: Preparing safetensor model files: 0%| | 0/4 [00:00<?, ?it/s]"]},{"output_type":"display_data","data":{"text/plain":["model-00001-of-00004.safetensors: 0%| | 0.00/4.83G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8c90c568806e4a1692b5b58d15c37983"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\rUnsloth: Preparing safetensor model files: 25%|██▌ | 1/4 [04:08<12:26, 248.70s/it]"]},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00004.safetensors: 0%| | 0.00/4.99G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"01eb0aa9d3484a18a0f83027972d79cd"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\rUnsloth: Preparing safetensor model files: 50%|█████ | 2/4 [08:37<08:40, 260.25s/it]"]},{"output_type":"display_data","data":{"text/plain":["model-00003-of-00004.safetensors: 0%| | 0.00/4.97G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"82c0acfc51224edca08e157a8c83fb06"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["\rUnsloth: Preparing safetensor model files: 75%|███████▌ | 3/4 [12:57<04:20, 260.13s/it]"]},{"output_type":"display_data","data":{"text/plain":["model-00004-of-00004.safetensors: 0%| | 0.00/2.79G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1502d9044e4f4cd198e45cb61a2d941d"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Unsloth: Preparing safetensor model files: 100%|██████████| 4/4 [15:04<00:00, 226.13s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Note: tokenizer.model not found (this is OK for non-SentencePiece models)\n"]},{"output_type":"stream","name":"stderr","text":["Unsloth: Merging weights into 16bit: 100%|██████████| 4/4 [07:51<00:00, 118.00s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Unsloth: Merge process complete. Saved to `/content/granite_finetune_q4`\n","Unsloth: Converting to GGUF format...\n","==((====))== Unsloth: Conversion from HF to GGUF information\n"," \\\\ /| [0] Installing llama.cpp might take 3 minutes.\n","O^O/ \\_/ \\ [1] Converting HF to GGUF f16 might take 3 minutes.\n","\\ / [2] Converting GGUF f16 to ['q4_k_m'] 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: Installing prebuilt llama.cpp b9663 (llama-b9663-bin-ubuntu-x64.tar.gz) - skipping compilation.\n","Unsloth: Preparing converter script...\n","Unsloth: [1] Converting model into f16 GGUF format.\n","This might take 3 minutes...\n","Unsloth: Initial conversion completed! Files: ['granite_finetune_q4_gguf/granite-4.1-8b.F16.gguf']\n","Unsloth: [2] Converting GGUF f16 into q4_k_m. This might take 10 minutes...\n","Unsloth: Model files cleanup...\n","Unsloth: All GGUF conversions completed successfully!\n","Generated files: ['granite_finetune_q4_gguf/granite-4.1-8b.Q4_K_M.gguf']\n","Unsloth: No Ollama template mapping found for model 'ibm-granite/granite-4.1-8b'. Skipping Ollama Modelfile\n","Unsloth: example usage for text only LLMs: /root/.unsloth/llama.cpp/llama-cli --model granite_finetune_q4_gguf/granite-4.1-8b.Q4_K_M.gguf -p \"why is the sky blue?\"\n"]},{"output_type":"execute_result","data":{"text/plain":["{'save_directory': 'granite_finetune_q4',\n"," 'gguf_directory': 'granite_finetune_q4_gguf',\n"," 'gguf_files': ['granite_finetune_q4_gguf/granite-4.1-8b.Q4_K_M.gguf'],\n"," 'modelfile_location': None,\n"," 'want_full_precision': False,\n"," 'is_vlm': False,\n"," 'fix_bos_token': False}"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["!ls -lh granite_finetune_q4_gguf"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"r8jGSQ9shndV","executionInfo":{"status":"ok","timestamp":1781598159944,"user_tz":-330,"elapsed":472,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"fecf97ae-0a00-41b4-9352-075bfdf03eb9"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["total 5.0G\n","-rw-r--r-- 1 root root 5.0G Jun 16 07:51 