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gemma4-research/tooling/fine-tuning/unsloth/notebooks/Gemma4_(26B_A4B)-Text.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": "64duhI2Gsavq"
},
"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": "tDDXqI4lsavq"
},
"source": [
"### News"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-Zyog1Zysavq"
},
"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": "LRFceDFcsavq"
},
"source": [
"### Installation"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "A6wGqvTjsavr"
},
"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": "lBN09c1tUlSV"
},
"outputs": [],
"source": [
"%%capture\n",
"!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TGMWlrRdzwgf"
},
"source": [
"### Unsloth\n",
"\n",
"`FastModel` supports loading nearly any model now! This includes Vision and Text models!"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "-Xbb0cuLzwgf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 461,
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"outputId": "d373134b-4b9d-43be-db56-d438180a5a5a"
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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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"metadata": {}
}
],
"source": [
"from unsloth import FastModel\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, tokenizer = FastModel.from_pretrained(\n",
" model_name = \"unsloth/gemma-4-26B-A4B-it\",\n",
" dtype = None, # None for auto detection\n",
" max_seq_length = 8192, # Choose any for long context!\n",
" load_in_4bit = True, # 4 bit quantization to reduce memory\n",
" full_finetuning = False, # [NEW!] We have full finetuning now!\n",
" # token = \"YOUR_HF_TOKEN\", # HF Token for gated models\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ixr4dyTHVIcI"
},
"source": [
"# Gemma 4 can process Text, Vision and Audio!\n",
"\n",
"Let's first experience how Gemma 4 can handle multimodal inputs. We use Gemma 4's recommended settings of `temperature = 1.0, top_p = 0.95, top_k = 64`"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "UsfUPU-oVQYu"
},
"outputs": [],
"source": [
"from transformers import TextStreamer\n",
"# Helper function for inference\n",
"def do_gemma_4_inference(messages, max_new_tokens = 128):\n",
" _ = model.generate(\n",
" **tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True, # Must add for generation\n",
" tokenize = True,\n",
" return_dict = True,\n",
" return_tensors = \"pt\",\n",
" ).to(\"cuda\"),\n",
" max_new_tokens = max_new_tokens,\n",
" use_cache = True,\n",
" temperature = 1.0, top_p = 0.95, top_k = 64,\n",
" streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q2-ddk0CWeTA"
},
"source": [
"# Gemma 4 can see images!\n",
"\n",
"<img src=\"https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg\" alt=\"Alt text\" height=\"256\">"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "9jGeSb9bWe0k",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "512b7df1-151d-4e3e-f8cf-88cd39c34e2f"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The animal in the image is a sloth. While sloths are not typically central characters in major films, they have appeared in various movies and documentaries. Some examples include:\n",
"\n",
"* **Zootopia (2016):** A sloth named Flash is a memorable character in this Disney animated film.\n",
"* **Nature Documentaries:** Sloths are frequently featured in nature documentaries from series like *Planet Earth* or *National Geographic*.\n",
"* **Various animated films:** They occasionally appear as background characters or minor roles in various animated productions.<turn|>\n"
]
}
],
"source": [
"sloth_link = \"https://files.worldwildlife.org/wwfcmsprod/images/Sloth_Sitting_iStock_3_12_2014/story_full_width/8l7pbjmj29_iStock_000011145477Large_mini__1_.jpg\"\n",
"\n",
"messages = [{\n",
" \"role\" : \"user\",\n",
" \"content\": [\n",
" { \"type\": \"image\", \"image\" : sloth_link },\n",
" { \"type\": \"text\", \"text\" : \"Which films does this animal feature in?\" }\n",
" ]\n",
"}]\n",
"# You might have to wait 1 minute for Unsloth's auto compiler\n",
"do_gemma_4_inference(messages, max_new_tokens = 256)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eh0BzbZPWtRD"
},
"source": [
"Let's make a poem about sloths!"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "R3ExuK8cWuT3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "db69482a-7f7d-4dc9-cc54-768eb938fbb1"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"In the canopy’s emerald, velvet embrace,\n",
"Where the sunlight descends with a slow, steady grace,\n",
"Dwells a master of stillness, a king of the pause,\n",
"Obeying the rhythm of nature’s own laws.\n",
"\n",
"No hurry disturbs him, no frantic pursuit,\n",
"He is content with the leaf and the fruit.\n",
"With limbs like slow rivers and eyes soft and wise,\n",
"He watches the world through a dreamy disguise.\n",
"\n",
"A coat made of moss and a spirit of peace,\n",
"He waits for the rush of the jungle to cease.\n",
"While the monkeys all chatter and colorful birds fly,\n",
"\n"
]
}
],
"source": [
"messages = [{\n",
" \"role\": \"user\",\n",
" \"content\": [{ \"type\" : \"text\",\n",
" \"text\" : \"Write a poem about sloths.\" }]\n",
"}]\n",
"do_gemma_4_inference(messages)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Bw5XPyYFajyM"
},
"source": [
"# Let's finetune Gemma 4!\n",
"\n",
"You can finetune the vision and text parts for now through selection - the audio part can also be finetuned - we're working to make it selectable as well!"
