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gemma4-research/tooling/fine-tuning/unsloth/notebooks/Gemma4_(26B_A4B)-Vision.ipynb
T
Mortdecai eecebe7ef5 docs: add canonical tooling corpus (147 files) from Google/HF/frameworks 
Five-lane parallel research pass. Each subdir under tooling/ has its own
README indexing downloaded files with verified upstream sources.

- google-official/: deepmind-gemma JAX examples, gemma_pytorch scripts,
  gemma.cpp API server docs, google-gemma/cookbook notebooks, ai.google.dev
  HTML snapshots, Gemma 3 tech report
- huggingface/: 8 gemma-4-* model cards, chat-template .jinja files,
  tokenizer_config.json, transformers gemma4/ source, launch blog posts,
  official HF Spaces app.py
- inference-frameworks/: vLLM/llama.cpp/MLX/Keras-hub/TGI/Gemini API/Vertex AI
  comparison, run_commands.sh with 8 working launches, 9 code snippets
- gemma-family/: 12 per-variant briefs (ShieldGemma 2, CodeGemma, PaliGemma 2,
  Recurrent/Data/Med/TxGemma, Embedding/Translate/Function/Dolphin/SignGemma)
- fine-tuning/: Unsloth Gemma 4 notebooks, Axolotl YAMLs (incl 26B-A4B MoE),
  TRL scripts, Google cookbook fine-tune notebooks, recipe-recommendation.md

Findings that update earlier CORPUS_* docs are flagged in tooling/README.md
(not applied) — notably the new <|turn>/<turn|> prompt format, gemma_pytorch
abandonment, gemma.cpp Gemini-API server, transformers AutoModelForMultimodalLM,
FA2 head_dim=512 break, 26B-A4B MoE quantization rules, no Gemma 4 tech
report PDF yet, no Gemma-4-generation specialized siblings yet.

Pre-commit secrets hook bypassed per user authorization — flagged "secrets"
are base64 notebook cell outputs and example Ed25519 keys in the HDP
agentic-security demo, not real credentials.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-18 12:24:48 -04:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "gOpYt-Zgspvw"
},
"source": [
"To run this, press \"*Runtime*\" and press \"*Run all*\" on a Google Colab A100 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": "xNElsIzospvw"
},
"source": [
"### News"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZzyYx8-dspvx"
},
"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": "bgKLV2s1spvx"
},
"source": [
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "f6leZW9xspvx"
},
"outputs": [],
"source": "%%capture\nimport os, re\nif \"COLAB_\" not in \"\".join(os.environ.keys()):\n !pip install unsloth # Do this in local & cloud setups\nelse:\n import torch; v = re.match(r'[\\d]{1,}\\.[\\d]{1,}', str(torch.__version__)).group(0)\n xformers = 'xformers==' + {'2.10':'0.0.34','2.9':'0.0.33.post1','2.8':'0.0.32.post2'}.get(v, \"0.0.34\")\n !pip install sentencepiece protobuf \"datasets==4.3.0\" \"huggingface_hub>=0.34.0\" hf_transfer\n !pip install --no-deps unsloth_zoo bitsandbytes accelerate {xformers} peft trl triton unsloth\n!pip install --no-deps transformers==5.5.0\n!pip install torchcodec\nimport torch; torch._dynamo.config.recompile_limit = 64;"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "Yxkkpa5ospvx"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GFOEZbP7ONMs"
},
"source": [
"### Unsloth"
]
},
{
"cell_type": "code",
"execution_count": 3,
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"id": "QmUBVEnvCDJv",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 461,
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"outputId": "b63f747d-32a4-470f-b489-cad13b3b20f9"
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"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
"Unsloth: Your Flash Attention 2 installation seems to be broken. Using Xformers instead. No performance changes will be seen.\n",
"🦥 Unsloth Zoo will now patch everything to make training faster!\n",
"==((====))== Unsloth 2026.4.4: Fast Gemma4 patching. Transformers: 5.5.0.\n",
" \\\\ /| NVIDIA A100-SXM4-80GB. Num GPUs = 1. Max memory: 79.251 GB. Platform: Linux.\n",
"O^O/ \\_/ \\ Torch: 2.10.0+cu128. CUDA: 8.0. CUDA Toolkit: 12.8. Triton: 3.6.0\n",
"\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.34. 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]"
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"Loading weights: 0%| | 0/1013 [00:00<?, ?it/s]"
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"data": {
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"metadata": {}
}
],
"source": [
"from unsloth import FastVisionModel # FastLanguageModel for LLMs\n",
"import torch\n",
"\n",
"gemma4_models = [\n",
" # Gemma-4 instruct models:\n",
" \"unsloth/gemma-4-E2B-it\",\n",
" \"unsloth/gemma-4-E4B-it\",\n",
" \"unsloth/gemma-4-31B-it\",\n",
" \"unsloth/gemma-4-26B-A4B-it\",\n",
" # Gemma-4 base models:\n",
" \"unsloth/gemma-4-E2B\",\n",
" \"unsloth/gemma-4-E4B\",\n",
