library_name: transformers license: other license_name: lfm1.0 license_link: LICENSE language:

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• vi pipeline_tag: image-text-to-text tags:
• liquid
• lfm2.5
• edge
• heretic
• uncensored
• decensored
• abliterated
• reproducible base_model:
• LiquidAI/LFM2.5-VL-3B

This is a decensored version of LiquidAI/LFM2.5-VL-3B, made using Heretic v1.4.0

[!TIP] This model is reproducible!

See the README in the reproduce directory for more information.

Abliteration parameters

Performance

LFM2.5-VL-3B

LFM2.5-VL-3B is a multimodal variant of LFM2.5, a family of hybrid models designed for on-device deployment. It builds on LFM2-VL-3B with further mid- and post-training. LFM2.5-VL-3B can process both text and images, and uses the LFM2.5-2.6B language model as its backbone, combined with a SigLIP2 NaFlex vision encoder.

• Better grounding: Improved grounding and object detection with natural language queries.
• Better OCR: Full page OCR with layout annotation. See layout annotation format for more information.
• Efficient inference: 228 tok/s on an Apple M5 Max and 116 tok/s on an AMD Ryzen AI Max+ 395, in under 3.3 GB of memory.

Find more information about LFM2.5-VL-3B in our release post.

lfm2_5_vl_3b_task_group_averages

[!NOTE] 💻 Demos: Try LFM2.5-VL-3B's vision understanding capabilities in a Hugging Face space without any setup: Vision-capable chat in your browser: allows you to upload images or use the webcam to capture stills and let the model interact with them.

Model Details

• LM Backbone: LFM2.5-2.6B
• Vision encoder: SigLIP2 NaFlex shape‑optimized 400M
• Vocabulary size: 128,000
• Context length: 32,768 tokens
• Languages: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish, Vietnamese, Thai, Indonesian, Hindi, Russian, Polish
• Native resolution processing: Uses SigLIP2's NaFlex; large images are split into non-overlapping 512×512 patches and a resized whole-image thumbnail.
• Generation parameters:
  • text: temperature=0.2, top_k=50, repetition_penalty=1.0
  • vision: Use the processor_config.json file.

We recommend using it for single-turn, high-throughput, low-latency tasks; for example, for near-realtime object detection in automotive applications, batch processing scanned documents with OCR with layout information for turning PDFs into searchable text, or for on-device translation of menus and road signs into your native language.

It is not recommended for long-context, reasoning-intensive tasks, such as visual web design, or answering highly technical questions about blueprints.

Chat Template

LFM2.5 uses a ChatML-like format. See the Chat Template documentation for details. Example:

<|startoftext|><|im_start|>system
You are a helpful assistant trained by Liquid AI.<|im_end|>
<|im_start|>user
What species is in this picture?<image><|im_end|>
<|im_start|>assistant

You can use tokenizer.apply_chat_template() to format your messages automatically.

[!TIP] Note: The apply_chat_template() method automatically inserts the <image> tag for each image in your message. Do not include <image> in your message content.

Inference

LFM2.5-VL is supported by many inference frameworks. See the Inference documentation for the full list.

Quick start

Quick start with Transformers (compatible with transformers>=5.0.0):

You will need torch, transformers, and torchvision.

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "LiquidAI/LFM2.5-VL-3B"

model = AutoModelForImageTextToText.from_pretrained(model_id, dtype=torch.bfloat16)
processor = AutoProcessor.from_pretrained(model_id)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://placecats.com/300/200"},
            {"type": "text", "text": "Describe this image."},
        ],
    }
]

inputs = processor.apply_chat_template(
    messages,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        do_sample=True,
        temperature=0.2,
        top_k=50,
        repetition_penalty=1.0,
        max_new_tokens=256,
    )

generated_ids = output_ids[:, inputs["input_ids"].shape[1] :]
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])

Tool Use

LFM2.5-VL-3B supports function calling in four steps:

1. Function definition: Provide the list of tools as a JSON object in the system prompt, or use tokenizer.apply_chat_template() with tools=....
2. Function call: By default, LFM2.5 writes Pythonic function calls (a Python list between <|tool_call_start|> and <|tool_call_end|> special tokens), as the assistant answer.
3. Function execution: Execute the call and return the result with the tool role.
4. Final answer: LFM2.5 interprets the tool output and returns a plain-text answer addressing the original prompt.