]
},
{
"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": 7,
"metadata": {
"id": "6bZsfBuZDeCL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "81d7a65c-3b00-437c-d418-67867cd126de"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Detected MoE model with num_experts = 128 and target_modules = '(?:.*?(?: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 = FastModel.get_peft_model(\n",
" model,\n",
" finetune_vision_layers = False, # Turn off for just text!\n",
" finetune_language_layers = True, # Should leave on!\n",
" finetune_attention_modules = True, # Attention good for GRPO\n",
" finetune_mlp_modules = True, # Should leave on always!\n",
"\n",
" r = 8, # Larger = higher accuracy, but might overfit\n",
" lora_alpha = 8, # Recommended alpha == r at least\n",
" lora_dropout = 0,\n",
" bias = \"none\",\n",
" random_state = 3407,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vITh0KVJ10qX"
},
"source": [
"<a name=\"Data\"></a>\n",
"### Data Prep\n",
"We now use the `Gemma-4` format for conversation style finetunes. We use [Maxime Labonne's FineTome-100k](https://huggingface.co/datasets/mlabonne/FineTome-100k) dataset in ShareGPT style. Gemma-4 renders multi turn conversations like below:\n",
"\n",
"```\n",
"<bos><|turn>user\n",
"Hello<turn|>\n",
"<|turn>model\n",
"Hey there!<turn|>\n",
"```\n",
"We use our `get_chat_template` function to get the correct chat template. We support `zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3, gemma-4` and more."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "LjY75GoYUCB8"
},
"outputs": [],
"source": [
"from unsloth.chat_templates import get_chat_template\n",
"tokenizer = get_chat_template(\n",
" tokenizer,\n",
" chat_template = \"gemma-4-thinking\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZQkXuGYxbJ-e"
},
"source": [
"We get the first 3000 rows of the dataset"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "Mkq4RvEq7FQr",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 113,
"referenced_widgets": [
"ea642c11633e49db979a42c731ee1d65",
"7d44ac1ab19148ddbeb30b5a154ad8ae",
"608c16dc8fc049c1aa16b44db90b3aba",
"9fe2a1a901af4445aed445bce8b40c83",
"caa699c0834f42c68854dd5d612dde74",
"c512cc070e8349408354f6c9454eb2b7",
"0302c405886540e9837f90608688c0be",
"1521c752798a47fb996da308bf41a27f",
"7208562807bb435b85271416df707e93",
"26779d9bd26b461e9df7846137d31213",
"9c3255be55bf4c1ea91f55754010961e",
"0921d7187ee24114880fa7d7b7db645d",
"f28157da9878442998595a29bda2f64b",
"13580885a8fc41acb660a155918fba88",
"90c1689829f242998f4b35bb8068d5f6",
"b9c0ae90bcbd498ab039c00f6d7c0017",
"dc0f07ea3fb74c9a9817e3c61a237211",
"22e5c73c269247f3915c216c9efb2a7e",
"ed6383465575471689ab4d8a2323da84",
"fb1419c94d254f38ae1007200c3cec92",
"a8bb4aa7409647c2b8949b14e67f8222",
"63dd480a71204e75b4bb7dd8ca76790f",
"3a0e9f6cd1014fd38ccd3ef2734b2929",
"7b0b5a0d6e474bb78ee8eac7281099b5",
"f8b0f6f04f734d4f8bb59c29ee163a42",
"e82867e03ef64048b0632e551a941eb3",
"33bd1e52d5094206942d06bbdab706cf",
"7818031043d44f27a87fa3574cc8c86e",
"38cca59244824cff98732887980d5172",
"ecc2db1896c244958fe0e79dd11b66bd",
"d838de584c474e8ea454a4c71185af61",
"bbdce42ed0704835ade22bd2a2f8c46c",
"750bfb16f1f442a2bd1e4b2ee021319b"
]
},
"outputId": "2ab453f2-3708-4f67-b137-dbd70ce21643"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/982 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ea642c11633e49db979a42c731ee1d65"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/train-00000-of-00001.parquet: 0%| | 0.00/117M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0921d7187ee24114880fa7d7b7db645d"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/100000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3a0e9f6cd1014fd38ccd3ef2734b2929"
}
},
"metadata": {}
}
],
"source": [
"from datasets import load_dataset\n",
"dataset = load_dataset(\"mlabonne/FineTome-100k\", split = \"train[:3000]\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K9CBpiISFa6C"
},
"source": [
"We now use `standardize_data_formats` to try converting datasets to the correct format for finetuning purposes!"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "reoBXmAn7HlN",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"3a8bada54de24005878735b98d444f29",