granite-4.1-8b.Q4_K_M.gguf\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o5b50SbuhvWh","executionInfo":{"status":"ok","timestamp":1781598224663,"user_tz":-330,"elapsed":31262,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"3f6aeaf2-afd3-42a7-a148-91d742a7d5ae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"code","source":["!mkdir -p /content/drive/MyDrive/unsloths"],"metadata":{"id":"Nlsx1pkmh2JK"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!cp granite_finetune_q4_gguf/*.gguf /content/drive/MyDrive/unsloths/"],"metadata":{"id":"TGMjzh0Bvjv1"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["config = model.config\n","\n","print(\"Layers:\", config.num_hidden_layers)\n","print(\"Attention Heads:\", config.num_attention_heads)\n","print(\"Hidden Size:\", config.hidden_size)\n","\n","head_dim = config.hidden_size // config.num_attention_heads\n","print(\"Head Dimension:\", head_dim)\n","\n","print(\"*\" *50)\n","print(model.config)\n","print(\"*\" *50)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"olYb3HGaoLrK","executionInfo":{"status":"ok","timestamp":1781599925829,"user_tz":-330,"elapsed":42,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"823e4e39-8834-459a-8980-28a58d495780"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Layers: 40\n","Attention Heads: 32\n","Hidden Size: 4096\n","Head Dimension: 128\n","**************************************************\n","GraniteConfig {\n"," \"architectures\": [\n"," \"GraniteForCausalLM\"\n"," ],\n"," \"attention_bias\": false,\n"," \"attention_dropout\": 0.0,\n"," \"attention_multiplier\": 0.0078125,\n"," \"bos_token_id\": 100257,\n"," \"dtype\": \"float16\",\n"," \"embedding_multiplier\": 12.0,\n"," \"eos_token_id\": 100257,\n"," \"hidden_act\": \"silu\",\n"," \"hidden_size\": 4096,\n"," \"initializer_range\": 0.1,\n"," \"intermediate_size\": 12800,\n"," \"logits_scaling\": 16.0,\n"," \"max_position_embeddings\": 131072,\n"," \"mlp_bias\": false,\n"," \"model_name\": \"ibm-granite/granite-4.1-8b\",\n"," \"model_type\": \"granite\",\n"," \"num_attention_heads\": 32,\n"," \"num_hidden_layers\": 40,\n"," \"num_key_value_heads\": 8,\n"," \"pad_token_id\": 100256,\n"," \"quantization_config\": {\n"," \"bnb_4bit_compute_dtype\": \"float16\",\n"," \"bnb_4bit_quant_type\": \"nf4\",\n"," \"bnb_4bit_use_double_quant\": true,\n"," \"llm_int8_enable_fp32_cpu_offload\": false,\n"," \"llm_int8_has_fp16_weight\": false,\n"," \"llm_int8_skip_modules\": null,\n"," \"llm_int8_threshold\": 6.0,\n"," \"load_in_4bit\": true,\n"," \"load_in_8bit\": false,\n"," \"quant_method\": \"bitsandbytes\"\n"," },\n"," \"residual_multiplier\": 0.22,\n"," \"rms_norm_eps\": 1e-05,\n"," \"rope_scaling\": null,\n"," \"rope_theta\": 10000000,\n"," \"tie_word_embeddings\": true,\n"," \"transformers_version\": \"4.56.2\",\n"," \"unsloth_version\": \"2026.6.7\",\n"," \"use_cache\": true,\n"," \"vocab_size\": 100352\n","}\n","\n","**************************************************\n"]}]},{"cell_type":"markdown","source":["LoRA Parameter Count\n","\n","If you're curious how many parameters you actually trained:"],"metadata":{"id":"1fgMm7vUo-ea"}},{"cell_type":"code","source":["trainable = sum(\n"," p.numel()\n"," for p in model.parameters()\n"," if p.requires_grad\n",")\n","\n","total = sum(\n"," p.numel()\n"," for p in model.parameters()\n",")\n","\n","print(f\"Trainable: {trainable:,}\")\n","print(f\"Total: {total:,}\")\n","print(f\"% Trained: {100 * trainable / total:.4f}%\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rvoiFFBvo7yo","executionInfo":{"status":"ok","timestamp":1781600091065,"user_tz":-330,"elapsed":46,"user":{"displayName":"sarathkumar ravi","userId":"03114359698583885786"}},"outputId":"266821b8-d97c-4ca4-a8f2-b25f64c3d537"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Trainable: 98,959,360\n","Total: 4,494,921,728\n","% Trained: 2.2016%\n"]}]},{"cell_type":"code","source":["!pip install huggingface_hub\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wdK_b7Cl5TLy","executionInfo":{"status":"ok","timestamp":1781688281711,"user_tz":-330,"elapsed":10019,"user":{"displayName":"Ravi R","userId":"06353650660974763333"}},"outputId":"3bd8ff89-6ca0-4af4-efef-885780a6b28a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (1.18.0)\n","Requirement already satisfied: click>=8.4.