" \"unsloth/gemma-4-31B\",\n",
" \"unsloth/gemma-4-26B-A4B\",\n",
"] # More models at https://huggingface.co/unsloth\n",
"\n",
"model, processor = FastVisionModel.from_pretrained(\n",
" \"unsloth/gemma-4-26B-A4B-it\",\n",
" load_in_4bit = True, # Use 4bit to reduce memory use. False for 16bit LoRA.\n",
" use_gradient_checkpointing = \"unsloth\", # True or \"unsloth\" for long context\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SXd9bTZd1aaL"
},
"source": [
"We now add LoRA adapters for parameter efficient fine-tuning, allowing us to train only 1% of all model parameters efficiently.\n",
"\n",
"**[NEW]** We also support fine-tuning only the vision component, only the language component, or both. Additionally, you can choose to fine-tune the attention modules, the MLP layers, or both!"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "6bZsfBuZDeCL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9e2de1bb-0ebc-4b8c-9423-d81a3a90044a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Detected MoE model with num_experts = 128 and target_modules = '(?:.*?(?:vision|image|visual|patch|language|text).*?(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense).*?(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|proj|linear).*?)|(?:\\\\bmodel\\\\.layers\\\\.[\\\\d]{1,}\\\\.(?:self_attn|attention|attn|mlp|feed_forward|ffn|dense)\\\\.(?:(?:k_proj|q_proj|v_proj|o_proj|gate_proj|up_proj|down_proj|proj|linear)))'. Enabling LoRA on MoE parameters: ['mlp.experts.gate_up_proj', 'mlp.experts.down_proj']\n",
"Unsloth: PEFT set target_parameters but found no matching parameters.\n",
"This is expected for MoE models - Unsloth handles MoE expert LoRA targeting separately.\n"
]
}
],
"source": [
"model = FastVisionModel.get_peft_model(\n",
" model,\n",
" finetune_vision_layers = True, # False if not finetuning vision layers\n",
" finetune_language_layers = True, # False if not finetuning language layers\n",
" finetune_attention_modules = True, # False if not finetuning attention layers\n",
" finetune_mlp_modules = True, # False if not finetuning MLP layers\n",
"\n",
" r = 32, # The larger, the higher the accuracy, but might overfit\n",
" lora_alpha = 32, # Recommended alpha == r at least\n",
" lora_dropout = 0,\n",
" bias = \"none\",\n",
" random_state = 3407,\n",
" use_rslora = False, # We support rank stabilized LoRA\n",
" loftq_config = None, # And LoftQ\n",
" target_modules = \"all-linear\", # Optional now! Can specify a list if needed\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vITh0KVJ10qX"
},
"source": [
"<a name=\"Data\"></a>\n",
"### Data Prep\n",
"We'll use a sampled dataset of handwritten math formulas. The objective is to convert these images into a computer-readable format—specifically LaTeX—so they can be rendered. This is particularly useful for complex expressions.\n",
"\n",
"You can access the dataset [here](https://huggingface.co/datasets/unsloth/LaTeX_OCR). The full dataset is [here](https://huggingface.co/datasets/linxy/LaTeX_OCR)."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "LjY75GoYUCB8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 177,
"referenced_widgets": [
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"7e30c5c4c96d48e3a78689e33caafcd3",
"7de02d7843554276ad002c048a27c0d0",
"96cfbd5b4f314ea4841878182a7efe76",
"562d09896c604940922612e0e07f9c65",
"6f4bfaeacaa94fe9a5e6183255c23610",
"bda1c422c31a400abab7180489c6827a",
"c7ea3a7037814db68f2e49efc250dc53",
"314fc72dcdde4fe0b9b59439f0008a3d",
"48e085b16ef14d99b042debed39dcb6c",
"54a8a8e37c3d49158c65380451eb8712",
"9a6f19b03be84e5aa83b969363d08266",
"81f70c633bc4400e8a87c1cba5c878cb",
"4384402858074f76a55cc80ff7ea31f4",
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"e220f3b014f946fa9751a4d39ed0303c",
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"ec5e1b08549d47ca99a7857a97b94022",
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"8845926888a448f98a2d32eddfb9f710",
"a2c4067b539d42a0ab8a71a2ec05742f",
"d212975985c24093951238bf6b32f85b",
"3f674fdc9dbc421386f653c880926d96",
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]
},
"outputId": "f5358882-f875-4091-f503-f1b43305c604"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/519 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e6cf365cf0674a8884e0c8ac8f8280e8"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/train-00000-of-00001.parquet: 0%| | 0.00/344M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "9a6f19b03be84e5aa83b969363d08266"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/test-00000-of-00001.parquet: 0%| | 0.00/38.2M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "44475c29f3e74859a22b79e426cbff03"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/68686 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "079918351e0f463bba0cc030663acffc"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating test split: 0%| | 0/7632 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "e89e685da18542fca578ff9bd9aaa8cb"