See the Tool Use documentation for the full guide. Example:

<|startoftext|><|im_start|>system
List of tools: [{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|im_end|>
<|im_start|>user
What is the current status of candidate ID 12345?<|im_end|>
<|im_start|>assistant
<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|>
<|im_start|>tool
[{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}]<|im_end|>
<|im_start|>assistant
The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|>

Layout Annotation Format

LFM2.5-VL-3B can do OCR with layout annotation. The layout annotation is a list of regions, each with a label, bounding box, and content. The format is:

image_index=<n> <label> [xmin, ymin, xmax, ymax]
<content>

image_index=<n> <label> [xmin, ymin, xmax, ymax]
<content>

image_index=<n> <label> [xmin, ymin, xmax, ymax]
<content>

...

where:

• image_index is the zero-based index of image
• <label> is one of these layout labels:
  • text
  • title
  • list
  • table
  • table_caption
  • table_footnote
  • image
  • image_block
  • image_caption
  • image_footnote
  • chart
  • equation
  • formula_number
  • code
  • code_caption
  • algorithm
  • aside_text
  • ref_text
  • phonetic
  • page_header
  • page_footer
  • page_number
  • page_footnote
• [xmin, ymin, xmax, ymax] are normalized integer coordinates in [0, 1000], same as our grounding format.
• <content> is the region's content:
  • plain text for text regions
  • LaTeX for equations
  • OTSL (Optimized Table Structure Language, introduced here by IBM) for tables
  • a short description for images and charts

There will be a blank line between each region.

To prompt the model to generate this structured output, use a system or user prompt that includes this:

Parse this document into its layout regions. The pages are provided as images in reading order. For every region, in reading order across all pages, output a header line immediately followed by the region's content:

image_index=<n> <label> [xmin, ymin, xmax, ymax]
<content>

where:
- image_index is the zero-based index of the page image the region appears on (0 for the first image, 1 for the second, and so on)
- <label> is one of these layout labels: text, title, list, table, table_caption, table_footnote, image, image_block, image_caption, image_footnote, chart, equation, formula_number, code, code_caption, algorithm, aside_text, ref_text, phonetic, page_header, page_footer, page_number, page_footnote
- [xmin, ymin, xmax, ymax] are normalized integer coordinates in [0, 1000]
- <content> is the region's content: plain text for text regions, LaTeX for equations, OTSL for tables, and a short description for images and charts

Separate each region block with one blank line. Return only the parsed regions.

Note that the layout annotation format is still experimental: it may change, may be unreliable, and may not be trivial to parse. We encourage users to try it out and provide feedback!

Fine-Tuning

We recommend fine-tuning LFM2.5-VL models for your specific use case to achieve the best results.

Performance

Benchmarks

LFM2.5-VL-3B significantly improves over LFM2-VL-3B in screen understanding, grounding, multi-image input, and tool use:

A selection of benchmarks for LFM2.5-VL-3B, including multimodal reasoning, math, and OCR (see our blog post for more benchmarks and details):

[1]: InternVL 3.5 doesn't support tool use.

[2]: English-only subset of OCRBenchv2.

On-device Inference

LFM2.5-VL-3B decodes 228 tokens/s on an Apple M5 Max and 116 tokens/s on an AMD Ryzen AI Max+ 395, and fits in about 3 GB of memory. It even reaches 20 tokens/s on a Galaxy S26 Ultra, so you can run it fully on-device.

lfm2_5_vl_3b_on-device_inference_TTFT

GPU Inference

On a single NVIDIA H100 with vLLM, LFM2.5-VL-3B reaches the highest output throughput of any model we tested, about 11K tokens per second at high concurrency, or nearly 1B tokens per day.

lfm2_5_vl_3b_throughput

Because it answers directly instead of reasoning, LFM2.5-VL-3B is quick to first token on a single H100, reaching about 34 ms on a 5-frame clip.

lfm2_5_vl_3b_ttft

Contact

• Got questions or want to connect? Join our Discord community
• If you are interested in custom solutions with edge deployment, please contact our sales team.

Citation

@article{liquidAI2026VL3B,
  author  = {Liquid AI},
  title   = {LFM2.5-VL-3B: A Better and Faster Vision-Language Model for the Edge},
  journal = {Liquid AI Blog},
  year    = {2026},
  note    = {www.liquid.ai/blog/lfm2-5-vl-3b},
}