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],
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [60/60 18:34, Epoch 0/1]\n",
"
\n",
" \n",
" \n",
" \n",
" | Step | \n",
" Training Loss | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 2.986776 | \n",
"
\n",
" \n",
" | 2 | \n",
" 3.611241 | \n",
"
\n",
" \n",
" | 3 | \n",
" 3.235800 | \n",
"
\n",
" \n",
" | 4 | \n",
" 3.207892 | \n",
"
\n",
" \n",
" | 5 | \n",
" 2.393348 | \n",
"
\n",
" \n",
" | 6 | \n",
" 1.994182 | \n",
"
\n",
" \n",
" | 7 | \n",
" 1.238167 | \n",
"
\n",
" \n",
" | 8 | \n",
" 1.074898 | \n",
"
\n",
" \n",
" | 9 | \n",
" 1.115454 | \n",
"
\n",
" \n",
" | 10 | \n",
" 0.414163 | \n",
"
\n",
" \n",
" | 11 | \n",
" 0.488537 | \n",
"
\n",
" \n",
" | 12 | \n",
" 0.463625 | \n",
"
\n",
" \n",
" | 13 | \n",
" 0.485965 | \n",
"
\n",
" \n",
" | 14 | \n",
" 0.257269 | \n",
"
\n",
" \n",
" | 15 | \n",
" 0.223266 | \n",
"
\n",
" \n",
" | 16 | \n",
" 0.299220 | \n",
"
\n",
" \n",
" | 17 | \n",
" 0.228006 | \n",
"
\n",
" \n",
" | 18 | \n",
" 0.165416 | \n",
"
\n",
" \n",
" | 19 | \n",
" 0.214501 | \n",
"
\n",
" \n",
" | 20 | \n",
" 0.163012 | \n",
"
\n",
" \n",
" | 21 | \n",
" 0.160776 | \n",
"
\n",
" \n",
" | 22 | \n",
" 0.227581 | \n",
"
\n",
" \n",
" | 23 | \n",
" 0.252964 | \n",
"
\n",
" \n",
" | 24 | \n",
" 0.154257 | \n",
"
\n",
" \n",
" | 25 | \n",
" 0.162097 | \n",
"
\n",
" \n",
" | 26 | \n",
" 0.201924 | \n",
"
\n",
" \n",
" | 27 | \n",
" 0.192899 | \n",
"
\n",
" \n",
" | 28 | \n",
" 0.150303 | \n",
"
\n",
" \n",
" | 29 | \n",
" 0.220560 | \n",
"
\n",
" \n",
" | 30 | \n",
" 0.132235 | \n",
"
\n",
" \n",
" | 31 | \n",
" 0.154113 | \n",
"
\n",
" \n",
" | 32 | \n",
" 0.265951 | \n",
"
\n",
" \n",
" | 33 | \n",
" 0.128669 | \n",
"
\n",
" \n",
" | 34 | \n",
" 0.118749 | \n",
"
\n",
" \n",
" | 35 | \n",
" 0.169299 | \n",
"
\n",
" \n",
" | 36 | \n",
" 0.130528 | \n",
"
\n",
" \n",
" | 37 | \n",
" 0.099769 | \n",
"
\n",
" \n",
" | 38 | \n",
" 0.122619 | \n",
"
\n",
" \n",
" | 39 | \n",
" 0.148352 | \n",
"
\n",
" \n",
" | 40 | \n",
" 0.202640 | \n",
"
\n",
" \n",
" | 41 | \n",
" 0.163365 | \n",
"
\n",
" \n",
" | 42 | \n",
" 0.065318 | \n",
"
\n",
" \n",
" | 43 | \n",
" 0.185360 | \n",
"
\n",
" \n",
" | 44 | \n",
" 0.124109 | \n",
"
\n",
" \n",
" | 45 | \n",
" 0.060225 | \n",
"
\n",
" \n",
" | 46 | \n",
" 0.107721 | \n",
"
\n",
" \n",
" | 47 | \n",
" 0.129032 | \n",
"
\n",
" \n",
" | 48 | \n",
" 0.118885 | \n",
"
\n",
" \n",
" | 49 | \n",
" 0.188991 | \n",
"
\n",
" \n",
" | 50 | \n",
" 0.109477 | \n",
"
\n",
" \n",
" | 51 | \n",
" 0.080716 | \n",
"
\n",
" \n",
" | 52 | \n",
" 0.167722 | \n",
"
\n",
" \n",
" | 53 | \n",
" 0.136780 | \n",
"
\n",
" \n",
" | 54 | \n",
" 0.125553 | \n",
"
\n",
" \n",
" | 55 | \n",
" 0.158793 | \n",
"
\n",
" \n",
" | 56 | \n",
" 0.392148 | \n",
"
\n",
" \n",
" | 57 | \n",
" 0.100937 | \n",
"
\n",
" \n",
" | 58 | \n",
" 0.066817 | \n",
"
\n",
" \n",
" | 59 | \n",
" 0.181567 | \n",
"
\n",
" \n",
" | 60 | \n",
" 0.087730 | \n",
"
\n",
" \n",
"
"
]
},
"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": [
"\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",
"[ [ B _ { n } ^ { + } , b _ { 2 } ^ { - } ] , b _ { 2 } ^ { + } ] = n B _ { n } ^ { + } , \\quad [ [ B _ { n } ^ { - } , b _ { 2 } ^ { + } ] , b _ { 2 } ^ { - } ] = n B _ { n } ^ { - } .\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": [
"\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",
"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,
"metadata": {
"id": "iHjt_SMYsd3P"
},
"outputs": [],
"source": [
"# 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",
"\n",
"

\n",
"

\n",
"

\n",
"\n",
" Join Discord if you need help + ⭐️
Star us on Github ⭐️\n",
"
\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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