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

> Liquid AI's 1.2B pretrained base model on the LFM2 hybrid conv+attention backbone — a text-completion and fine-tuning foundation (no chat template).

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-Base
- This configuration as JSON: https://vllm-recipes-cimvhk5sf-inferact-inc.vercel.app/LiquidAI/LFM2.5-1.2B-Base.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-Base \
  --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-Base \
  --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-Base](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Base) is the pretrained base
checkpoint behind [Liquid AI's](https://www.liquid.ai/) LFM2.5-1.2B instruct models, built on
the **LFM2 hybrid backbone** (short-range gated convolution blocks interleaved with grouped-query
attention). It is **not** instruction-tuned and has no chat template — use the
`/v1/completions` endpoint (raw text continuation), or fine-tune it for your own task.

#### Key Features
- **Pretrained base**: No chat template, no instruction tuning — a clean foundation for fine-tuning or raw completion.
- **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-Base
```

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

### Client Usage

#### Text Completion
This is a base model — use the **completions** endpoint, not chat. Recommended sampling:
temperature `0.3`, `min_p 0.15`, `repetition_penalty 1.05`.
```python
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
response = client.completions.create(
    model="LiquidAI/LFM2.5-1.2B-Base",
    prompt="The three laws of thermodynamics are:",
    temperature=0.3,
    extra_body={"min_p": 0.15, "repetition_penalty": 1.05},
)
print(response.choices[0].text)
```

### Configuration Tips
- Use `/v1/completions` (raw continuation) — there is no chat template on a base model.
- For fine-tuning, this checkpoint is the recommended starting point for custom LFM2.5-1.2B tasks.
- Set `--max-model-len` to match your workload (up to 32K).
- 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-Base)
- [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)