"8eded708fd6742cc96cec313c512b0f0",
"b9c5d9b844874196ae366f285e49ebfe",
"b40f36a643b242dfbaf1ffd475e83da2",
"45a9724053244ca998c84a5e0a81c1b9",
"89d693d6c7a142c68c7802423585b151",
"a2c6355a6a6b4ef787cbeba481410426",
"76fd0b3eac784c53943244976d315519",
"50ffb521dbd1411bb0d8537d3bd4b6d0",
"20c834cf533442388acf92187ee4445e",
"9dcb860a29474e5b93713073549d35f1"
]
},
"outputId": "a625262f-bf08-4db5-c699-6a4d13fcfdde"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Unsloth: Standardizing formats (num_proc=16): 0%| | 0/3000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3a8bada54de24005878735b98d444f29"
}
},
"metadata": {}
}
],
"source": [
"from unsloth.chat_templates import standardize_data_formats\n",
"dataset = standardize_data_formats(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "6i5Sx9In7vHi"
},
"source": [
"Let's see how row 100 looks like!"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "dzE1OEXi7s3P",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4029d65c-0b74-45d4-e673-a67fdab72e94"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'conversations': [{'content': 'What is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?',\n",
" 'role': 'user'},\n",
" {'content': 'In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.',\n",
" 'role': 'assistant'}],\n",
" 'source': 'infini-instruct-top-500k',\n",
" 'score': 4.774171352386475}"
]
},
"metadata": {},
"execution_count": 11
}
],
"source": [
"dataset[100]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Xs0LXio7rfd"
},
"source": [
"We now have to apply the chat template for `Gemma-3` onto the conversations, and save it to `text`. We remove the `<bos>` token using removeprefix(`'<bos>'`) since we're finetuning. The Processor will add this token before training and the model expects only one."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "1ahE8Ys37JDJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"89abc50a036d4faf9be10ceed297717b",
"fd4fe9ecb07b4a93b28cc23aff8c869d",
"10f5aeae5a594847aac7310d83942932",
"cbd6d8c7ed884edd9f53d56f8abfd194",
"aaf13b4c776146be9b377259caad3a9f",
"eceb4abf92b744df8973b3443e956ff5",
"4ed42a6b2ed541edaa51d5769589b234",
"f81f35d00b0443f5aff0a50382fca927",
"27abe44e35554d3e8338b50f494f7bd2",
"d07a25a3f10e47fa835d5d9ef6088e09",
"f37eb7bb43cc4409bfbfd827450dc4c0"
]
},
"outputId": "8fcfe6f4-5d8e-4f43-ca00-3ed0d290a09d"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/3000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "89abc50a036d4faf9be10ceed297717b"
}
},
"metadata": {}
}
],
"source": [
"def formatting_prompts_func(examples):\n",
" convos = examples[\"conversations\"]\n",
" texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False).removeprefix('<bos>') for convo in convos]\n",
" return { \"text\" : texts, }\n",
"\n",
"dataset = dataset.map(formatting_prompts_func, batched = True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ndDUB23CGAC5"
},
"source": [
"Let's see how the chat template did! Notice there is no `<bos>` token as the processor tokenizer will be adding one."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "gGFzmplrEy9I",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140
},
"outputId": "2985589a-7ff8-4334-afde-3354f60e3ce6"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?<turn|>\\n<|turn>model\\n<|channel>thought\\n<channel|>In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.<turn|>\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 13
}
],
"source": [
"dataset[100][\"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": "code",
"execution_count": 14,
"metadata": {
"id": "95_Nn-89DhsL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"c3d4dd1d33394f1a88ca079188777fe1",
"c1533ef6cb884d6e86a22519ed7125ce",
"824b65bd8b7a41baadbe6ddb79b17de4",
"c118b89131354eb98deaae78238b0210",
"89fccf64a5ca4b08b435b4223f42793e",
"a4b2ebf57ab144f4bff90820725b6108",
"f5b43de869004a42b2b9d0740aa455e7",
"85243a7a0b3a4abb891cf55828ba8b7a",
"a9805e75f7df4882a90770bf8a7a5530",
"b3ac166597b342ca8cbf4eb0b361e7a4",
"97802db0631649d79348faed67ba75ef"
]
},
