LiquidAI/LFM2.5-1.2B-Thinking

Overview

LFM2.5-1.2B-Thinking is the reasoning variant of Liquid AI's 1.2B LFM2.5 model, built on the LFM2 hybrid backbone (short-range gated convolution blocks interleaved with grouped-query attention). On reasoning-heavy tasks it produces an explicit <think>…</think> chain-of-thought before its final answer, so it reasons well above its weight class while still serving on a single small GPU through vLLM's OpenAI-compatible API.

Key Features

• Hybrid backbone: Gated short convolutions interleaved with grouped-query attention — a smaller KV cache and lower decode latency than a same-size full-attention transformer.
• <think> reasoning: Emits an explicit <think>…</think> chain-of-thought on non-trivial problems; vLLM's qwen3 parser splits it into a separate reasoning_content field.
• 128K context: Long-context support (max_position_embeddings = 128000).
• Tool calling: Pythonic tool calls surfaced as OpenAI tool_calls by vLLM's native lfm2 parser.
• Native vLLM support: Served via the Lfm2ForCausalLM architecture — no --trust-remote-code required.

Supported Variants

Dense:

• LiquidAI/LFM2.5-350M (350M)
• LiquidAI/LFM2.5-1.2B-Instruct (1.2B)
• LiquidAI/LFM2.5-1.2B-Thinking (1.2B, reasoning)
• LiquidAI/LFM2.5-1.2B-JP / LiquidAI/LFM2.5-1.2B-JP-202606 (Japanese)
• LiquidAI/LFM2.5-1.2B-Base (pretrained base)

MoE:

• LiquidAI/LFM2.5-8B-A1B (8B total / ~1B active, also a reasoning model)

Vision-Language:

• LiquidAI/LFM2.5-VL-450M, LiquidAI/LFM2.5-VL-1.6B

See the LFM2.5 usage guide for the full family.

Prerequisites

• Hardware: 1× GPU with ≥8 GB VRAM. Verified on H100.
• vLLM: ≥ 0.23.0 — the LFM2 architecture ships in the 0.23.0 stable release.

pip (NVIDIA CUDA)

uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto

Deployment Configurations

Quick Start (Single GPU, BF16)

Enable the reasoning parser so the chain-of-thought is returned in reasoning_content:

vllm serve LiquidAI/LFM2.5-1.2B-Thinking \
  --reasoning-parser qwen3

Full-Featured Server Launch

Reasoning and tool calling:

vllm serve LiquidAI/LFM2.5-1.2B-Thinking \
  --reasoning-parser qwen3 \
  --enable-auto-tool-choice \
  --tool-call-parser lfm2 \
  --host 0.0.0.0 --port 8000

Docker (NVIDIA)

docker run -itd --name lfm2.5-thinking \
  --ipc=host --network host --shm-size 16G --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
    --model LiquidAI/LFM2.5-1.2B-Thinking \
    --reasoning-parser qwen3 \
    --host 0.0.0.0 --port 8000

Client Usage

Reasoning Mode

The card recommends a low temperature for the reasoning model — temperature 0.05, top_k 50, repetition_penalty 1.05. Do not cap max_tokens too low: it can truncate the chain-of-thought mid-stream.

from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="LiquidAI/LFM2.5-1.2B-Thinking",
    messages=[{"role": "user", "content": "A snail climbs 3 ft up a 20 ft well each day and slides 2 ft back each night. How many days to reach the top?"}],
    temperature=0.05,
    max_tokens=2048,
    extra_body={"top_k": 50, "repetition_penalty": 1.05},
)
msg = response.choices[0].message
print("reasoning:", msg.reasoning_content)
print("answer:", msg.content)

Note: the model opens the <think> channel for non-trivial problems; a simple prompt may be answered directly, in which case reasoning_content is empty. That is expected behavior — the qwen3 parser extracts the block whenever it is present.

Tool Calling

Add --enable-auto-tool-choice --tool-call-parser lfm2 at launch, then pass tools=[…]; the model can reason about which tool to call before emitting it.

Structured Outputs

vLLM's guided decoding constrains output to a JSON schema via response_format. The model does not see the schema itself — put semantic instructions in the system prompt.

Configuration Tips

• Give reasoning room: keep max_tokens high enough for the <think> block plus the answer.
• Set --max-model-len to match your workload (up to 128K).
• --gpu-memory-utilization 0.90–0.95 maximizes KV cache capacity.
• Sampling presets are per-request client defaults — don't bake them into vllm serve.

References

• Model card
• LFM2.5 usage guide
• Liquid AI
• vLLM supported models