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (8.4.1)\n","Requirement already satisfied: filelock>=3.10.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (3.29.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2025.3.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.5.1)\n","Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.28.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (26.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (6.0.3)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.67.3)\n","Requirement already satisfied: typer<0.26.0,>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.25.1)\n","Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.15.0)\n","Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (4.13.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (2026.5.20)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (1.0.9)\n","Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (3.18)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub) (0.16.0)\n","Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (1.5.4)\n","Requirement already satisfied: rich>=13.8.0 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (13.9.4)\n","Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (4.2.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (2.20.0)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (0.1.2)\n"]}]},{"cell_type":"code","source":["# 1. Install the required Hugging Face library\n","\n","import os\n","from google.colab import drive\n","from huggingface_hub import HfApi\n","from google.colab import userdata\n","# 2. Mount your Google Drive\n","# Click the link that appears or follow the pop-up prompt to grant access\n","drive.mount('/content/drive')\n","\n","# 3. Configure your paths and Hugging Face details\n","# UPDATE THESE VARIABLES\n","GDRIVE_FILE_PATH = \"/content/drive/MyDrive/unsloth/grantie-4.1-8b.Q4_K_M.gguf\"\n","HF_REPO_ID = \"sarathrkrishna/python_coding_assistant\"\n","\n","HF_WRITE_TOKEN=userdata.get('hf_token')\n","\n","# Validate that the file actually exists in your Google Drive before continuing\n","if not os.path.exists(GDRIVE_FILE_PATH):\n"," print(f\"❌ Error: Could not find your file at: {GDRIVE_FILE_PATH}\")\n"," print(\"Please double-check your Google Drive folder structure and file name.\")\n","else:\n"," print(\"✅ File located successfully in Google Drive. Starting upload to Hugging Face...\")\n","\n"," # 4. Initialize Hugging Face API Client\n"," api = HfApi()\n","\n"," # 5. Upload the model file directly\n"," try:\n"," api.upload_file(\n"," path_or_fileobj=GDRIVE_FILE_PATH,\n"," path_in_repo=\"python_coding_assistant.gguf\", # Name it appears as on Hugging Face\n"," repo_id=HF_REPO_ID,\n"," repo_type=\"model\",\n"," token=HF_WRITE_TOKEN\n"," )\n"," print(\"\\n🎉 Success! Your model has been uploaded to your Hugging Face repository.\")\n"," print(f\"Check it here: https://huggingface.co{HF_REPO_ID}\")\n"," except Exception as e:\n"," print(f\"\\n❌ Upload failed with error: {e}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":686,"referenced_widgets":["6f8daec501384211b7a450cd9eabc7e6","694802c149f54c9f95a6dd9174d0745b","c2680823090140ea9a10143b40ee4784","e460a06e24744e1e97abec779ef6614a","396a4561d8f546e2b6f00da2342c76c6","f4d3c7cda4bd4b978b70f3bb0fe9c481","950432f176d8404884a8c8272d17a9c5","1dd2f89e8ca04d99bcc5024e6864dc6d","66df2db76d21495491df41de0774d4d7","462a1cf2fd314c45864bf399dd17ae05","3fc11657d6064d71884ac68ebc858e35","4cb603f83fef45aa8e06b3d86a3ebe82","a362010eaba74656b9428f61320e7e63","7a6cea3aab804e16873c4efd6f481d3a","fb69b888713f4061b035f43af7820ff9","16cdfc1adbae49afb60e29b622efc33c","1f6fd8cde2974a59be03d88efa0d6cc2","5189c65184db44ada36fdcc6e002d75b","6d2001ab1e38498cace76cd5c51187e8","324c340364d04f9d84e1d8cc8df67a4d","1dacf59d521b461db54c95cd162dc99e","c577415f088a4e42af59d23bff9d4bf7","acc9ce18f29449d08b796d8baf525e6e","b32dc63388d647f2b3134e589dc14f6f","7687f305f5a740c68a5257f69199f9ea","edf504030c9d44d881c88d3ee3f443dd","abc78e7f84cf43c8ab13234f330138f5","17d9ddc809b449e98ee7c38452706f84","d15613fd5aca4a618549bcf81a6742d4","08bb1838c88d4114a5625b7715f56a7c","4f3575b5a6064c38b5b999ebc8ecdf64","b879f8682db54b99bb6931253fd538c3","84dbaed35990495ca484f31f495ef907"]},"id":"KVdVvfUS5HeD","executionInfo":{"status":"ok","timestamp":1781688619801,"user_tz":-330,"elapsed":180846,"user":{"displayName":"Ravi R","userId":"06353650660974763333"}},"outputId":"5aa0f89b-5459-41a7-dfe4-f1bfabfef551"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.12/dist-packages (1.18.0)\n","Requirement already satisfied: click>=8.4.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (8.4.1)\n","Requirement already satisfied: filelock>=3.10.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (3.29.2)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (2025.3.0)\n","Requirement already satisfied: hf-xet<2.0.0,>=1.4.3 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (1.5.1)\n","Requirement already satisfied: httpx<1,>=0.23.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.28.1)\n","Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (26.2)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (6.0.3)\n","Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.67.3)\n","Requirement already satisfied: typer<0.26.0,>=0.20.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (0.25.1)\n","Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.12/dist-packages (from huggingface_hub) (4.15.0)\n","Requirement already satisfied: anyio in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (4.13.0)\n","Requirement already satisfied: certifi in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (2026.5.20)\n","Requirement already satisfied: httpcore==1.* in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (1.0.9)\n","Requirement already satisfied: idna in /usr/local/lib/python3.12/dist-packages (from httpx<1,>=0.23.0->huggingface_hub) (3.18)\n","Requirement already satisfied: h11>=0.16 in /usr/local/lib/python3.12/dist-packages (from httpcore==1.*->httpx<1,>=0.23.0->huggingface_hub) (0.16.0)\n","Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (1.5.4)\n","Requirement already satisfied: rich>=13.8.0 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (13.9.4)\n","Requirement already satisfied: annotated-doc>=0.0.2 in /usr/local/lib/python3.12/dist-packages (from typer<0.26.0,>=0.20.0->huggingface_hub) (0.0.4)\n","Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (4.2.0)\n","Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (2.20.0)\n","Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=13.8.0->typer<0.26.0,>=0.20.0->huggingface_hub) (0.1.2)\n","Mounted at /content/drive\n","✅ File located successfully in Google Drive. Starting upload to Hugging Face...\n"]},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:112: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0) : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6f8daec501384211b7a450cd9eabc7e6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4cb603f83fef45aa8e06b3d86a3ebe82"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...rantie-4.1-8b.Q4_K_M.gguf: 0%| | 538kB / 5.35GB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"acc9ce18f29449d08b796d8baf525e6e"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["\n","🎉 Success! Your model has been uploaded to your Hugging Face repository.