Instructions to use LiquidAI/LFM3-VL-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM3-VL-2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="LiquidAI/LFM3-VL-2B", trust_remote_code=True) 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)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageTextToText model = AutoModelForImageTextToText.from_pretrained("LiquidAI/LFM3-VL-2B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LiquidAI/LFM3-VL-2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/LFM3-VL-2B" # 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/LFM3-VL-2B", "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/LFM3-VL-2B
- SGLang
How to use LiquidAI/LFM3-VL-2B 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/LFM3-VL-2B" \ --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/LFM3-VL-2B", "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/LFM3-VL-2B" \ --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/LFM3-VL-2B", "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/LFM3-VL-2B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM3-VL-2B
Download tokenizer_config.json from LiquidAI/LFM3-VL-2B: direct link, hf CLI and curl.
- Browser
- Download file 1.76 kB
-
https://huggingface.co/LiquidAI/LFM3-VL-2B/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://LiquidAI/LFM3-VL-2B/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/LiquidAI/LFM3-VL-2B/resolve/main/tokenizer_config.json
1.76 kB
| { | |
| "backend": "tokenizers", | |
| "bos_token": "<|startoftext|>", | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": [], | |
| "image_token": "<|image|>", | |
| "is_local": true, | |
| "local_files_only": false, | |
| "model_max_length": 1000000000000000019884624838656, | |
| "model_specific_special_tokens": { | |
| "image_token": "<|image|>", | |
| "video_token": "<|video|>", | |
| "vision_end_token": "<|vision_end|>", | |
| "vision_start_token": "<|vision_start|>" | |
| }, | |
| "pad_token": "<|pad|>", | |
| "processor_class": "Lfm3VlProcessor", | |
| "tokenizer_class": "TokenizersBackend", | |
| "video_token": "<|video|>", | |
| "vision_end_token": "<|vision_end|>", | |
| "vision_start_token": "<|vision_start|>", | |
| "response_template": { | |
| "defaults": { | |
| "role": "assistant" | |
| }, | |
| "start_anchor_pattern": "<\\|im_start\\|>assistant\\n", | |
| "fields": { | |
| "reasoning_content": { | |
| "open_pattern": "<think>\\n?", | |
| "close_pattern": "</think>\\s*", | |
| "content": "text" | |
| }, | |
| "tool_calls": { | |
| "open_pattern": "<tool_call>\\s*<function=(?P<name>[^\\n>]+)>", | |
| "close_pattern": "</tool_call>\\s*", | |
| "repeats": true, | |
| "content": "xml-inline", | |
| "content_args": { | |
| "tag_pattern": "<parameter=(?P<key>[^>\\n]+)>\\n?(?P<value>.*?)\\n?</parameter>(?=\\s*(?:<parameter=|</function>\\s*\\Z|\\Z))", | |
| "value_parser": { | |
| "name": "text", | |
| "args": { | |
| "strip": false | |
| } | |
| } | |
| }, | |
| "transform": { | |
| "type": "function", | |
| "function": { | |
| "name": "{name}", | |
| "arguments": "{content}" | |
| } | |
| } | |
| }, | |
| "content": { | |
| "close_pattern": "<\\|im_end\\|>\\s*", | |
| "content": "text" | |
| } | |
| } | |
| } | |
| } | |