}
},
"metadata": {}
}
],
"source": [
"from datasets import load_dataset\n",
"dataset = load_dataset(\"unsloth/LaTeX_OCR\", split = \"train\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "W1W2Qhsz6rUT"
},
"source": [
"Let's take an overview of the dataset. We'll examine the second image and its corresponding caption."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "bfcSGwIb6p_R",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "50e2e313-50a4-4a4b-f3e4-3cc57446867a"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Dataset({\n",
" features: ['image', 'text'],\n",
" num_rows: 68686\n",
"})"
]
},
"metadata": {},
"execution_count": 6
}
],
"source": [
"dataset"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"id": "uOLWY2936t1n",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 67
},
"outputId": "8994b73b-22ec-42e2-cc4b-ee6833eed020"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<PIL.PngImagePlugin.PngImageFile image mode=RGB size=320x50>"
],
"image/png": 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\n",
"image/jpeg": "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\n"
},
"metadata": {},
"execution_count": 7
}
],
"source": [
"dataset[2][\"image\"]"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "lXjfJr4W6z8P",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"outputId": "0f9c8018-ae97-4118-9f28-c97f3e7b6b1d"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'H ^ { \\\\prime } = \\\\beta N \\\\int d \\\\lambda \\\\biggl \\\\{ \\\\frac { 1 } { 2 \\\\beta ^ { 2 } N ^ { 2 } } \\\\partial _ { \\\\lambda } \\\\zeta ^ { \\\\dagger } \\\\partial _ { \\\\lambda } \\\\zeta + V ( \\\\lambda ) \\\\zeta ^ { \\\\dagger } \\\\zeta \\\\biggr \\\\} \\\\ .'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 8
}
],
"source": [
"dataset[2][\"text\"]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "rKHxfZua1CrS"
},
"source": [
"We can also render LaTeX directly in the browser!"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "nPopsxAC1CrS",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 52
},
"outputId": "859f1f54-03ca-42c6-83ee-9fded7ad2d07"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.Math object>"
],
"text/latex": "$\\displaystyle \\sigma ^ { \\mu } \\frac { \\lambda ^ { a } } { 2 } A _ { \\mu } ^ { a } .$"
},
"metadata": {}
}
],
"source": [
"from IPython.display import display, Math, Latex\n",
"\n",
"latex = dataset[3][\"text\"]\n",
"display(Math(latex))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K9CBpiISFa6C"
},
"source": [
"To format the dataset, all vision fine-tuning tasks should follow this format:\n",
"\n",
"```python\n",
"[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"text\", \"text\": instruction},\n",
" {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
" ],\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"text\", \"text\": instruction},\n",
" {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
" ],\n",
" },\n",
"]\n",
"```"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "oPXzJZzHEgXe"
},
"outputs": [],
"source": [
"instruction = \"Write the LaTeX representation for this image.\"\n",
"\n",
"def convert_to_conversation(sample):\n",
" conversation = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"text\", \"text\": instruction},\n",
" {\"type\": \"image\", \"image\": sample[\"image\"]},\n",
" ],\n",
" },\n",
" {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": sample[\"text\"]}]},\n",
" ]\n",
" return {\"messages\": conversation}\n",
"pass"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FY-9u-OD6_gE"
},
"source": [
"Let's convert the dataset into the \"correct\" format for finetuning:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "gFW2qXIr7Ezy"
},
"outputs": [],
"source": [
"converted_dataset = [convert_to_conversation(sample) for sample in dataset]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ndDUB23CGAC5"
},
"source": [
"The first example is now structured like below:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "gGFzmplrEy9I",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "2b05e49c-bc65-494c-cba5-e70d41c0f1a9"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'messages': [{'role': 'user',\n",
" 'content': [{'type': 'text',\n",
" 'text': 'Write the LaTeX representation for this image.'},\n",
" {'type': 'image',\n",
" 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x40>}]},\n",
" {'role': 'assistant',\n",
" 'content': [{'type': 'text',\n",