\n","Check it here: https://huggingface.cosarathrkrishna/python_coding_assistant\n"]}]},{"cell_type":"code","source":["from google.colab import userdata\n","# UPDATE THESE PATHS\n","GDRIVE_FOLDER_PATH = \"/content/drive/MyDrive/unsloth\"\n","HF_REPO_ID = \"sarathrkrishna/python_coding_assistant\"\n","HF_WRITE_TOKEN = userdata.get('hf_token')\n","\n","# Validate that the folder actually exists in your Google Drive before continuing\n","api = HfApi()\n","\n","if not os.path.exists(GDRIVE_FOLDER_PATH):\n"," print(f\"❌ Error: Folder not found at {GDRIVE_FOLDER_PATH}\")\n","else:\n"," print(\"🚀 Uploading your scripts and screenshots...\")\n"," try:\n"," api.upload_folder(\n"," folder_path=GDRIVE_FOLDER_PATH,\n"," repo_id=HF_REPO_ID,\n"," repo_type=\"model\",\n"," token=HF_WRITE_TOKEN\n"," )\n"," print(\"🎉 Additional files uploaded successfully!\")\n"," except Exception as e:\n"," print(f\"❌ Upload failed: {e}\")"],"metadata":{"id":"bD6JHxGD8Sod","colab":{"base_uri":"https://localhost:8080/","height":258,"referenced_widgets":["137c26d858e64cec9f1760def2dd2024","1183e7ade24a45e4b0ebfda5e602940c","79a2bf8a3ccf460f8283240adb0cc2b3","16fcbff8507a44ed867f0d03f7eb92f3","bc36fcca27af4df4ae07ddda78a90ea9","9ac4c124616a4d239b8a3be95cd3257d","6fb6ab2475ea47a2839e4f2f2371b46b","03578f33b4514475a8e9a38c571cedf9","5391888e42be49fd9701b269f56a11a6","3ac1e69a32bb4fd4bf2c8abad98223a1","b9d41484d1f44d018497b59046a4463b","a17fc4893107497d84d848188425107f","de217a3bcaa54a529199d42cced0ee11","9c2ed8655ab242d194c1ce305dea2128","77d06b4a69a442ae8a51a2ec966655a0","a2992e86e8984a18ad0fe1fcc1a0502c","6462db9aba1a44f2925e6faf2850a193","1a607feb015a4fc9b486989a0c485f03","a3add39775154430bd910467934267f4","3e4730cdd044483e9048af3456b896fb","20081e9de99b44bda29f5b3ef31f5c5c","f68e1728315c4b73a88be7f3f8260520","91717c9fb96349e8ab3ab7d974e78beb","8d3c565bc21d43449e69fecc7fccf447","fbc2668b69f445c09493b3bd19e96ac6","9938dddf655e497db6a3dc90423f1e98","fbc66a053d1949459220e00dfc849c40","d63db31927594627a5099ca56fcb11bc","fb6a6f48738145ea91e09e1d527ca937","c06844ba158a464e9cd41b4842c975d6","be4e67ca76f74d5ab6ed845855d4651b","0c57e4f6797b412bb95778c023414d22","8525386003f3422b93058f43ee2c9b94","a69211a3955d4cda8ece93e02cf41e3e","38dc9bee49104b7388f92f0c8bcf0d99","8b4b371e5fbf417aa741d945d8eb0ae6","1854b810ba5145879467b0270682d9be","f9704d16e38349c0a0488b1620f6884b","8c7a2f197cbc41939a0f7cc3388d92a6","eadbb1d708bd45ba943a11502b332616","76f625bfbf474e9f9ed6d79e7b557005","a4e11ae4a06541759674730363092ef3","4f8a074244d04937a776bd4c289bab3b","d992041dfbf54b58ba499c6ecef87e52","67736e60ae0f484e87504d6b1128ae9d","9f09b198248b4f2d8c4fcbd0d3f13121","62b5bb4f42ab496e9f2af81e746cefba","5b018ba0fdb14338944a1eb1d6335ab1","9ce7845f69fe40abb1106eceee1dcc00","6ff76d52aa54477e84acc96de847de94","3570bc15b318499aba921b522ce947b9","75207f3d3be14b55be85cdde8cb4d0dc","6e5f1297f6794113ae6f8789cec8b1f6","8ca58e2a51fb436b9b4de05315922a83","8706030e411846e68bd0a80b2b59936f","a2737bfd4d0f433d91ce0ee58c48d4c6","132263356ecd4155b54d52517f95b099","ef6aee28b5164224a7d22b7db1b8872d","74b83b5e005a43b19c466179027ad405","30290e118f6f47eca8e817d09320533f","fbcf4352310a4716a1fa4cb8380b9779","80494071424c4ca8a7ec3764183aaca7","c2420431e32a4a36b73a8d5eed5ccd15","858a120cdfb34faa829a8dd36d36ddc8","745a39047d4a49c1acc492c846109838","a9e139157ba444bc97a9bfb6ff2fbdc1","49a53d690cd54a70869be111757991fb","25f46ae304d84eccb0b12f7322e083d9","99d2a8ced6ea4c0c9cfa5678f923d1ce","5ba8109776844027bb2226b663558f9a","a58859a2c87349d9a544836b03618121","0ec5bbd9cc36402fa50559ae5b7bec45","0ada67d1815744a893499fbf8c970a91","16191bb6c9484559ae42911b43fb0aa5","9a7c417d454640adb2e0e7985a5802a4","9b8330f7bdf54c9bb1b6c79ba5b58c5b","3ccfdd23cd6b413e937312e8b352e36e"]},"outputId":"d33efe79-3f10-4ffb-a1b4-4505fda8de64"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["🚀 Uploading your scripts and screenshots...