# LiquidAI/LFM2.5-350M — vLLM serving recipe

> Liquid AI's smallest LFM2.5 chat model (350M) on the LFM2 hybrid conv+attention backbone — tool calling and 32K context, light enough for edge GPUs.

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

- Interactive page: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/LiquidAI/LFM2.5-350M
- This configuration as JSON: https://vllm-recipes-git-preview-vllm-pr-60762-inferact-inc.vercel.app/LiquidAI/LFM2.5-350M.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/LFM2.5-350M \
  --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/LFM2.5-350M \
  --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

[LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) is the smallest chat model in
[Liquid AI's](https://www.liquid.ai/) LFM2.5 family, built on the **LFM2 hybrid backbone**:
short-range gated convolution blocks interleaved with grouped-query attention. It shares that
backbone with its larger siblings, which keeps memory and latency low enough for edge and
on-device deployment while still supporting tool calling and a 32K context window — 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**: ~0.7 GB of BF16 weights — runs on commodity and on-device GPUs.

#### 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 (~1 GB VRAM for weights; any modern GPU works).
- **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/LFM2.5-350M
```

#### Full-Featured Server Launch
Enables tool calling:
```bash
vllm serve LiquidAI/LFM2.5-350M \
  --enable-auto-tool-choice \
  --tool-call-parser lfm2 \
  --host 0.0.0.0 --port 8000
```

#### Docker (NVIDIA)
```bash
docker run -itd --name lfm2.5-350m \
  --ipc=host --network host --shm-size 16G --gpus all \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  vllm/vllm-openai:latest \
    --model LiquidAI/LFM2.5-350M \
    --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` ride in `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/LFM2.5-350M",
    messages=[{"role": "user", "content": "Give me three uses for baking soda."}],
    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/LFM2.5-350M",
    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 in the system prompt.

### Configuration Tips
- At 350M the model fits any GPU; raise `--max-num-seqs` to push batch throughput.
- Set `--max-model-len` to match your workload (up to 32K).
- `--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](https://huggingface.co/LiquidAI/LFM2.5-350M)
- [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)