Instructions to use ThreadAbort/LFM2.5-VL-450M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ThreadAbort/LFM2.5-VL-450M with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="ThreadAbort/LFM2.5-VL-450M")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("ThreadAbort/LFM2.5-VL-450M")
model = AutoModelForImageTextToText.from_pretrained("ThreadAbort/LFM2.5-VL-450M")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ThreadAbort/LFM2.5-VL-450M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ThreadAbort/LFM2.5-VL-450M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThreadAbort/LFM2.5-VL-450M",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/ThreadAbort/LFM2.5-VL-450M

SGLang

How to use ThreadAbort/LFM2.5-VL-450M with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ThreadAbort/LFM2.5-VL-450M" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThreadAbort/LFM2.5-VL-450M",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ThreadAbort/LFM2.5-VL-450M" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ThreadAbort/LFM2.5-VL-450M",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use ThreadAbort/LFM2.5-VL-450M with Docker Model Runner:
```
docker model run hf.co/ThreadAbort/LFM2.5-VL-450M
```

LFM2.5-VL-450M / README.md

ThreadAbort

Duplicate from LiquidAI/LFM2.5-VL-450M

80da3b5 about 1 month ago

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	---
	library_name: transformers
	license: other
	license_name: lfm1.0
	license_link: LICENSE
	language:
	- en
	- ja
	- ko
	- fr
	- es
	- de
	- ar
	- zh
	- pt
	pipeline_tag: image-text-to-text
	tags:
	- liquid
	- lfm2
	- lfm2-vl
	- edge
	- lfm2.5-vl
	- lfm2.5
	base_model: LiquidAI/LFM2.5-350M
	---

	<center>
	<div style="text-align: center;">
	<img
	src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png"
	alt="Liquid AI"
	style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
	/>
	</div>
	<div style="display: flex; justify-content: center; gap: 0.5em;">
	<a href="https://playground.liquid.ai/chat?model=lfm2.5-vl-450m"><strong>Try LFM</strong></a> • <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
	</div>
	</center>

	<br>

	# LFM2.5‑VL-450M

	LFM2.5‑VL-450M is [Liquid AI](https://www.liquid.ai/)'s refreshed version of the first vision-language model, [LFM2-VL-450M](https://huggingface.co/LiquidAI/LFM2-VL-450M), built on an updated backbone [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our [blog post](http://www.liquid.ai/blog/lfm2-5-vl-450m).

	* Enhanced instruction following on vision and language tasks.
	* Improved multilingual vision understanding in Arabic, Chinese, French, German, Japanese, Korean, Portuguese and Spanish.
	* Bounding box prediction and object detection for grounded visual understanding.
	* Function calling support for text-only input.

	🎥⚡️ You can try LFM2.5-VL-450M running locally in your browser with our real-time video stream captioning [WebGPU demo](https://huggingface.co/spaces/LiquidAI/LFM2.5-VL-450M-WebGPU) 🎥⚡️

	Alternatively, try the API model on the [Playground](https://playground.liquid.ai/chat?model=lfm2.5-vl-450m).

	## 📄 Model details

	LFM2.5-VL-450M is a general-purpose vision-language model with the following features:

	- LM Backbone: LFM2.5-350M
	- Vision encoder: SigLIP2 NaFlex shape‑optimized 86M
	- Context length: 32,768 tokens
	- Vocabulary size: 65,536
	- Languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish
	- 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
	- Inference-time flexibility: user-tunable maximum image tokens and tile count for speed/quality tradeoff without retraining
	- Generation parameters:
	- text: `temperature=0.1`, `min_p=0.15`, `repetition_penalty=1.05`
	- vision: `min_image_tokens=32` `max_image_tokens=256`, `do_image_splitting=True`

