#!/usr/bin/env python
# coding: utf-8
# To run this, press "*Runtime*" and press "*Run all*" on a **free** Tesla T4 Google Colab instance!
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Run GGUF models on Mac, Windows & Linux |
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# Train MoEs - DeepSeek, GLM, Qwen and gpt-oss 12x faster with 35% less VRAM. [Blog](https://unsloth.ai/docs/new/faster-moe)
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# Ultra Long-Context Reinforcement Learning is here with 7x more context windows! [Blog](https://unsloth.ai/docs/new/grpo-long-context)
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# 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)
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# 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).
# # ### Installation
#
# # In[ ]:
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# get_ipython().run_cell_magic('capture', '', 'import 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;\n')
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# get_ipython().run_cell_magic('capture', '', '!pip install --no-deps --upgrade timm # For Gemma 4 vision/audio\n')
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# # ### Unsloth
# In[ ]:
from unsloth import FastVisionModel # FastLanguageModel for LLMs
import torch
gemma4_models = [
# Gemma-4 instruct models:
"unsloth/gemma-4-E2B-it",
"unsloth/gemma-4-E4B-it",
"unsloth/gemma-4-31B-it",
"unsloth/gemma-4-26B-A4B-it",
# Gemma-4 base models:
"unsloth/gemma-4-E2B",
"unsloth/gemma-4-E4B",
"unsloth/gemma-4-31B",
"unsloth/gemma-4-26B-A4B",
] # More models at https://huggingface.co/unsloth
model, processor = FastVisionModel.from_pretrained(
"unsloth/gemma-4-E2B-it",
load_in_4bit = False, # Use 4bit to reduce memory use. False for 16bit LoRA.
use_gradient_checkpointing = "unsloth", # True or "unsloth" for long context
)
# We now add LoRA adapters for parameter efficient fine-tuning, allowing us to train only 1% of all model parameters efficiently.
#
# **[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!
# In[ ]:
model = FastVisionModel.get_peft_model(
model,
finetune_vision_layers = True, # False if not finetuning vision layers
finetune_language_layers = True, # False if not finetuning language layers
finetune_attention_modules = True, # False if not finetuning attention layers
finetune_mlp_modules = True, # False if not finetuning MLP layers
r = 32, # The larger, the higher the accuracy, but might overfit
lora_alpha = 32, # Recommended alpha == r at least
lora_dropout = 0,
bias = "none",
random_state = 3407,
use_rslora = False, # We support rank stabilized LoRA
loftq_config = None, # And LoftQ
target_modules = "all-linear", # Optional now! Can specify a list if needed
)
#
# ### Data Prep
# 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.
#
# 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).
# In[ ]:
from datasets import load_dataset
dataset = load_dataset("unsloth/LaTeX_OCR", split = "train")
# Let's take an overview of the dataset. We'll examine the second image and its corresponding caption.
# In[ ]:
dataset
# In[ ]:
dataset[2]["image"]
# In[ ]:
dataset[2]["text"]
# We can also render LaTeX directly in the browser!
# In[ ]:
from IPython.display import display, Math, Latex
latex = dataset[3]["text"]
display(Math(latex))
# To format the dataset, all vision fine-tuning tasks should follow this format:
#
# ```python
# [
# {
# "role": "user",
# "content": [
# {"type": "text", "text": instruction},
# {"type": "image", "image": sample["image"]},
# ],
# },
# {
# "role": "user",
# "content": [
# {"type": "text", "text": instruction},
# {"type": "image", "image": sample["image"]},
# ],
# },
# ]
# ```
# In[ ]:
instruction = "Write the LaTeX representation for this image."
def convert_to_conversation(sample):
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": instruction},
{"type": "image", "image": sample["image"]},
],
},
{"role": "assistant", "content": [{"type": "text", "text": sample["text"]}]},
]
return {"messages": conversation}
pass
# Let's convert the dataset into the "correct" format for finetuning:
# In[ ]:
converted_dataset = [convert_to_conversation(sample) for sample in dataset]
# The first example is now structured like below:
# In[ ]:
converted_dataset[0]
# Lets take the Gemma 4 instruction chat template and use it in our base model
# In[ ]:
from unsloth import get_chat_template
processor = get_chat_template(
processor,
"gemma-4"
)
# 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.
# In[ ]:
image = dataset[2]["image"]
instruction = "Write the LaTeX representation for this image."
