Image-Text-to-Text
Transformers
Safetensors
lfm2_vl
liquid
lfm2
lfm2-vl
edge
lfm2.5-vl
lfm2.5
conversational
Instructions to use ThreadAbort/LFM2.5-VL-450M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| {{- bos_token -}} | |
| {%- set keep_past_thinking = keep_past_thinking | default(false) -%} | |
| {%- macro format_arg_value(arg_value) -%} | |
| {%- if arg_value is string -%} | |
| {{- '"' + arg_value + '"' -}} | |
| {%- elif arg_value is mapping -%} | |
| {{- arg_value | tojson -}} | |
| {%- else -%} | |
| {{- arg_value | string -}} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- macro parse_content(content) -%} | |
| {%- if content is string -%} | |
| {{- content -}} | |
| {%- else -%} | |
| {%- set _ns = namespace(result="") -%} | |
| {%- for item in content -%} | |
| {%- if item.type == "image" -%} | |
| {%- set _ns.result = _ns.result + "<image>" -%} | |
| {%- elif item.type == "text" -%} | |
| {%- set _ns.result = _ns.result + item.text -%} | |
| {%- else -%} | |
| {%- set _ns.result = _ns.result + item | tojson -%} | |
| {%- endif -%} | |
| {%- endfor -%} | |
| {{- _ns.result -}} | |
| {%- endif -%} | |
| {%- endmacro -%} | |
| {%- macro render_tool_calls(tool_calls) -%} | |
| {%- set tool_calls_ns = namespace(tool_calls=[]) -%} | |
| {%- for tool_call in tool_calls -%} | |
| {%- set func_name = tool_call.function.name -%} | |
| {%- set func_args = tool_call.function.arguments -%} | |
| {%- set args_ns = namespace(arg_strings=[]) -%} | |
| {%- for arg_name, arg_value in func_args.items() -%} | |
| {%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%} | |
| {%- endfor -%} | |
| {%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%} | |
| {%- endfor -%} | |
| {{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}} | |
| {%- endmacro -%} | |
| {%- set ns = namespace(system_prompt="", last_assistant_index=-1) -%} | |
| {%- if messages[0].role == "system" -%} | |
| {%- if messages[0].content is defined -%} | |
| {%- set ns.system_prompt = parse_content(messages[0].content) -%} | |
| {%- endif -%} | |
| {%- set messages = messages[1:] -%} | |
| {%- endif -%} | |
| {%- if tools -%} | |
| {%- set ns.system_prompt = ns.system_prompt + ("\n\n" if ns.system_prompt else "") + "Today's date: " + strftime_now("%Y-%m-%d") + "\n\nList of tools: " + (tools | tojson) -%} | |
| {%- endif -%} | |
| {%- if ns.system_prompt -%} | |
| {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- 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.role == "assistant" -%} | |
| {%- generation -%} | |
| {%- if message.thinking is defined and (keep_past_thinking or loop.index0 == ns.last_assistant_index) -%} | |
| {{- "<think>" + message.thinking + "</think>" -}} | |
| {%- endif -%} | |
| {%- if message.tool_calls is defined -%} | |
| {{- render_tool_calls(message.tool_calls) -}} | |
| {%- endif -%} | |
| {%- if message.content is defined -%} | |
| {%- set content = parse_content(message.content) -%} | |
| {%- if 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 + ("" if (continue_final_message and loop.last) else "<|im_end|>\n") -}} | |
| {%- endif -%} | |
| {%- endgeneration -%} | |
| {%- else %} | |
| {%- if message.content is defined -%} | |
| {{- parse_content(message.content) + "<|im_end|>\n" -}} | |
| {%- endif -%} | |
| {%- endif %} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- "<|im_start|>assistant\n" -}} | |
| {%- endif -%} |