---
title: LFM3-1.7B-NPU2
canonical_url: "https://www.modelscope.ai/models/AMD/LFM3-1.7B-NPU2"
md_url: "https://www.modelscope.ai/models/AMD/LFM3-1.7B-NPU2.md"
repository: AMD/LFM3-1.7B-NPU2
last_updated: 2026-10-29
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - lfm3
architectures:
  - Lfm3ForCausalLM
base_model:
  - LiquidAI/LFM3-1.7B
base_model_relation: finetune
library_name:
  - other
frameworks:
  - other
downloads: 15
stars: 0
tags:
  - npu
  - amd
  - ryzen-ai
  - fastflowlm
  - lfm3
  - liquid
---

# LFM3-1.7B-NPU2

> LFM3-1.7B-NPU2 - An open-source model by AMD on ModelScope. LFM3-1.7B-NPU2 (FastFlowLM)

AMD/LFM3-1.7B-NPU2 is a text-generation model on ModelScope. licensed under other. derived from LiquidAI/LFM3-1.7B.

- **Repository**: AMD/LFM3-1.7B-NPU2
- **License**: other
- **Tasks**: text-generation
- **Base model**: LiquidAI/LFM3-1.7B
- **Tags**: npu, amd, ryzen-ai, fastflowlm, lfm3, liquid
- **Downloads**: 15
- **Stars**: 0
- **Last updated**: 2026-10-29

Source: https://www.modelscope.ai/models/AMD/LFM3-1.7B-NPU2

---

# LFM3-1.7B-NPU2 (FastFlowLM)

[LiquidAI/LFM3-1.7B](https://huggingface.co/LiquidAI/LFM3-1.7B) packaged for the **AMD Ryzen™ AI NPU** (XDNA2) with
[FastFlowLM](https://github.com/FastFlowLM/FastFlowLM). The weights are the vendor's quantization-aware-distilled **Q4_0** build
(`LFM3-1.7B-GGUF`, `QAD-Q4_0`); the tied embedding / LM head is stored as Q4_1. The package holds the converted weights
(`model.q4nx`), the tokenizer and the chat template; it runs only with FastFlowLM (version 1.0.11 or later).

```shell
flm run lfm3:1.7b
flm serve lfm3:1.7b        # OpenAI-compatible server
```

## Model

- 24 layers: 13 gated short-convolution (width 7), 5 sliding-window attention (window 256), 6 full attention
- hidden 2048, FFN 7680, 16 query / 4 KV heads, head dim 128, per-head sigmoid attention gate, sandwich RMSNorms, vocab 128000
- ChatML prompt format; **thinking is on by default** and can be switched off; tool calls use the Qwen-style XML format
- up to 32k tokens of context in FastFlowLM (the model supports 256k)
- **FastFlowLM:** 1.0.11 or later
- LiquidAI's recommended sampling is the default: temperature 0.1, top_k 50, repetition penalty 1.1

## Performance (FastFlowLM on an AMD Ryzen™ AI MAX+ PRO 395, NPU only)

| context | 1k | 2k | 4k | 8k | 16k | 32k |
|---|---:|---:|---:|---:|---:|---:|
| decode, tokens/s | 41 | 40 | 39 | 36 | 31 | 24 |
| prefill, tokens/s | 1092 | 1543 | 1467 | 1397 | 1346 | 1223 |

## Accuracy

The NPU computes in bf16, so its logits differ slightly from a PyTorch reference run on the same quantized weights.

| test | PyTorch reference | FastFlowLM on NPU |
|---|---:|---:|
| WikiText-2 perplexity (6.1k tokens) | 19.23 | 20.16 |
| GSM8K, first 150 test questions (greedy, no thinking) | n/a | 81.3% |

Greedy output on the project's golden prompts is identical to the reference. Running the attention layers on the CPU
(`LFM3_DECODE=conv`) raises GSM8K to 84.0% at a lower decoding speed.

## License

This package contains a conversion of LiquidAI's LFM3-1.7B and is distributed under the **LFM Open License v1.0** (see `LICENSE`).
In short: free for research and for commercial use by organizations below the revenue threshold stated in the license; read it
before deploying. FastFlowLM's own runtime is licensed separately by the FastFlowLM project.