Instructions to use LiquidAI/LFM2-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LiquidAI/LFM2-1.2B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LiquidAI/LFM2-1.2B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("LiquidAI/LFM2-1.2B") model = AutoModelForCausalLM.from_pretrained("LiquidAI/LFM2-1.2B", device_map="auto") 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 LiquidAI/LFM2-1.2B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LiquidAI/LFM2-1.2B" # 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/LFM2-1.2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/LiquidAI/LFM2-1.2B
- SGLang
How to use LiquidAI/LFM2-1.2B 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/LFM2-1.2B" \ --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/LFM2-1.2B", "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 "LiquidAI/LFM2-1.2B" \ --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/LFM2-1.2B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use LiquidAI/LFM2-1.2B with Docker Model Runner:
docker model run hf.co/LiquidAI/LFM2-1.2B
Fix chat template: render assistant tool_calls
Browse filesAssistant messages carrying `tool_calls` render to nothing under the current
template: the message loop ends in a single `{{- content + "<|im_end|>\n" -}}`
with no branch for tool calls. Two consequences, both reproduced on
llama.cpp `server-rocm-b10088`:
1. llama.cpp probes the template at startup, sees that a tool call renders to
nothing, and reports `supports_tool_calls: false` /
`supports_parallel_tool_calls: false` in `/props`. It then constrains
generation to at most one call, so the second call of a parallel request is
never sampled.
2. Replaying a conversation that already contains an assistant tool call
produces an empty assistant turn, while the following `tool` result is still
rendered -- the model sees an answer to a question it has no record of asking.
**This PR** adds the `format_arg_value` / `render_tool_calls` macros (taken
verbatim from the latest LFM2.5 template) and one branch that emits them, so a
tool call renders as
`<|tool_call_start|>[name(arg='value'), ...]<|tool_call_end|>`.
It also changes `message["content"]` to `message.get("content")`: an assistant
message with `tool_calls` and no `content` key -- the ordinary OpenAI-style
payload -- otherwise raises
`TypeError: Object of type Undefined is not JSON serializable`.
Nothing else changes. The tool-marker convention, thinking handling and
formatting are untouched, and a conversation without tools renders
byte-identically to the current template.
**Verified** on llama.cpp: `chat_template_caps` goes false -> true, parallel
tool calls 1 -> 2, and the replayed assistant turn is preserved. transformers
and minja render identically. Benchmark runs (IFBench, MATH-500, GPQA Diamond,
AA-Omniscience, BFCL v4 multiple/multi_turn_base/irrelevance) are in flight;
please hold merge until those confirm no regression.
- chat_template.jinja +37 -2
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{{- bos_token -}}
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{%- set system_prompt = "" -%}
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{%- set ns = namespace(system_prompt="") -%}
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{%- if messages[0]["role"] == "system" -%}
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@@ -23,14 +50,22 @@
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{%- endif -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message["role"] + "\n" -}}
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{%- set content = message
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{%- if content is not string -%}
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{%- set content = content | tojson -%}
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{%- endif -%}
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{%- if message["role"] == "tool" -%}
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{%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
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{%- endif -%}
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{
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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{{- bos_token -}}
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{%- macro format_arg_value(arg_value) -%}
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{%- if arg_value is string -%}
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{{- "'" + (arg_value | replace("\\", "\\\\") | replace("'", "\\'") | replace("\n", "\\n") | replace("\r", "\\r")) + "'" -}}
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{%- elif arg_value is mapping or arg_value is iterable -%}
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{{- arg_value | tojson -}}
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{%- else -%}
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{{- arg_value | string -}}
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{%- endif -%}
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{%- endmacro -%}
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{%- macro render_tool_calls(tool_calls) -%}
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{%- set tool_calls_ns = namespace(tool_calls=[]) -%}
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{%- for tool_call in tool_calls -%}
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{%- set func = tool_call["function"] if "function" in tool_call else tool_call -%}
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{%- set func_name = func["name"] -%}
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{%- set func_args = func.get("arguments") -%}
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{%- set args_ns = namespace(arg_strings=[]) -%}
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{%- if func_args is mapping -%}
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{%- for arg_name, arg_value in func_args.items() -%}
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{%- set args_ns.arg_strings = args_ns.arg_strings + [arg_name + "=" + format_arg_value(arg_value)] -%}
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{%- endfor -%}
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{%- elif func_args is string and (func_args | trim) not in ["", "{}", "null"] -%}
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{{- raise_exception("Tool call arguments must be a mapping, got a JSON-encoded string: parse arguments with json.loads() before applying the chat template") -}}
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{%- endif -%}
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{%- set tool_calls_ns.tool_calls = tool_calls_ns.tool_calls + [func_name + "(" + (args_ns.arg_strings | join(", ")) + ")"] -%}
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{%- endfor -%}
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{{- "<|tool_call_start|>[" + (tool_calls_ns.tool_calls | join(", ")) + "]<|tool_call_end|>" -}}
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{%- endmacro -%}
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{%- set system_prompt = "" -%}
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{%- set ns = namespace(system_prompt="") -%}
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{%- if messages[0]["role"] == "system" -%}
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{%- endif -%}
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{%- for message in messages -%}
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{{- "<|im_start|>" + message["role"] + "\n" -}}
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{%- set content = message.get("content") -%}
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{%- if content is not string -%}
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{%- set content = content | tojson -%}
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{%- endif -%}
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{%- if message["role"] == "tool" -%}
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{%- set content = "<|tool_response_start|>" + content + "<|tool_response_end|>" -%}
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{%- endif -%}
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{%- if message["role"] == "assistant" and message.get("tool_calls") -%}
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{%- if content and content != "null" -%}
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{{- content -}}
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{%- endif -%}
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{{- render_tool_calls(message["tool_calls"]) -}}
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{{- "<|im_end|>\n" -}}
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{%- else -%}
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{{- content + "<|im_end|>\n" -}}
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{%- endif -%}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{- "<|im_start|>assistant\n" -}}
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