"outputId": "fb94fdc8-f519-4b05-d689-525b6857cc89"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Unsloth: Tokenizing [\"text\"] (num_proc=16): 0%| | 0/3000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "c3d4dd1d33394f1a88ca079188777fe1"
}
},
"metadata": {}
}
],
"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 = 4, # 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 = 60,\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": 15,
"metadata": {
"id": "juQiExuBG5Bt",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 81,
"referenced_widgets": [
"6c2f48e8343b4722bc30bb62d94ea55f",
"e2755c541dad407ba30990db458f29e4",
"927ce392ba2a403081cb68ddf07fcc78",
"c0e8470e86fd4f34b8adff7ac78ae0cd",
"ecbed13d665c454da7f835028d56e590",
"89d74579171d48668c0542e20fbe877c",
"e03a39c6a9354ed9a018e2487f073d59",
"29c397be81794e3aaf80ff115b1bd8fe",
"b8c562d039ca4c9bba34c03f10dd65a7",
"0e3d391c4f8d42318f19ba0f832a4438",
"92015d6aa1274b69a48acafff7cc1abe",
"3802243f57e44bbe993ec24f6a8c3fee",
"1429ff4a23174e25b800190869b5aa4a",
"9644878b1d71428ca8bf953423132e1a",
"7eb91b277e664af291ede6355337cae6",
"bb05f72207104b9a854ef607a034330a",
"8f4a1acdc33a4d42be4af93b5ac94d2f",
"851ab005663641dd92883ee29796768b",
"0fa697ca6068489ea5c7acb5f6d9c4bd",
"8501848fec5c4e51a213b86aeac44213",
"97d6b96094d34d6985855ba18a5f329b",
"9ed61906d50b444da7e8a22a636c65ac"
]
},
"outputId": "45ccbc48-f63e-4bfe-9476-c03b6041d3ca"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map (num_proc=16): 0%| | 0/3000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "6c2f48e8343b4722bc30bb62d94ea55f"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Filter (num_proc=16): 0%| | 0/3000 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3802243f57e44bbe993ec24f6a8c3fee"
}
},
"metadata": {}
}
],
"source": [
"from unsloth.chat_templates import train_on_responses_only\n",
"trainer = train_on_responses_only(\n",
" trainer,\n",
" instruction_part = \"<|turn>user\\n\",\n",
" response_part = \"<|turn>model\\n\",\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dv1NBUozV78l"
},
"source": [
"Let's verify masking the instruction part is done! Let's print the 100th row again. Notice how the sample only has a single `<bos>` as expected!"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "LtsMVtlkUhja",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140
},
"outputId": "e6063abd-0fbb-4ae7-d93a-500cbe5f9e5f"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'<|turn>user\\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?<turn|>\\n<|turn>model\\n<|channel>thought\\n<channel|>In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.<turn|>\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 16
}
],
"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": 17,
"metadata": {
"id": "_rD6fl8EUxnG",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 140
},
"outputId": "420e56a4-9651-45b4-d8f7-c33f3b7ff052"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"' <|channel>thought\\n<channel|>In programming, the modulus operator is represented by the \\'%\\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\\n\\n```python\\n# Calculate the modulus\\nModulus = a % b\\n\\nprint(\"Modulus of the given numbers is: \", Modulus)\\n```\\n\\nIn this code snippet, the variables \\'a\\' and \\'b\\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \\'%\\', we calculate the remainder when \\'a\\' is divided by \\'b\\'. The result is then stored in the variable \\'Modulus\\'. Finally, the modulus value is printed using the \\'print\\' statement.\\n\\nFor example, if \\'a\\' is 10 and \\'b\\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\\n\\n```\\nModulus of the given numbers is: 2\\n```\\n\\nThis means that the modulus of 10 and 4 is 2.<turn|>\\n'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 17
}
],
"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": 18,
"metadata": {
"cellView": "form",
"id": "2ejIt2xSNKKp",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7c222465-fa0f-467c-91d8-f2c6a2ad3938"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"GPU = NVIDIA A100-SXM4-80GB. Max memory = 79.251 GB.\n",
"46.416 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!\n",
"\n",
