iMatrix GGUF quants of https://huggingface.co/pszemraj/LFM2-VL-3B-heretic generated using ubergarm's imatrix-calibration-corpus-v02 dataset.

MMPROJ files can be found in the static quant repo.

iMatrix stats: PPL = 7.9876 +/- 0.04865

This is a decensored version of LiquidAI/LFM2-VL-3B, made using Heretic v1.0.1

Abliteration parameters

Performance

? Which trial do you want to use? (Use arrow keys)
 » [Trial 251] Refusals:  0/100, KL divergence: 0.08
   [Trial 386] Refusals:  1/100, KL divergence: 0.03
   [Trial 277] Refusals:  2/100, KL divergence: 0.03
   [Trial 389] Refusals:  3/100, KL divergence: 0.03
   -->[Trial 323] Refusals:  4/100, KL divergence: 0.02<--
   [Trial 324] Refusals:  6/100, KL divergence: 0.02
   [Trial 220] Refusals:  7/100, KL divergence: 0.02
   [Trial 357] Refusals:  8/100, KL divergence: 0.02
   [Trial 316] Refusals: 10/100, KL divergence: 0.01
   [Trial 230] Refusals: 12/100, KL divergence: 0.01
   [Trial 234] Refusals: 18/100, KL divergence: 0.01
   [Trial 379] Refusals: 27/100, KL divergence: 0.01
   [Trial 336] Refusals: 34/100, KL divergence: 0.01
   [Trial 345] Refusals: 35/100, KL divergence: 0.01
   [Trial 248] Refusals: 40/100, KL divergence: 0.01
   [Trial 398] Refusals: 60/100, KL divergence: 0.00
   [Trial 380] Refusals: 64/100, KL divergence: 0.00
   [Trial 363] Refusals: 66/100, KL divergence: 0.00
   [Trial 155] Refusals: 69/100, KL divergence: 0.00
   [Trial 310] Refusals: 70/100, KL divergence: 0.00

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LFM2‑VL

LFM2-VL-3B is the newest and most capable model in Liquid AI's multimodal LFM2-VL series, designed to process text and images with variable resolutions.
Built on the LFM2 backbone, it extends the architecture for higher-capacity reasoning and stronger visual understanding while retaining efficiency.

We are releasing the weights of the new 3B checkpoint—offering higher performance across benchmarks while remaining optimized for scalable deployment.

• Competitive multimodal performance among lightweight open models.
• Enhanced visual understanding and reasoning, particularly on fine-grained perception tasks
• Retains efficient inference with the same flexible architecture and user-tunable speed-quality tradeoffs
• Processes native resolutions up to 512×512 with intelligent patch-based handling for larger inputs

For more details, see the LFM2-VL-3B post and the LFM2 blog post.

📄 Model details

Due to their small size, we recommend fine-tuning LFM2-VL models on narrow use cases to maximize performance. They were trained for instruction following and lightweight agentic flows. Not intended for safety‑critical decisions.

Supported languages: English

Generation parameters: We recommend the following parameters:

• Text: temperature=0.1, min_p=0.15, repetition_penalty=1.05
• Vision: min_image_tokens=64 max_image_tokens=256, do_image_splitting=True

Chat template: LFM2-VL uses a ChatML-like chat template as follows:

<|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|>

Images are referenced with a sentinel (<image>), which is automatically replaced with the image tokens by the processor.

You can apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.

Architecture

• Hybrid backbone: Language model tower (LFM2-2.6B) paired with SigLIP2 NaFlex vision encoders (400M shape-optimized)
• Native resolution processing: Handles images up to 512×512 pixels without upscaling and preserves non-standard aspect ratios without distortion
• Tiling strategy: Splits large images into non-overlapping 512×512 patches and includes thumbnail encoding for global context
• Efficient token mapping: 2-layer MLP connector with pixel unshuffle reduces image tokens (e.g., 256×384 image → 96 tokens, 1000×3000 → 1,020 tokens)
• Inference-time flexibility: User-tunable maximum image tokens and patch count for speed/quality tradeoff without retraining

Training approach

• Builds on the LFM2 base model with joint mid-training that fuses vision and language capabilities using a gradually adjusted text-to-image ratio
• Applies joint SFT with emphasis on image understanding and vision tasks
• Leverages large-scale open-source datasets combined with in-house synthetic vision data, selected for balanced task coverage
• Follows a progressive training strategy: base model → joint mid-training → supervised fine-tuning

🏃 How to run LFM2-VL

You can run LFM2-VL with Hugging Face transformers via installing Transformers from source as follows:

pip install git+https://github.com/huggingface/transformers.git@87be5595081364ef99393feeaa60d71db3652679 pillow

Here is an example of how to generate an answer with transformers in Python:

from transformers import AutoProcessor, AutoModelForImageTextToText
from transformers.image_utils import load_image

# Load model and processor
model_id = "LiquidAI/LFM2-VL-3B"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16"
)
processor = AutoProcessor.from_pretrained(model_id)

# Load image and create conversation
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = load_image(url)
conversation = [
    {
        "role": "user",
        "content": [
            {"type": "image", "image": image},
            {"type": "text", "text": "What is in this image?"},
        ],
    },
]

# Generate Answer
inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
    tokenize=True,
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=64)
processor.batch_decode(outputs, skip_special_tokens=True)[0]

# This image captures a vibrant street scene in a Chinatown area. The focal point is a large red Chinese archway with gold and black accents, adorned with Chinese characters. Flanking the archway are two white stone lion statues, which are traditional guardians in Chinese culture.

You can directly run and test the model with this Colab notebook.

🔧 How to fine-tune

We recommend fine-tuning LFM2-VL models on your use cases to maximize performance.

📈 Performance

More benchmark scores are reported in our LFM2-VL-3B post. We obtained the scores for competitive models using VLMEvalKit. Qwen3-VL-2B is not listed in the results table, as its release occurred the day before.

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.