\n"]},{"output_type":"display_data","data":{"text/plain":["Processing Files (0 / 0) : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"137c26d858e64cec9f1760def2dd2024"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["New Data Upload : | | 0.00B / 0.00B "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a17fc4893107497d84d848188425107f"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...quirement_Calculation.pdf: 100%|##########| 302kB / 302kB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"91717c9fb96349e8ab3ab7d974e78beb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...hot 2026-06-16 183053.png: 48%|####7 | 67.7kB / 141kB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a69211a3955d4cda8ece93e02cf41e3e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...hot 2026-06-16 183528.png: 55%|#####5 | 94.9kB / 172kB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"67736e60ae0f484e87504d6b1128ae9d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...anite_python_lora (1).zip: 4%|4 | 15.9MB / 369MB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a2737bfd4d0f433d91ce0ee58c48d4c6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[" ...rantie-4.1-8b.Q4_K_M.gguf: 1%| | 39.9MB / 5.35GB "],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"49a53d690cd54a70869be111757991fb"}},"metadata":{}}]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iHjt_SMYsd3P"},"outputs":[],"source":["# # Merge to 16bit\n","# if False:\n","# model.save_pretrained_merged(\"granite_finetune_16bit\", tokenizer, save_method = \"merged_16bit\",)\n","# if False: # Pushing to HF Hub\n","# model.push_to_hub_merged(\"HF_USERNAME/granite_finetune_16bit\", tokenizer, save_method = \"merged_16bit\", token = \"YOUR_HF_TOKEN\")\n","\n","# # Merge to 4bit\n","# if False:\n","# model.save_pretrained_merged(\"granite_finetune_4bit\", tokenizer, save_method = \"merged_4bit\",)\n","# if False: # Pushing to HF Hub\n","# model.push_to_hub_merged(\"HF_USERNAME/granite_finetune_4bit\", tokenizer, save_method = \"merged_4bit\", token = \"YOUR_HF_TOKEN\")\n","\n","# # Just LoRA adapters\n","# if False:\n","# model.save_pretrained(\"granite_lora\")\n","# tokenizer.save_pretrained(\"granite_lora\")\n","# if False: # Pushing to HF Hub\n","# model.push_to_hub(\"HF_USERNAME/granite_lora\", token = \"YOUR_HF_TOKEN\")\n","# tokenizer.push_to_hub(\"HF_USERNAME/granite_lora\", token = \"YOUR_HF_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 [docs page](https://unsloth.ai/docs/basics/inference-and-deployment/saving-to-gguf)):\n","* `q8_0` - Fast conversion. High resource use, but generally acceptable.\n","* `q4_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.\n","* `q5_k_m` - Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.\n","\n","[**NEW**] To finetune and auto export to Ollama, try our [Ollama notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)"]},{"cell_type":"markdown","metadata":{"id":"Q974YEVPI7JS"},"source":["Likewise, if you want to instead push to GGUF to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"]},{"cell_type":"code","source":[],"metadata":{"id":"A_fb1RdnwI-O"},"execution_count":null,"outputs":[]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZgcJIhJ0I_es"},"outputs":[],"source":["# # Save to 8bit Q8_0\n","# if False:\n","# model.save_pretrained_gguf(\"granite_finetune\", tokenizer,)\n","# # Remember to go to https://huggingface.co/settings/tokens for a token!\n","# # And change hf to your username!\n","# if False:\n","# model.push_to_hub_gguf(\"HF_USERNAME/granite_finetune\", tokenizer, token = \"YOUR_HF_TOKEN\")\n","\n","# # Save to 16bit GGUF\n","# if False:\n","# model.save_pretrained_gguf(\"granite_finetune\", tokenizer, quantization_method = \"f16\")\n","# if False: # Pushing to HF Hub\n","# model.push_to_hub_gguf(\"HF_USERNAME/granite_finetune\", tokenizer, quantization_method = \"f16\", token = \"YOUR_HF_TOKEN\")\n","\n","# # Save to q4_k_m GGUF\n","# if False:\n","# model.save_pretrained_gguf(\"granite_finetune\", tokenizer, quantization_method = \"q4_k_m\")\n","# if False: # Pushing to HF Hub\n","# model.push_to_hub_gguf(\"HF_USERNAME/granite_finetune\", tokenizer, quantization_method = \"q4_k_m\", token = \"YOUR_HF_TOKEN\")\n","\n","# # Save to multiple GGUF options - much faster if you want multiple!