LFM2.5 VL (1.6B) Vision
💧 LFM2.5-VL - SFT with Unsloth
To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!
To install Unsloth on your own computer, follow the installation instructions on our Github page here.
You will learn how to do data prep, how to train, how to run the model, & how to save it
News
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New in Reinforcement Learning: FP8 RL • Vision RL • Standby • gpt-oss RL
Visit our docs for all our model uploads and notebooks.
Installation
Unsloth
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. 🦥 Unsloth Zoo will now patch everything to make training faster! ==((====))== Unsloth 2025.12.8: Fast Lfm2 patching. Transformers: 5.0.0.dev0. \\ /| NVIDIA L4. Num GPUs = 1. Max memory: 22.161 GB. Platform: Linux. O^O/ \_/ \ Torch: 2.9.0+cu126. CUDA: 8.9. CUDA Toolkit: 12.6. Triton: 3.5.0 \ / Bfloat16 = TRUE. FA [Xformers = 0.0.33.post1. FA2 = False] "-____-" Free license: http://github.com/unslothai/unsloth Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.
Loading weights: 0%| | 0/589 [00:00<?, ?it/s]
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads. WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
We now add LoRA adapters so we only need to update a small amount of parameters!
Unsloth: Making `model.base_model.model.model.language_model` require gradients
Data Prep
We'll be using a sampled dataset of handwritten maths formulas. The goal is to convert these images into a computer readable form - ie in LaTeX form, so we can render it. This can be very useful for complex formulas.
You can access the dataset here. The full dataset is here. LFM-VL renders ChatML conversations with images like below:
<|startoftext|><|im_start|>system
You are a helpful multimodal assistant by Liquid AI.<|im_end|>
<|im_start|>user
<image>Describe this image.<|im_end|>
<|im_start|>assistant
This image shows a Caenorhabditis elegans (C. elegans) nematode.<|im_end|>
"<|startoftext|><|im_start|>user\n<image>What's in this image?<|im_end|>\n<|im_start|>assistant\nI can see a cat sitting on a couch.<|im_end|>\n"
We get the first 3000 rows of the dataset
Let's take an overview look at the dataset. We shall see what the 3rd image is, and what caption it had.
Dataset({
, features: ['image', 'text'],
, num_rows: 3000
,}) 
'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 \\} \\ .' To format the dataset, all vision finetuning tasks should be formatted as follows:
[
{ "role": "user",
"content": [{"type": "text", "text": Q}, {"type": "image", "image": image} ]
},
{ "role": "assistant",
"content": [{"type": "text", "text": A} ]
},
]
Let's convert the dataset into the "correct" format for finetuning:
We look at how the conversations are structured for the first example:
{'messages': [{'role': 'user',
, 'content': [{'type': 'text',
, 'text': 'Write the LaTeX representation for this image.'},
, {'type': 'image',
, 'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=160x40>}]},
, {'role': 'assistant',
, 'content': [{'type': 'text',
, 'text': '{ \\frac { N } { M } } \\in { \\bf Z } , { \\frac { M } { P } } \\in { \\bf Z } , { \\frac { P } { Q } } \\in { \\bf Z }'}]}]} Let's first see before we do any finetuning what the model outputs for the first example!
H ^ { \prime } = \beta N \int d \lambda \Big \{ \frac { 1 } { 2 \beta ^ { 2 } N ^ { 2 } } \partial _ { \lambda } \zeta ^ { \dagger } \partial _ { \lambda } \zeta + V ( \lambda ) \zeta ^ { \dagger } \zeta \Big \} \ .<|im_end|>
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.
warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.
Unsloth: Model does not have a default image size - using 512
GPU = NVIDIA L4. Max memory = 22.161 GB. 3.109 GB of memory reserved.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 \\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 30 O^O/ \_/ \ Batch size per device = 2 | Gradient accumulation steps = 4 \ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8 "-____-" Trainable parameters = 9,142,272 of 1,605,768,176 (0.57% trained)
Unsloth: Will smartly offload gradients to save VRAM!
88.8178 seconds used for training. 1.48 minutes used for training. Peak reserved memory = 3.639 GB. Peak reserved memory for training = 0.53 GB. Peak reserved memory % of max memory = 16.421 %. Peak reserved memory for training % of max memory = 2.392 %.
Inference
Let's run the model! You can change the instruction and input - leave the output blank!
We use min_p = 0.1 and temperature = 1.5. Read this Tweet for more information on why.
H ^ { \prime } = \beta N \int d \lambda \Big \{ \frac { 1 } { 2 \beta ^ { 2 } N ^ { 2 } } \partial _ { \lambda } \zeta ^ { \dagger } \partial _ { \lambda } \zeta + V ( \lambda ) \zeta ^ { \dagger } \zeta \Big \} \ .<|im_end|>
['lora_model/processor_config.json']
Now if you want to load the LoRA adapters we just saved for inference, set False to True:
\frac { N } { M } \in { \bf Z } , \frac { M } { P } \in { \bf Z } , \frac { P } { Q } \in { \bf Z }<|im_end|> 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 for more deployment options.
GGUF / llama.cpp Conversion
To save to GGUF / llama.cpp, we support it natively now! We clone llama.cpp and we default save it to q8_0. We allow all methods like q4_k_m. Use save_pretrained_gguf for local saving and push_to_hub_gguf for uploading to HF.
Some supported quant methods (full list on our docs page):
q8_0- Fast conversion. High resource use, but generally acceptable.q4_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.q5_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
[NEW] To finetune and auto export to Ollama, try our Ollama notebook
Now, use the lfm_finetune.Q8_0.gguf file or lfm_finetune.Q4_K_M.gguf file in llama.cpp.
And we're done! If you have any questions on Unsloth, we have a Discord 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:
- Train your own reasoning model - Llama GRPO notebook Free Colab
- Saving finetunes to Ollama. Free notebook
- Llama 3.2 Vision finetuning - Radiography use case. Free Colab
- See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our documentation!