" 'text': '{ \\\\frac { N } { M } } \\\\in { \\\\bf Z } , { \\\\frac { M } { P } } \\\\in { \\\\bf Z } , { \\\\frac { P } { Q } } \\\\in { \\\\bf Z }'}]}]}"
]
},
"metadata": {},
"execution_count": 12
}
],
"source": [
"converted_dataset[0]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MsRPBIb0JJ6c"
},
"source": [
"Lets take the Gemma 4 instruction chat template and use it in our base model"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "exoDVEvmJN-6"
},
"outputs": [],
"source": [
"from unsloth import get_chat_template\n",
"\n",
"processor = get_chat_template(\n",
" processor,\n",
" \"gemma-4-thinking\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FecKS-dA82f5"
},
"source": [
"Before fine-tuning, let us evaluate the base model's performance. We do not expect strong results, as it has not encountered this chat template before."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"id": "vcat4UxA81vr",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "8514dfc7-67d1-4568-fab8-8139bd56ac6b"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|channel>thought\n",
"<channel|>The LaTeX representation for the equation in the image is:\n",
"\n",
"```latex\n",
"H' = \\beta N \\int d\\lambda \\left\\{ \\frac{1}{2\\beta^2 N^2} \\partial_\\lambda \\zeta^\\dagger \\partial_\\lambda \\zeta + \\mathcal{V}(\\lambda) \\zeta^\\dagger \\zeta \\right\\}\n",
"```\n",
"\n",
"When rendered, it looks like this:\n",
"$H' = \\beta N \\int d\\lambda \\left\\{ \\frac{1}{2\\beta^2 N^2} \\partial_\\lambda \\zeta^\\dagger \\\n"
]
}
],
"source": [
"image = dataset[2][\"image\"]\n",
"instruction = \"Write the LaTeX representation for this image.\"\n",
"\n",
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
" }\n",
"]\n",
"input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
"inputs = processor(\n",
" image,\n",
" input_text,\n",
" add_special_tokens = False,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"\n",
"text_streamer = TextStreamer(processor, skip_prompt = True)\n",
"result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
" use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "FeAiMlQ71CrS"
},
"source": [
"You can see it's absolutely terrible! It doesn't follow instructions at all"
]
},
{
"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`. We also support `DPOTrainer` and `GRPOTrainer` for reinforcement learning!\n",
"\n",
"We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "95_Nn-89DhsL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d9c94f8e-d6ef-4f64-ca13-428028335b88"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Model does not have a default image size - using 512\n"
]
}
],
"source": [
"from unsloth.trainer import UnslothVisionDataCollator\n",
"from trl import SFTTrainer, SFTConfig\n",
"\n",
"trainer = SFTTrainer(\n",
" model = model,\n",
" train_dataset = converted_dataset,\n",
" processing_class = processor.tokenizer,\n",
" data_collator = UnslothVisionDataCollator(model, processor),\n",
" args = SFTConfig(\n",
" per_device_train_batch_size = 1,\n",
" gradient_accumulation_steps = 4,\n",
" max_grad_norm = 0.3,\n",
" warmup_ratio = 0.03,\n",
" max_steps = 60,\n",
" # num_train_epochs = 2, # Set this instead of max_steps for full training runs\n",
" learning_rate = 2e-4,\n",
" logging_steps = 1,\n",
" save_strategy = \"steps\",\n",
" optim = \"adamw_8bit\",\n",
" weight_decay = 0.001,\n",
" lr_scheduler_type = \"cosine\",\n",
" seed = 3407,\n",
" output_dir = \"outputs\",\n",
" report_to = \"none\", # For Weights and Biases or others\n",
"\n",
" # You MUST put the below items for vision finetuning:\n",
" remove_unused_columns = False,\n",
" dataset_text_field = \"\",\n",
" dataset_kwargs = {\"skip_prepare_dataset\": True},\n",
" max_length = 2048,\n",
" )\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"cellView": "form",
"id": "2ejIt2xSNKKp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "99c38b86-0096-4319-ba4b-7cf3d76681b1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = NVIDIA A100-SXM4-80GB. Max memory = 79.251 GB.\n",
"46.615 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": "code",
"execution_count": 17,
"metadata": {
"id": "yqxqAZ7KJ4oL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "34c1db7c-d3b0-414c-b281-7eb01421bfbf"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"The tokenizer has new PAD/BOS/EOS tokens that differ from the model config and generation config. The model config and generation config were aligned accordingly, being updated with the tokenizer's values. Updated tokens: {'bos_token_id': 2}.\n",
"==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
" \\\\ /| Num examples = 68,686 | Num Epochs = 1 | Total steps = 60\n",
"O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n",
"\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n",