messages = [
{
"role": "user",
"content": [{"type": "image"}, {"type": "text", "text": instruction}],
}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt = True)
inputs = processor(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(processor, skip_prompt = True)
result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)
# You can see it's absolutely terrible! It doesn't follow instructions at all
#
# ### Train the model
# 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!
#
# We use our new `UnslothVisionDataCollator` which will help in our vision finetuning setup.
# In[ ]:
from unsloth.trainer import UnslothVisionDataCollator
from trl import SFTTrainer, SFTConfig
trainer = SFTTrainer(
model = model,
train_dataset = converted_dataset,
processing_class = processor.tokenizer,
data_collator = UnslothVisionDataCollator(model, processor),
args = SFTConfig(
per_device_train_batch_size = 1,
gradient_accumulation_steps = 4,
max_grad_norm = 0.3,
warmup_ratio = 0.03,
max_steps = 60,
# num_train_epochs = 2, # Set this instead of max_steps for full training runs
learning_rate = 2e-4,
logging_steps = 1,
save_strategy = "steps",
optim = "adamw_8bit",
weight_decay = 0.001,
lr_scheduler_type = "cosine",
seed = 3407,
output_dir = "outputs",
report_to = "none", # For Weights and Biases or others
# You MUST put the below items for vision finetuning:
remove_unused_columns = False,
dataset_text_field = "",
dataset_kwargs = {"skip_prepare_dataset": True},
max_length = 2048,
)
)
# In[ ]:
# @title Show current memory stats
gpu_stats = torch.cuda.get_device_properties(0)
start_gpu_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
max_memory = round(gpu_stats.total_memory / 1024 / 1024 / 1024, 3)
print(f"GPU = {gpu_stats.name}. Max memory = {max_memory} GB.")
print(f"{start_gpu_memory} GB of memory reserved.")
# In[ ]:
trainer_stats = trainer.train()
# In[ ]:
# @title Show final memory and time stats
used_memory = round(torch.cuda.max_memory_reserved() / 1024 / 1024 / 1024, 3)
used_memory_for_lora = round(used_memory - start_gpu_memory, 3)
used_percentage = round(used_memory / max_memory * 100, 3)
lora_percentage = round(used_memory_for_lora / max_memory * 100, 3)
print(f"{trainer_stats.metrics['train_runtime']} seconds used for training.")
print(
f"{round(trainer_stats.metrics['train_runtime']/60, 2)} minutes used for training."
)
print(f"Peak reserved memory = {used_memory} GB.")
print(f"Peak reserved memory for training = {used_memory_for_lora} GB.")
print(f"Peak reserved memory % of max memory = {used_percentage} %.")
print(f"Peak reserved memory for training % of max memory = {lora_percentage} %.")
#
# ### Inference
# Let's run the model! You can modify the instruction and input—just leave the output blank.
#
# We'll use the best hyperparameters for inference on Gemma: `top_p=0.95`, `top_k=64`, and `temperature=1.0`.
# In[ ]:
image = dataset[10]["image"]
instruction = "Write the LaTeX representation for this image."
messages = [
{
"role": "user",
"content": [{"type": "image"}, {"type": "text", "text": instruction}],
}
]
input_text = processor.apply_chat_template(messages, add_generation_prompt = True)
inputs = processor(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(processor, skip_prompt = True)
result = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)
#
# ### Saving, loading finetuned models
# To save the final model as LoRA adapters, use Hugging Face’s `push_to_hub` for online saving, or `save_pretrained` for local storage.
#
# **[NOTE]** This ONLY saves the LoRA adapters, and not the full model. To save to 16bit or GGUF, scroll down!
# In[ ]:
model.save_pretrained("gemma_4_lora") # Local saving
processor.save_pretrained("gemma_4_lora")
# model.push_to_hub("your_name/gemma_4_lora", token = "YOUR_HF_TOKEN") # Online saving
# processor.push_to_hub("your_name/gemma_4_lora", token = "YOUR_HF_TOKEN") # Online saving
# Now if you want to load the LoRA adapters we just saved for inference, set `False` to `True`:
# In[ ]:
if False:
from unsloth import FastVisionModel
model, processor = FastVisionModel.from_pretrained(
model_name = "gemma_4_lora", # YOUR MODEL YOU USED FOR TRAINING
load_in_4bit = True, # Set to False for 16bit LoRA
)
sample = dataset[1]
image = sample["image"].convert("RGB")
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": sample["text"],
},
{
"type": "image",
},
],
},
]
input_text = processor.apply_chat_template(messages, add_generation_prompt = True)
inputs = processor(
image,
input_text,
add_special_tokens = False,
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(processor.tokenizer, skip_prompt = True)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,
use_cache = True, temperature = 1.0, top_p = 0.95, top_k = 64)
# ### Saving to float16 for VLLM
#
# 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.
# In[ ]:
# Select ONLY 1 to save! (Both not needed!)
# Save locally to 16bit
if False: model.save_pretrained_merged("unsloth_finetune", processor,)
# To export and save to your Hugging Face account
if False: model.push_to_hub_merged("YOUR_USERNAME/unsloth_finetune", processor, token = "YOUR_HF_TOKEN")
# 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!
#
# Some other resources:
# 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)
# 2. Saving finetunes to Ollama. [Free notebook](https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3_(8B)-Ollama.ipynb)
# 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)
# 4. See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our [documentation](https://unsloth.ai/docs/get-started/unsloth-notebooks)!
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# Join Discord if you need help + ⭐️
Star us on Github ⭐️
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