	\| Model \| Description \|
	\|-------\|-------------\|
	\| [LFM2.5-VL-450M](https://huggingface.co/LiquidAI/LFM2.5-VL-450M) \| Original model checkpoint in native format. Best for fine-tuning or inference with Transformers and vLLM. \|
	\| [LFM2.5-VL-450M-GGUF](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-GGUF) \| Quantized format for llama.cpp and compatible tools. Optimized for CPU inference and local deployment with reduced memory usage. \|
	\| [LFM2.5-VL-450M-ONNX](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-ONNX) \| ONNX Runtime format for cross-platform deployment. Enables hardware-accelerated inference across diverse environments (cloud, edge, mobile). \|
	\| [LFM2.5-VL-450M-MLX-8bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-8bit) \| MLX format for Apple Silicon. Optimized for fast on-device inference on Mac with [mlx-vlm](https://github.com/Blaizzy/mlx-vlm). Also available in [4bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-4bit), [5bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-5bit), [6bit](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-6bit), and [bf16](https://huggingface.co/LiquidAI/LFM2.5-VL-450M-MLX-bf16). \|

	We recommend using it for general vision-language workloads, captioning and object detection. It’s not well-suited for knowledge-intensive tasks or fine-grained OCR.

	### Chat Template

	LFM2.5-VL uses a ChatML-like format. See the [Chat Template documentation](https://docs.liquid.ai/lfm/key-concepts/chat-template#vision-models) for details.

	```
	<\|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\|>
	```

	You can use [`processor.apply_chat_template()`](https://huggingface.co/docs/transformers/en/chat_templating_multimodal) to format your messages automatically.

	## 🏃 Inference

	You can run LFM2.5-VL-450M with Hugging Face [`transformers`](https://github.com/huggingface/transformers) v5.1 or newer:

	```bash
	pip install transformers pillow
	```

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

	# Load model and processor
	model_id = "LiquidAI/LFM2.5-VL-450M"
	model = AutoModelForImageTextToText.from_pretrained(
	model_id,
	device_map="auto",
	dtype="bfloat16"
	)
	processor = AutoProcessor.from_pretrained(model_id)

	# Load image and create conversation
	url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.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 the iconic Statue of Liberty standing majestically on Liberty Island in New York City. The statue, a symbol of freedom and democracy, is prominently featured in the foreground, its greenish-gray hue contrasting beautifully with the surrounding water.
	```

	### Visual grounding

	LFM2.5-VL-450M supports bounding box prediction:

	```python
	url = "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
	image = load_image(url)
	query = "status"
	prompt = f'Detect all instances of: {query}. Response must be a JSON array: [{"label": ..., "bbox": [x1, y1, x2, y2]}, ...]. Coordinates are normalized to [0,1].'

	conversation = [
	{
	"role": "user",
	"content": [
	{"type": "image", "image": image},
	{"type": "text", "text": prompt},
	],
	},
	]

	# 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]

	# [{"label": "statue", "bbox": [0.3, 0.25, 0.4, 0.65]}]
	```

	### Tool Use

	LFM2.5 supports function calling for text only input by applying the chat template with the tokenizer. See the [Tool Use documentation](https://docs.liquid.ai/lfm/key-concepts/tool-use) for the full guide.

	```python
	tools = [{
	"name": "get_weather",
	"description": "Get current weather for a location",
	"parameters": {
	"type": "object",
	"properties": {"location": {"type": "string"}},
	"required": ["location"]
	}
	}]

	messages = [{"role": "user", "content": "What's the weather in Paris?"}]

	# Apply chat template with tools
	inputs = processor.tokenizer.apply_chat_template(
	messages,
	tools=tools,
	add_generation_prompt=True,
	return_tensors="pt",
	return_dict=True,
	)
	input_ids = inputs["input_ids"].to(model.device)
	outputs = model.generate(input_ids, max_new_tokens=256)
	response = processor.tokenizer.decode(outputs[0, input_ids.shape[1]:], skip_special_tokens=False)