"To resume a training run, set `trainer.train(resume_from_checkpoint = True)`"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"id": "yqxqAZ7KJ4oL",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "c2db93ff-2bc9-4ebc-dcc5-36d484a7618c"
},
"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 = 3,000 | 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 = 9,292,800 of 25,815,226,672 (0.04% 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 20:53, 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>1.781083</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.566694</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>0.878096</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1.368889</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>1.104031</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>0.794103</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>0.815552</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>0.770014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>0.916691</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>1.033089</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>1.126286</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>0.652004</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>0.769890</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>0.978357</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>0.766254</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>0.999658</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>1.138031</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>1.053890</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>1.042485</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>0.973515</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>0.605233</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>1.156014</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>0.922710</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>0.928817</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>0.575161</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>0.498077</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>0.613930</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>0.653096</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>0.572882</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>0.831407</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>0.706046</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>0.678690</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>1.082695</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>0.499532</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>0.965407</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>0.964235</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>0.643548</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>0.955152</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>0.993222</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>0.663521</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>0.862385</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>0.868709</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>0.650642</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>0.554028</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>0.584829</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>1.281055</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>0.672700</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>0.687023</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>0.953930</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>1.126283</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>0.532105</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>0.613895</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>0.654956</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>0.610443</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>0.815666</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>0.950072</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>0.495718</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>0.817099</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>0.894461</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>0.800382</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {
"cellView": "form",
"id": "pCqnaKmlO1U9",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "1e195e99-92bc-4b23-91a9-a9602d8035a7"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"1326.0614 seconds used for training.\n",
"22.1 minutes used for training.\n",
"Peak reserved memory = 48.988 GB.\n",
"Peak reserved memory for training = 2.572 GB.\n",
"Peak reserved memory % of max memory = 61.814 %.\n",
"Peak reserved memory for training % of max memory = 3.245 %.\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 via Unsloth native inference! According to the `Gemma-3` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64`"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"id": "kR3gIAX-SM2q",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "7c7c8ee7-33b3-474e-af73-7e743a706b57"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['<bos><|turn>user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,<turn|>\\n<|turn>model\\n<|channel>thought\\n<channel|>13, 21, 34, 55, 89, 144, ...\\n\\nThis is the **Fibonacci sequence**, where each number is the sum of the two preceding ones.<turn|>']"
],
"text/html": [
"<pre>[&#x27;&lt;bos&gt;&lt;|turn&gt;user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,&lt;turn|&gt;\\n&lt;|turn&gt;model\\n&lt;|channel&gt;thought\\n&lt;channel|&gt;13, 21, 34, 55, 89, 144, ...\\n\\nThis is the **Fibonacci sequence**, where each number is the sum of the two preceding ones.&lt;turn|&gt;&#x27;]</pre>"
]
},
"metadata": {},
"execution_count": 21
}
],
"source": [
"from unsloth.chat_templates import get_chat_template\n",
"tokenizer = get_chat_template(\n",
" tokenizer,\n",
" chat_template = \"gemma-4-thinking\",\n",
")\n",
"messages = [{\n",
" \"role\": \"user\",\n",
" \"content\": [{\n",
" \"type\" : \"text\",\n",
" \"text\" : \"Continue the sequence: 1, 1, 2, 3, 5, 8,\",\n",
" }]\n",
"}]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True, # Must add for generation\n",
" return_tensors = \"pt\",\n",
" tokenize = True,\n",
" return_dict = True,\n",
").to(\"cuda\")\n",
"outputs = model.generate(\n",
" **inputs,\n",
" max_new_tokens = 64, # Increase for longer outputs!\n",
" use_cache = True,\n",
" # Recommended Gemma-3 settings!\n",
" temperature = 1.0, top_p = 0.95, top_k = 64,\n",
")\n",
"tokenizer.batch_decode(outputs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CrSvZObor0lY"
},
"source": [
" You can also use a `TextStreamer` for continuous inference - so you can see the generation token by token, instead of waiting the whole time!"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"id": "e2pEuRb1r2Vg",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9ad7c937-8c04-46ba-d850-ce24f294ffc0"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|channel>thought\n",
"<channel|>The sky is blue because of a phenomenon called **Rayleigh scattering**.\n",
"\n",
"Here is the step-by-step breakdown of why this happens:\n",
"\n",
"### 1. Sunlight is a spectrum of colors\n",
"Although sunlight looks white, it is actually made up of all the colors of the rainbow (red\n"
]
}
],
"source": [
"messages = [{\n",
" \"role\": \"user\",\n",
" \"content\": [{\"type\" : \"text\", \"text\" : \"Why is the sky blue?\",}]\n",
"}]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True, # Must add for generation\n",
" return_tensors = \"pt\",\n",
" tokenize = True,\n",
" return_dict = True,\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"_ = model.generate(\n",
" **inputs,\n",
" max_new_tokens = 64, # Increase for longer outputs!\n",
" use_cache = True,\n",
" # Recommended Gemma-3 settings!\n",
" temperature = 1.0, top_p = 0.95, top_k = 64,\n",
" streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
")"
]
},
{
"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": 23,
"metadata": {
"id": "upcOlWe7A1vc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "42f363fe-2e66-41ef-9645-998f4b011299"
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['gemma_4_lora/processor_config.json']"
]
},
"metadata": {},
"execution_count": 23
}
],
"source": [
"model.save_pretrained(\"gemma_4_lora\") # Local saving\n",
