Image-Text-to-Text
Transformers
Safetensors
lfm2_vl
liquid
lfm2
lfm2-vl
edge
lfm2.5-vl
lfm2.5
conversational
Instructions to use LiquidAI/LFM2.5-VL-1.6B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2.5-VL-1.6B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="LiquidAI/LFM2.5-VL-1.6B") 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, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("LiquidAI/LFM2.5-VL-1.6B") model = AutoModelForMultimodalLM.from_pretrained("LiquidAI/LFM2.5-VL-1.6B", device_map="auto") 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 Settings
- vLLM
How to use LiquidAI/LFM2.5-VL-1.6B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/LFM2.5-VL-1.6B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LiquidAI/LFM2.5-VL-1.6B", "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/LiquidAI/LFM2.5-VL-1.6B
- SGLang
How to use LiquidAI/LFM2.5-VL-1.6B 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 "LiquidAI/LFM2.5-VL-1.6B" \ --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": "LiquidAI/LFM2.5-VL-1.6B", "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 "LiquidAI/LFM2.5-VL-1.6B" \ --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": "LiquidAI/LFM2.5-VL-1.6B", "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 LiquidAI/LFM2.5-VL-1.6B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM2.5-VL-1.6B
| {{- bos_token -}} | |
| {%- set keep_past_thinking = keep_past_thinking | default(false) -%} | |
| {%- set ns = namespace(system_prompt="") -%} | |
| {%- if messages[0]["role"] == "system" -%} | |
| {%- set sys_content = messages[0]["content"] -%} | |
| {%- if sys_content is not string -%} | |
| {%- for item in sys_content -%} | |
| {%- if item["type"] == "text" -%} | |
| {%- set ns.system_prompt = ns.system_prompt + item["text"] -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- else -%} | |
| {%- set ns.system_prompt = sys_content -%} | |
| {%- endif -%} | |
| {%- set messages = messages[1:] -%} | |
| {%- endif -%} | |
| {%- if tools -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%} | |
| {%- for tool in tools -%} | |
| {%- if tool is not string -%} | |
| {%- set tool = tool | tojson -%} | |
| {%- endif -%} | |
| {%- set ns.system_prompt = ns.system_prompt + tool -%} | |
| {%- if not loop.last -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ", " -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- set ns.system_prompt = ns.system_prompt + "]" -%} | |
| {%- endif -%} | |
| {%- if ns.system_prompt -%} | |
| {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- set ns.last_assistant_index = -1 -%} | |
| {%- for message in messages -%} | |
| {%- if message["role"] == "assistant" -%} | |
| {%- set ns.last_assistant_index = loop.index0 -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- for message in messages -%} | |
| {{- "<|im_start|>" + message["role"] + "\n" -}} | |
| {%- if message["content"] is not string -%} | |
| {%- set ns.content = "" -%} | |
| {%- for item in message["content"] -%} | |
| {%- if item["type"] == "image" -%} | |
| {%- set ns.content = ns.content + "<image>" -%} | |
| {%- elif item["type"] == "text" -%} | |
| {%- set ns.content = ns.content + item["text"] -%} | |
| {%- else -%} | |
| {%- set ns.content = ns.content + item | tojson -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {%- set content = ns.content -%} | |
| {%- else -%} | |
| {%- set content = message["content"] -%} | |
| {%- endif -%} | |
| {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%} | |
| {%- if "</think>" in content -%} | |
| {%- set content = content.split("</think>")[-1] | trim -%} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {{- content + "<|im_end|>\n" -}} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- "<|im_start|>assistant\n" -}} | |
| {%- endif -%} |