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

> Liquid AI's 1.2B Japanese-specialized chat model on the LFM2 hybrid conv+attention backbone, with a 32K context window on a single small GPU.

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

- Interactive page: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/LiquidAI/LFM2.5-1.2B-JP
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/LiquidAI/LFM2.5-1.2B-JP.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>`)

## 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-JP \
  --tensor-parallel-size 1
```

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-JP \
  --tensor-parallel-size 1
```

## Guide

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

### Overview

[LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) is the Japanese-specialized
1.2B chat model from [Liquid AI](https://www.liquid.ai/), built on the **LFM2 hybrid backbone**
(short-range gated convolution blocks interleaved with grouped-query attention). It serves on a
single small GPU through vLLM's OpenAI-compatible API. For the latest Japanese checkpoint with
tool calling, see
[LFM2.5-1.2B-JP-202606](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606).

#### Key Features
- **Japanese-specialized**: Tuned for Japanese chat and instruction following.
- **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.
- **Native vLLM support**: Served via the `Lfm2ForCausalLM` architecture.

#### 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.
- **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-JP
```

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

### Client Usage

#### Text Generation
The card recommends temperature `0.3`, `min_p 0.15`, `repetition_penalty 1.05` for the JP
checkpoint (`min_p` 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/LFM2.5-1.2B-JP",
    messages=[{"role": "user", "content": "日本の四季について簡単に説明してください。"}],
    temperature=0.3,
    extra_body={"min_p": 0.15, "repetition_penalty": 1.05},
)
print(response.choices[0].message.content)
```

### 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.
- 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-JP)
- [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)