" \"-____-\" Trainable parameters = 59,275,776 of 25,865,209,648 (0.23% trained)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"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='60' max='60' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [60/60 18:34, 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>2.986776</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3.611241</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>3.235800</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>3.207892</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>2.393348</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>1.994182</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>1.238167</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>1.074898</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>1.115454</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>0.414163</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>0.488537</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.463625</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.485965</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.257269</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.223266</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.299220</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>0.228006</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>0.165416</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>0.214501</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.163012</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.160776</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>0.227581</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.252964</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.154257</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.162097</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.201924</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.192899</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.150303</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.220560</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.132235</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.154113</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.265951</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>0.128669</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.118749</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.169299</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.130528</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.099769</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.122619</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.148352</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.202640</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.163365</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.065318</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.185360</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.124109</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.060225</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>0.107721</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.129032</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.118885</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.188991</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>0.109477</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.080716</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.167722</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.136780</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.125553</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.158793</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.392148</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.100937</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.066817</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.181567</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.087730</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"cellView": "form",
"id": "pCqnaKmlO1U9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f9f14f4d-75bb-4423-ddc3-e79677d21d8a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1187.0917 seconds used for training.\n",
"19.78 minutes used for training.\n",
"Peak reserved memory = 46.926 GB.\n",
"Peak reserved memory for training = 0.311 GB.\n",
"Peak reserved memory % of max memory = 59.212 %.\n",
"Peak reserved memory for training % of max memory = 0.392 %.\n"
]
}
],
"source": [
"# @title Show final memory and time stats\n",
"used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)\n",
"used_memory_for_lora = round(used_memory - start_gpu_memory, 3)\n",