\n","# if False:\n","# model.push_to_hub_gguf(\n","# \"HF_USERNAME/granite_finetune\", # Change hf to your username!\n","# tokenizer,\n","# quantization_method = [\"q4_k_m\", \"q8_0\", \"q5_k_m\",],\n","# token = \"YOUR_HF_TOKEN\", # Get a token at https://huggingface.co/settings/tokens\n","# )"]},{"cell_type":"markdown","metadata":{"id":"jzmjlc3gzJVs"},"source":["Now, use the `granite_finetune.Q8_0.gguf` file or `granite_finetune.Q4_K_M.gguf` file in llama.cpp.\n","\n","And we're done! If you have any questions on Unsloth, we have a [Discord](https://discord.gg/unsloth) channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!\n","\n","Some other resources:\n","1. Train your own reasoning model - Llama GRPO notebook [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb)\n","2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)\n","3. Llama 3.2 Vision finetuning - Radiography use case. [Free Colab](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.2_(11B)-Vision.ipynb)\n","4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!\n","\n","<div class=\"align-center\">\n"," <a href=\"https://unsloth.ai\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/unsloth%20new%20logo.png\" width=\"115\"></a>\n"," <a href=\"https://discord.gg/unsloth\"><img src=\"https://github.com/unslothai/unsloth/raw/main/images/Discord.png\" width=\"145\"></a>\n"," <a href=\"https://unsloth.ai/docs/\"><img src=\"https://github.com/unslothai/unsloth/blob/main/images/documentation%20green%20button.png?raw=true\" width=\"125\"></a>\n","\n"," Join Discord if you need help + ⭐️ <i>Star us on <a href=\"https://github.com/unslothai/unsloth\">Github</a> </i> ⭐️\n","</div>\n","\n"," This notebook and all Unsloth notebooks are licensed [LGPL-3.0](https://github.com/unslothai/notebooks?tab=LGPL-3.0-1-ov-file#readme)."]}],"metadata":{"accelerator":"GPU","colab":{"gpuType":"T4","provenance":[{"file_id":"https://github.com/unslothai/notebooks/blob/main/nb/Granite4.0.ipynb","timestamp":1781522821634}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"},"widgets":{"application/vnd.jupyter.widget-state+json":{"fdfa12e530634012b7ee1b5667590b3b":{"model_module":"@jupyter-widgets/controls","model_name":"HBoxModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HBoxModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HBoxView","box_style":"","children":["IPY_MODEL_f1c73eef13314b8cb8662f3527a5a28b","IPY_MODEL_40dd90487ba1456992772d93718bbda6","IPY_MODEL_0e2dfc1adebb4940ad335e7955426f11"],"layout":"IPY_MODEL_7f3496917c374650a6ec02e3a30af008"}},"f1c73eef13314b8cb8662f3527a5a28b":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_5b6f51c01201413e8b287d241029defa","placeholder":"","style":"IPY_MODEL_bd06df4c9bab4b9190a0c1fb672af0a4","value":"model.safetensors.index.json: "}},"40dd90487ba1456992772d93718bbda6":{"model_module":"@jupyter-widgets/controls","model_name":"FloatProgressModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"FloatProgressModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"ProgressView","bar_style":"success","description":"","description_tooltip":null,"layout":"IPY_MODEL_e0d28f73c4f846088dc87fb8a92f0b58","max":1,"min":0,"orientation":"horizontal","style":"IPY_MODEL_fc9ed7f6311a47f7812d5126704e7782","value":1}},"0e2dfc1adebb4940ad335e7955426f11":{"model_module":"@jupyter-widgets/controls","model_name":"HTMLModel","model_module_version":"1.5.0","state":{"_dom_classes":[],"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"HTMLModel","_view_count":null,"_view_module":"@jupyter-widgets/controls","_view_module_version":"1.5.0","_view_name":"HTMLView","description":"","description_tooltip":null,"layout":"IPY_MODEL_03fcc03cffcc4d7a88c787a71301b001","placeholder":"","style":"IPY_MODEL_47cae1f3ce8a4e62b91b5c44a272596f","value":" 29.9k/? 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