---
license: other
license_name: lfm1.0
license_link: LICENSE
base_model: LiquidAI/LFM2.5-8B-A1B
pipeline_tag: text-generation
tags:
  - hipfire
  - amd
  - rdna
  - quantized
  - lfm2.5
  - liquid
  - moe
  - edge
library_name: hipfire
---

# LFM2.5-8B-A1B for hipfire

Pre-quantized **LFM2.5-8B-A1B** for [hipfire](https://github.com/Kaden-Schutt/hipfire),
a Rust-native LLM inference engine for AMD RDNA GPUs (HIP/ROCm-direct, no Python
in the hot path).

Quantized from [LiquidAI/LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) —
Liquid AI's reasoning-tuned MoE hybrid: **8.3B total / 1.5B active** parameters,
24 layers (18 double-gated LIV convolution blocks + 6 GQA blocks), 128,000-token
context, vocab 128,000.

These `.mq*` files are hipfire's HFQ container format — **not** GGUF or
safetensors; they won't load in llama.cpp / transformers.

## Files

| File | Quant | Size | sha256 |
|------|-------|------|--------|
| `lfm2.5-8b-a1b.mq4` | MQ4 (FWHT-rotated 4-bit) | 4.90 GB | `c1896590…24fe6b7` |

The upstream LiquidAI `chat_template.jinja` is embedded in the file's HFQ
metadata; hipfire's jinja chat path picks it up by default. The model is
reasoning-tuned and emits `<think>` blocks before answering.

## Usage

```bash
# Install hipfire
curl -L https://raw.githubusercontent.com/Kaden-Schutt/hipfire/master/scripts/install.sh | bash

# Pull and run via the registry
hipfire pull lfm2.5:8b-a1b
hipfire run lfm2.5:8b-a1b "What is the capital of France?"

# Or download the file directly and serve it
hf download hipfire-models/hipfire-LFM2.5-8B-A1B lfm2.5-8b-a1b.mq4 --local-dir ~/.hipfire/models
hipfire serve --model lfm2.5-8b-a1b.mq4
```

Recommended sampling (from the upstream card): `temperature 0.2`, `top_k 80`,
`repetition_penalty 1.05`.

## Quantization & validation

**MQ4 (MagnumQuant 4-bit)** — FWHT-rotated 4-bit: weights are pre-rotated
through a Walsh–Hadamard transform at quantization time and the input vector is
rotated through the same transform on the fly inside the GEMV kernels. The
rotation flattens outliers, giving roughly Q8-grade output at 4-bit storage.

This exact file was validated on gfx1100 (RX 7900 XTX): daemon load + greedy
generation, full coherence-detector battery clean (no attractors, no
special-token leaks, no loops), ≈300 tok/s decode on an 80-token greedy probe.

## License & attribution

This is a quantized **Derivative Work** of
[LiquidAI/LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B),
original work copyright **Liquid AI, Inc.**, redistributed under the
[LFM Open License v1.0](LICENSE) (the LICENSE file in this repo is copied
verbatim from the upstream repository).

**Modification notice (License §4(b)):** the upstream safetensors weights were
re-quantized into hipfire's MQ4/HFQ container format, and the upstream
`chat_template.jinja` was embedded into the HFQ metadata (the export pipeline
did not carry it). No other changes.

Note the license's Commercial Use limitation (§5): commercial use is licensed
only for entities below US$10M annual revenue — read the [LICENSE](LICENSE).