"tokenizer.save_pretrained(\"gemma_4_lora\")\n",
"# model.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n",
"# tokenizer.push_to_hub(\"HF_ACCOUNT/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": 24,
"metadata": {
"id": "MKX_XKs_BNZR",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c05ee3db-0983-468f-bc1c-4a99b1ef1ac5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<|channel>thought\n",
"<channel|>Gemma 4 is a family of open weights large language models developed by Google DeepMind. \n",
"\n",
"Key characteristics of the Gemma 4 family include:\n",
"\n",
"* **Open Weights:** These models are released with open weights, allowing developers and researchers to customize, fine-tune, and deploy them in various applications.\n",
"* **Multimodal Capabilities:** Gemma 4 models are capable of understanding and processing both text and image inputs.\n",
"* **Audio Processing:** Within the Gemma 4 family, the 2B and 4B models also have the capability to process audio input.\n",
"* **Text Generation:** While\n"
]
}
],
"source": [
"if False:\n",
" from unsloth import FastModel\n",
" model, tokenizer = FastModel.from_pretrained(\n",
" model_name = \"gemma_4_lora\", # YOUR MODEL YOU USED FOR TRAINING\n",
" max_seq_length = 2048,\n",
" load_in_4bit = True,\n",
" )\n",
"\n",
"messages = [{\n",
" \"role\": \"user\",\n",
" \"content\": [{\"type\" : \"text\", \"text\" : \"What is Gemma-4?\",}]\n",
"}]\n",
"inputs = tokenizer.apply_chat_template(\n",
" messages,\n",
" add_generation_prompt = True, # Must add for generation\n",
" return_tensors = \"pt\",\n",
" tokenize = True,\n",
" return_dict = True,\n",
").to(\"cuda\")\n",
"\n",
"from transformers import TextStreamer\n",
"_ = model.generate(\n",
" **inputs,\n",
" max_new_tokens = 128, # Increase for longer outputs!\n",
" # Recommended Gemma-3 settings!\n",
" temperature = 1.0, top_p = 0.95, top_k = 64,\n",
" streamer = TextStreamer(tokenizer, skip_prompt = True),\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f422JgM9sdVT"
},
"source": [
"### Saving to float16 for VLLM\n",
"\n",
"We also support saving to `float16` directly for deployment! We save it in the folder `gemma-4-finetune`. Set `if False` to `if True` to let it run!"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"id": "iHjt_SMYsd3P"
},
"outputs": [],
"source": [
"if False: # Change to True to save finetune!\n",
" model.save_pretrained_merged(\"gemma-4-finetune\", tokenizer)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "z6O48DbNIAr0"
},
"source": [
"If you want to upload / push to your Hugging Face account, set `if False` to `if True` and add your Hugging Face token and upload location!"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"id": "ZV-CiKPrIFG0"
},
"outputs": [],
"source": [
"if False: # Change to True to upload finetune\n",
" model.push_to_hub_merged(\n",
" \"HF_ACCOUNT/gemma-4-finetune\", tokenizer,\n",
" token = \"YOUR_HF_TOKEN\"\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TCv4vXHd61i7"
},
"source": [
"### GGUF / llama.cpp Conversion\n",
"To save to `GGUF` / `llama.cpp`, we support it natively now for all models! For now, you can convert easily to `Q8_0, F16 or BF16` precision. `Q4_K_M` for 4bit will come later!"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"id": "FqfebeAdT073"
},
"outputs": [],
"source": [
"if False: # Change to True to save to GGUF\n",
" model.save_pretrained_gguf(\n",
" \"gemma_4_finetune\",\n",
" tokenizer,\n",
" quantization_method = \"Q8_0\", # For now only Q8_0, BF16, F16 supported\n",
" )"
]
},
{
"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",
"execution_count": 28,
"metadata": {
"id": "ZgcJIhJ0I_es"
},
"outputs": [],
"source": [
"if False: # Change to True to upload GGUF\n",
" model.push_to_hub_gguf(\n",
" \"HF_ACCOUNT/gemma_4_finetune\",\n",
" tokenizer,\n",
" quantization_method = \"Q8_0\", # Only Q8_0, BF16, F16 supported\n",
" token = \"YOUR_HF_TOKEN\",\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pnz9QOYTMvbH"
},
"source": [
"Now, use the `gemma-4-finetune.gguf` file or `gemma-4-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)."
]
}
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