"
],
"text/html": [
"\n",
" \n",
" \n",
"
\n",
" [60/60 01:52, Epoch 0/1]\n",
"
\n",
" \n",
" \n",
" \n",
" | Step | \n",
" Training Loss | \n",
"
\n",
" \n",
" \n",
" \n",
" | 1 | \n",
" 16.656757 | \n",
"
\n",
" \n",
" | 2 | \n",
" 16.980976 | \n",
"
\n",
" \n",
" | 3 | \n",
" 16.712601 | \n",
"
\n",
" \n",
" | 4 | \n",
" 16.727814 | \n",
"
\n",
" \n",
" | 5 | \n",
" 16.962500 | \n",
"
\n",
" \n",
" | 6 | \n",
" 16.116722 | \n",
"
\n",
" \n",
" | 7 | \n",
" 14.512408 | \n",
"
\n",
" \n",
" | 8 | \n",
" 13.387623 | \n",
"
\n",
" \n",
" | 9 | \n",
" 13.035607 | \n",
"
\n",
" \n",
" | 10 | \n",
" 11.445036 | \n",
"
\n",
" \n",
" | 11 | \n",
" 11.030166 | \n",
"
\n",
" \n",
" | 12 | \n",
" 10.789420 | \n",
"
\n",
" \n",
" | 13 | \n",
" 10.289940 | \n",
"
\n",
" \n",
" | 14 | \n",
" 8.890602 | \n",
"
\n",
" \n",
" | 15 | \n",
" 8.548616 | \n",
"
\n",
" \n",
" | 16 | \n",
" 8.881482 | \n",
"
\n",
" \n",
" | 17 | \n",
" 8.596722 | \n",
"
\n",
" \n",
" | 18 | \n",
" 7.866756 | \n",
"
\n",
" \n",
" | 19 | \n",
" 7.271352 | \n",
"
\n",
" \n",
" | 20 | \n",
" 6.942740 | \n",
"
\n",
" \n",
" | 21 | \n",
" 6.609519 | \n",
"
\n",
" \n",
" | 22 | \n",
" 6.196516 | \n",
"
\n",
" \n",
" | 23 | \n",
" 6.201073 | \n",
"
\n",
" \n",
" | 24 | \n",
" 5.180562 | \n",
"
\n",
" \n",
" | 25 | \n",
" 5.545625 | \n",
"
\n",
" \n",
" | 26 | \n",
" 5.184031 | \n",
"
\n",
" \n",
" | 27 | \n",
" 4.980759 | \n",
"
\n",
" \n",
" | 28 | \n",
" 4.628974 | \n",
"
\n",
" \n",
" | 29 | \n",
" 4.494946 | \n",
"
\n",
" \n",
" | 30 | \n",
" 4.073771 | \n",
"
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" \n",
" | 31 | \n",
" 3.741437 | \n",
"
\n",
" \n",
" | 32 | \n",
" 4.186295 | \n",
"
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" \n",
" | 33 | \n",
" 4.365701 | \n",
"
\n",
" \n",
" | 34 | \n",
" 3.872540 | \n",
"
\n",
" \n",
" | 35 | \n",
" 4.338237 | \n",
"
\n",
" \n",
" | 36 | \n",
" 3.886693 | \n",
"
\n",
" \n",
" | 37 | \n",
" 3.677906 | \n",
"
\n",
" \n",
" | 38 | \n",
" 3.996698 | \n",
"
\n",
" \n",
" | 39 | \n",
" 3.182209 | \n",
"
\n",
" \n",
" | 40 | \n",
" 3.487222 | \n",
"
\n",
" \n",
" | 41 | \n",
" 3.710633 | \n",
"
\n",
" \n",
" | 42 | \n",
" 3.023618 | \n",
"
\n",
" \n",
" | 43 | \n",
" 3.892579 | \n",
"
\n",
" \n",
" | 44 | \n",
" 3.624817 | \n",
"
\n",
" \n",
" | 45 | \n",
" 3.996858 | \n",
"
\n",
" \n",
" | 46 | \n",
" 3.806535 | \n",
"
\n",
" \n",
" | 47 | \n",
" 3.086546 | \n",
"
\n",
" \n",
" | 48 | \n",
" 3.591533 | \n",
"
\n",
" \n",
" | 49 | \n",
" 3.202632 | \n",
"
\n",
" \n",
" | 50 | \n",
" 3.103097 | \n",
"
\n",
" \n",
" | 51 | \n",
" 3.445866 | \n",
"
\n",
" \n",
" | 52 | \n",
" 3.171514 | \n",
"
\n",
" \n",
" | 53 | \n",
" 3.542655 | \n",
"
\n",
" \n",
" | 54 | \n",
" 3.233638 | \n",
"
\n",
" \n",
" | 55 | \n",
" 3.718076 | \n",
"
\n",
" \n",
" | 56 | \n",
" 3.130233 | \n",
"
\n",
" \n",
" | 57 | \n",
" 3.508533 | \n",
"
\n",
" \n",
" | 58 | \n",
" 3.527344 | \n",
"
\n",
" \n",
" | 59 | \n",
" 3.706471 | \n",
"
\n",
" \n",
" | 60 | \n",
" 3.161814 | \n",
"
\n",
" \n",
"
"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Unsloth: Will smartly offload gradients to save VRAM!\n"
]
}
],
"source": [
"trainer_stats = trainer.train()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"cellView": "form",
"id": "pCqnaKmlO1U9",
"trusted": true,
"outputId": "c2a372bc-2110-438d-abb5-217aacacd66a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"149.1358 seconds used for training.\n",
"2.49 minutes used for training.\n",
"Peak reserved memory = 11.242 GB.\n",
"Peak reserved memory for training = 1.578 GB.\n",
"Peak reserved memory % of max memory = 77.196 %.\n",
