Text Generation
Transformers
Safetensors
lfm2_moe
lfm2.5
liquid
conversational
uncensored
abliterated
Instructions to use blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated") model = AutoModelForMultimodalLM.from_pretrained("blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated
- SGLang
How to use blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated 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 "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated" \ --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": "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated" \ --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": "blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated with Docker Model Runner:
docker model run hf.co/blockblockblock/LFM2.5-8B-A1B-uncensored-abliterated
| {{- bos_token -}} | |
| {%- set preserve_thinking = preserve_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_user_index=-1) -%} | |
| {%- if messages[0]["role"] == "system" -%} | |
| {%- if messages[0].get("content") -%} | |
| {%- 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" 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 -%} | |
| {%- for message in messages -%} | |
| {%- if message["role"] == "user" -%} | |
| {%- set ns.last_user_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 (preserve_thinking or loop.index0 > ns.last_user_index) -%} | |
| {{- "<think>" + message.thinking + "</think>" -}} | |
| {%- endif -%} | |
| {%- set _cfm_tag = "CONTINUE_FINAL_MESSAGE_TAG " -%} | |
| {%- set _has_cfm = false -%} | |
| {%- if message.content is defined -%} | |
| {%- set content = parse_content(message.content) -%} | |
| {%- if not (preserve_thinking or loop.index0 > ns.last_user_index) -%} | |
| {%- if "</think>" in content -%} | |
| {%- set content = content.split("</think>")[-1] | trim -%} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- if message.tool_calls is defined and content.endswith(_cfm_tag) -%} | |
| {%- set _has_cfm = true -%} | |
| {%- set _trunc_len = (content | length) - (_cfm_tag | length) -%} | |
| {{- content[:_trunc_len] -}} | |
| {%- else -%} | |
| {{- content -}} | |
| {%- endif -%} | |
| {%- endif -%} | |
| {%- if message.tool_calls is defined -%} | |
| {{- render_tool_calls(message.tool_calls) -}} | |
| {%- endif -%} | |
| {%- if _has_cfm -%} | |
| {{- _cfm_tag -}} | |
| {%- endif -%} | |
| {{- "<|im_end|>\n" -}} | |
| {%- endgeneration -%} | |
| {%- else %} | |
| {%- if message.get("content") -%} | |
| {{- parse_content(message["content"]) -}} | |
| {%- endif -%} | |
| {{- "<|im_end|>\n" -}} | |
| {%- endif %} | |
| {%- endfor -%} | |
| {%- if add_generation_prompt -%} | |
| {{- "<|im_start|>assistant\n" -}} | |
| {%- endif -%} |