	# <\|tool_call_start\|>[get_weather(location="Paris")]<\|tool_call_end\|>I am retrieving the current weather for Paris.<\|im_end\|>
	```

	\| Name \| Description \| Docs \| Notebook \|
	\|------\|-------------\|------\|----------\|
	\| [Transformers](https://github.com/huggingface/transformers) \| Simple inference with direct access to model internals. \| <a href="https://docs.liquid.ai/lfm/inference/transformers#vision-models">Link</a>\| <a href="https://colab.research.google.com/drive/1WVQpf4XrHgHFkP0FnlZfx2nK8PugvQNZ?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [vLLM](https://github.com/vllm-project/vllm) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/deployment/gpu-inference/vllm#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1sUfQlqAvuAVB4bZ6akYVQPGmHtTDUNpF?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [SGLang](https://github.com/vllm-project/vllm) \| High-throughput production deployments with GPU. \| <a href="https://docs.liquid.ai/deployment/gpu-inference/sglang#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1qJlAFag223yFOZGzuMIkYUFhybM9ao5g?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| [llama.cpp](https://github.com/ggml-org/llama.cpp) \| Cross-platform inference with CPU offloading. \| <a href="https://docs.liquid.ai/lfm/inference/llama-cpp#vision-models">Link</a> \| <a href="https://colab.research.google.com/drive/1q2PjE6O_AahakRlkTNJGYL32MsdUcj7b?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|

	## 🔧 Fine-tuning

	We recommend fine-tuning LFM2.5-VL-450M model on your use cases to maximize performance.

	\| Notebook \| Description \| Link \|
	\|-----------\|----------------------------------------------------------------------\|------\|
	\| SFT (Unsloth) \| Supervised Fine-Tuning with LoRA using Unsloth. \| <a href="https://colab.research.google.com/drive/1FaR2HSe91YDe88TG97-JVxMygl-rL6vB?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|
	\| SFT (TRL) \| Supervised Fine-Tuning with LoRA using TRL. \| <a href="https://colab.research.google.com/drive/10530_jt_Joa5zH2wgYlyXosypq1R7PIz?usp=sharing"><img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/vlOyMEjwHa_b_LXysEu2E.png" width="110" alt="Colab link"></a> \|


	## 📊 Performance

	LFM2.5-VL-450M improves over LFM2-VL-450M across both vision and language benchmarks, while also adding two new capabilities: bounding box prediction on RefCOCO-M and function calling support measured by BFCLv4.

	### Vision benchmarks

	\| Model \| MMStar \| RealWorldQA \| MMBench (dev en) \| MMMU (val) \| POPE \| MMVet \| BLINK \| InfoVQA (val) \| OCRBench \| MM-IFEval \| MMMB \| CountBench \| RefCOCO-M \|
	\|--------------------\|--------\|-------------\|------------------\|------------\|------\|-------\|-------\|---------------\|----------\|------------\|------\|------------\|-----------\|
	\| LFM2.5-VL-450M \| 43.00 \| 58.43 \| 60.91 \| 32.67 \| 86.93\| 41.10 \| 43.92 \| 43.02 \| 684 \| 45.00 \| 68.09\| 73.31 \| 81.28 \|
	\| LFM2-VL-450M \| 40.87 \| 52.03 \| 56.27 \| 34.44 \| 83.79\| 33.85 \| 42.61 \| 44.56 \| 657 \| 33.09 \| 54.29\| 47.64 \| - \|
	\| SmolVLM2-500M \| 38.20 \| 49.90 \| 52.32 \| 34.10 \| 82.67\| 29.90 \| 40.70 \| 24.64 \| 609 \| 11.27 \| 46.79\| 61.81 \| - \|

	All vision benchmark scores are obtained using [VLMEvalKit](https://github.com/open-compass/VLMEvalKit). Multilingual scores are based on the average of benchmarks translated by GPT-4.1-mini from English to Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

	### Language benchmarks

	\| Model \| GPQA \| MMLU Pro \| IFEval \| Multi-IF \| BFCLv4 \|
	\|--------------------\|------\|----------\|--------\|----------\|--------\|
	\| LFM2.5-VL-450M \| 25.66\| 19.32 \| 61.16 \| 34.63 \| 21.08 \|
	\| LFM2-VL-450M \| 23.13\| 17.22 \| 51.75 \| 26.21 \| - \|
	\| SmolVLM2-500M \| 23.84\| 13.57 \| 30.14 \| 6.82 \| - \|

	## 📬 Contact

	- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
	- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).

	## Citation

	```
	@article{liquidai2025lfm2,
	title={LFM2 Technical Report},
	author={Liquid AI},
	journal={arXiv preprint arXiv:2511.23404},
	year={2025}
	}
	```