"Peak reserved memory for training % of max memory = 10.836 %.\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 via Unsloth native inference! According to the `Gemma-4` team, the recommended settings for inference are `temperature = 1.0, top_p = 0.95, top_k = 64` but for this example we use `do_sample=False` for ASR."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"execution": {
"iopub.execute_input": "2025-07-20T13:57:34.004664Z",
"iopub.status.busy": "2025-07-20T13:57:34.004306Z",
"iopub.status.idle": "2025-07-20T13:57:59.332316Z",
"shell.execute_reply": "2025-07-20T13:57:59.331671Z",
"shell.execute_reply.started": "2025-07-20T13:57:34.004639Z"
},
"id": "kR3gIAX-SM2q",
"outputId": "3cdee613-1d20-4c15-fe02-d3b0b5a95b2f",
"trusted": true
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Sie sich direkt mich an und ist mir völlig klar, was politische Interessen gibt im Handel im Austausch mit Waren, dass es politische Einflüsse gibt. Die globale ist die Alternative soll es nicht sein.\n",
"\n"
]
}
],
"source": [
"messages = [\n",
" {\n",
" \"role\": \"system\",\n",
" \"content\": [\n",
" {\n",
" \"type\": \"text\",\n",
" \"text\": \"You are an assistant that transcribes speech accurately.\",\n",
" }\n",
" ],\n",
" },\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": [\n",
" {\"type\": \"audio\", \"audio\": test_audio['audio']['array']},\n",
" {\"type\": \"text\", \"text\": \"Please transcribe this audio.\"}\n",
" ]\n",
" }\n",
"]\n",
"\n",
"do_gemma_4_inference(messages, max_new_tokens = 256)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uMuVrWbjAzhc"
},
"source": [
"\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": 16,
"metadata": {
"id": "upcOlWe7A1vc",
"trusted": true,
"outputId": "181b8fb3-c43a-46f5-cd1e-d05ad6b3c7ab",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['gemma_4_lora/processor_config.json']"
]
},
"metadata": {},
"execution_count": 16
}
],
"source": [
"model.save_pretrained(\"gemma_4_lora\") # Local saving\n",
"processor.save_pretrained(\"gemma_4_lora\")\n",
"# model.push_to_hub(\"HF_ACCOUNT/gemma_4_lora\", token = \"YOUR_HF_TOKEN\") # Online saving\n",
"# processor.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": 17,
"metadata": {
"id": "MKX_XKs_BNZR",
"trusted": true,
"outputId": "9dbc6e55-b0ab-42a3-992c-985b3bd336e6",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"I am Gemma 4, a Large Language Model developed by Google DeepMind. I am an open weights model.\n"
]
}
],
"source": [
"if False:\n",
" from unsloth import FastModel\n",
" model, processor = 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 = processor.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-4 settings!\n",
" temperature = 1.0, top_p = 0.95, top_k = 64,\n",
" streamer = TextStreamer(processor, 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": 18,
"metadata": {
"id": "iHjt_SMYsd3P",
"trusted": true
},
"outputs": [],
"source": [
"if False: # Change to True to save finetune!\n",
" model.save_pretrained_merged(\"gemma-4\", processor)"
]
},
{
"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": 19,
"metadata": {
"id": "ZV-CiKPrIFG0",
"trusted": true
},
"outputs": [],
"source": [
"if False: # Change to True to upload finetune\n",
" model.push_to_hub_merged(\n",
" \"HF_ACCOUNT/gemma-4-finetune\", processor,\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": 20,
"metadata": {
"id": "FqfebeAdT073",
"trusted": true
},
"outputs": [],
"source": [
"if False: # Change to True to save to GGUF\n",
" model.save_pretrained_gguf(\n",
" \"gemma_4_finetune\",\n",
" processor,\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": 21,
"metadata": {
"id": "ZgcJIhJ0I_es",
"trusted": true
},
"outputs": [],
"source": [
"if False: # Change to True to upload GGUF\n",
" model.push_to_hub_gguf(\n",
" \"HF_ACCOUNT/gemma_4_finetune\",\n",
" processor,\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",
"\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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