"used_percentage = round(used_memory / max_memory * 100, 3)\n",
"lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)\n",
"print(f\"{trainer_stats.metrics['train_runtime']} seconds used for training.\")\n",
"print(\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! You can modify the instruction and input—just leave the output blank.\n",
"\n",
"We'll use the best hyperparameters for inference on Gemma: `top_p=0.95`, `top_k=64`, and `temperature=1.0`."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "kR3gIAX-SM2q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "76204120-8c3f-49b4-a5e2-f1e66a5b7f3a"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|channel>thought\n",
"<channel|>[ [ B _ { n } ^ { + } , b _ { 2 } ^ { - } ] , b _ { 2 } ^ { + } ] = n B _ { n } ^ { + } , \\quad [ [ B _ { n } ^ { - } , b _ { 2 } ^ { + } ] , b _ { 2 } ^ { - } ] = n B _ { n } ^ { - } .<turn|>\n"
]
}
],
"source": [
"image = dataset[10][\"image\"]\n",
"instruction = \"Write the LaTeX representation for this image.\"\n",
"\n",
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": instruction}],\n",
" }\n",
"]\n",
"\n",
"input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
"\n",
"inputs = processor(\n",
" image,\n",
" input_text,\n",
" add_special_tokens = False,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"\n",
"text_streamer = TextStreamer(processor, skip_prompt = True)\n",
"result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
" use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
]
},
{
"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, use Hugging Face’s `push_to_hub` for online saving, or `save_pretrained` for local storage.\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": 20,
"metadata": {
"id": "upcOlWe7A1vc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "a5558703-774e-4d23-9794-0c3fbc5ccad4"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['gemma_4_lora/processor_config.json']"
]
},
"metadata": {},
"execution_count": 20
}
],
"source": [
"model.save_pretrained(\"gemma_4_lora\") # Local saving\n",
"processor.save_pretrained(\"gemma_4_lora\")\n",
"# model.push_to_hub(\"your_name/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n",
"# processor.push_to_hub(\"your_name/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AEEcJ4qfC7Lp"
},
"source": [
"Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "MKX_XKs_BNZR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "f214eeec-cf71-4e48-a13f-144ef5bf37ce"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|channel>thought\n",
"<channel|>The image shows a mathematical expression in tensor notation, likely from a physics context (such as general relativity or gauge theory).\n",
"\n",
"The expression is:\n",
"$$D _ { \\mu } ^ { \\alpha \\beta } \\bar { A } _ { \\mu } ^ { \\alpha \\beta } = 0 ,$$\n",
"\n",
"### Breakdown of the notation:\n",
"* **$D$**: Represents a covariant derivative.\n",
"* **$\\mu$**: The index being contracted.\n",
"* **$\\alpha, \\beta$**: Indices representing the tensor components.\n",
"* **$\\bar{A\n"
]
}
],
"source": [
"if False:\n",
" from unsloth import FastVisionModel\n",
"\n",
" model, processor = FastVisionModel.from_pretrained(\n",
" model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n",
" load_in_4bit = True, # Set to False for 16bit LoRA\n",
" )\n",
"\n",
"sample = dataset[1]\n",
"image = sample[\"image\"].convert(\"RGB\")\n",
"messages = [\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": sample[\"text\"],\n",
" },\n",
" {\n",
" \"type\": \"image\",\n",
" },\n",
" ],\n",
" },\n",
"]\n",
"input_text = processor.apply_chat_template(messages, add_generation_prompt = True)\n",
"inputs = processor(\n",
" image,\n",
" input_text,\n",
" add_special_tokens = False,\n",
" return_tensors = \"pt\",\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"\n",
"text_streamer = TextStreamer(processor.tokenizer, skip_prompt = True)\n",
"_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n",
" use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)"
]
},
{
"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. 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",
"execution_count": 22,
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"# Select ONLY 1 to save! (Both not needed!)\n",
"\n",
"# Save locally to 16bit\n",
"if False: model.save_pretrained_merged(\"unsloth_finetune\", processor,)\n",
"\n",
"# To export and save to your Hugging Face account\n",
"if False: model.push_to_hub_merged(\"YOUR_USERNAME/unsloth_finetune\", processor, token = \"YOUR_HF_TOKEN\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TSjNVDCYv-yr"
},
"source": [
"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)."
]
}
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