Feature Extraction
Transformers
Safetensors
lfm2
fill-mask
encoder-only
multimodal
image-text-retrieval
image-text-matching
siglip2
lfm2.5
gptq
custom_code
compressed-tensors
Instructions to use konic-labs/LFM2.5-multimodal-encoder-230M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use konic-labs/LFM2.5-multimodal-encoder-230M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True) model = AutoModelForMaskedLM.from_pretrained("konic-labs/LFM2.5-multimodal-encoder-230M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| {{- 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" -}} | |
| {%- set content = message["content"] -%} | |
| {%- if content is not string -%} | |
| {%- set ns.content = "" -%} | |
| {%- for item in 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 -%} | |
| {%- 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 -%} |