# LiquidAI/LFM2.5-1.2B-Instruct — vLLM serving recipe

> Liquid AI's 1.2B instruction-tuned model on the LFM2 hybrid conv+attention backbone, with tool calling and a 32K context window on a single small GPU.

Configuration: hardware `h200` (8× H200) · strategy `single_node_tp` (Tensor Parallel) · variant `default` (BF16) · features `tool_calling`

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/LiquidAI/LFM2.5-1.2B-Instruct
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/LiquidAI/LFM2.5-1.2B-Instruct.json?hardware=h200

The commands below were rendered for exactly this configuration and take precedence over any example in the guide.

## Other configurations

Change the query string (the same parameters as the page URL) and fetch this URL again. Unknown or incompatible values fall back to the recipe default, so check the Configuration line.

- `hardware`: `h100`, `h200`, `b200`, `gb200`, `b300`, `gb300`, `mi300x`, `mi325x`, `mi350x`, `mi355x`
- `variant`: `default`
- `strategy`: `single_node_tp` (multi-node strategies also need `nodes=<n>`)
- `features`: comma-separated list of `tool_calling` (default: `tool_calling`)

## Install

Requires vLLM >= 0.23.0.

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

Docker image: `vllm/vllm-openai:latest`

## Serve

```bash
vllm serve LiquidAI/LFM2.5-1.2B-Instruct \
  --tensor-parallel-size 1 \
  --enable-auto-tool-choice \
  --tool-call-parser lfm2
```

Docker equivalent:

```bash
docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest LiquidAI/LFM2.5-1.2B-Instruct \
  --tensor-parallel-size 1 \
  --enable-auto-tool-choice \
  --tool-call-parser lfm2
```

## Guide

> Background reference. Commands in it are examples and may target another configuration — use the Serve section above.

### Overview

[LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) is a 1.2B
instruction-tuned model from [Liquid AI](https://www.liquid.ai/), built on the **LFM2 hybrid
backbone**: short-range gated convolution blocks interleaved with grouped-query attention.
The hybrid design keeps the KV cache small and decode fast, so the model serves comfortably on
a single small GPU while supporting a 32K context window and Pythonic tool calling — all
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.
- **32K context**: 32,768-token context window.
- **Tool calling**: Pythonic tool calls (`<|tool_call_start|>…<|tool_call_end|>`) surfaced as OpenAI `tool_calls` by vLLM's native `lfm2` parser.
- **Native vLLM support**: Served via the `Lfm2ForCausalLM` architecture.
- **Edge-ready family**: Day-one support across llama.cpp, MLX, ONNX, vLLM, and SGLang.

#### Supported Variants

Dense:
- `LiquidAI/LFM2.5-230M` (230M)
- `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)

Vision-Language:
- `LiquidAI/LFM2.5-VL-450M`, `LiquidAI/LFM2.5-VL-1.6B`

See the [LFM2.5 usage guide](../../LiquidAI/LFM2.5) for the full family.

### Prerequisites

- **Hardware**: 1× GPU with ≥8 GB VRAM (e.g. L4, A10, RTX 4090, H100).
- **vLLM**: ≥ 0.23.0.

#### pip (NVIDIA CUDA)
```bash
uv venv
source .venv/bin/activate
uv pip install -U vllm --torch-backend auto
```

### Deployment Configurations

#### Quick Start (Single GPU, BF16)
```bash
vllm serve LiquidAI/LFM2.5-1.2B-Instruct
```
Cap the context to fit a smaller GPU (the model supports up to 32K):
```bash
vllm serve LiquidAI/LFM2.5-1.2B-Instruct --max-model-len 32768
```

#### Full-Featured Server Launch
Enables tool calling:
```bash
vllm serve LiquidAI/LFM2.5-1.2B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser lfm2 \
  --host 0.0.0.0 --port 8000
```

#### Docker (NVIDIA)
```bash
docker run -itd --name lfm2.5-1.2b \
  --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-Instruct \
    --host 0.0.0.0 --port 8000
```

### Client Usage

#### Text Generation
The model card recommends temperature `0.1`, `top_k 50`, `repetition_penalty 1.05`. `top_k` and
`repetition_penalty` are vLLM extra sampling params — pass them via `extra_body`.
```python
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-Instruct",
    messages=[{"role": "user", "content": "What is C. elegans? Answer in one sentence."}],
    temperature=0.1,
    extra_body={"top_k": 50, "repetition_penalty": 1.05},
)
print(response.choices[0].message.content)
```

#### Tool Calling
Launch with `--enable-auto-tool-choice --tool-call-parser lfm2`, then pass `tools=[…]`. The
`lfm2` parser converts the model's Pythonic call into a standard `tool_calls` array.
```python
response = client.chat.completions.create(
    model="LiquidAI/LFM2.5-1.2B-Instruct",
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=[{
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Get current weather for a location",
            "parameters": {"type": "object", "properties": {"location": {"type": "string"}}, "required": ["location"]},
        },
    }],
    temperature=0.1,
    extra_body={"top_k": 50, "repetition_penalty": 1.05},
)
print(response.choices[0].message.tool_calls)
```

#### 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 (units, formatting) in the system prompt.

### Configuration Tips
- Set `--max-model-len` to match your workload (up to 32K); lowering it frees VRAM for KV cache.
- `--gpu-memory-utilization 0.90–0.95` maximizes KV cache capacity.
- FP8 KV cache (`--kv-cache-dtype fp8`) roughly halves KV memory.
- Sampling presets are per-request client defaults — don't bake them into `vllm serve`.

### References
- [Model card](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)
- [LFM2.5 usage guide](../../LiquidAI/LFM2.5)
- [Liquid AI](https://www.liquid.ai/)
- [vLLM supported models](https://docs.vllm.ai/en/latest/models/supported_models.html)