---
title: LFM2-8B-A1B-8bit-MLX
canonical_url: "https://www.modelscope.cn/models/mlx-community/LFM2-8B-A1B-8bit-MLX"
md_url: "https://www.modelscope.cn/models/mlx-community/LFM2-8B-A1B-8bit-MLX.md"
repository: mlx-community/LFM2-8B-A1B-8bit-MLX
last_updated: 2025-10-09
license: other
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - lfm2_moe
architectures:
  - Lfm2MoeForCausalLM
base_model:
  - LiquidAI/LFM2-8B-A1B
base_model_relation: quantized
parameters: 2.3B
tensor_type:
  - F32
  - U32
library_name:
  - mlx
  - safetensors
  - pytorch
frameworks:
  - pytorch
language:
  - en
downloads: 85
stars: 0
tags:
  - mlx
  - apple-silicon
  - liquidai
  - lfm2
  - moe
  - transformer
  - long-context
  - instruct
  - quantized
  - 8bit
  - "Mixture of Experts"
  - coding
---

# LFM2-8B-A1B-8bit-MLX

> LFM2-8B-A1B-8bit-MLX - mlx-community 在 ModelScope 开源的模型。LFM2-8B-A1B — MLX 8-bit (Apple Silicon)

mlx-community/LFM2-8B-A1B-8bit-MLX 是 ModelScope 魔搭社区上的 2.3B 参数text-generation模型，采用 other 许可，基于 LiquidAI/LFM2-8B-A1B 构建。

- **Repository**: mlx-community/LFM2-8B-A1B-8bit-MLX
- **License**: other
- **Tasks**: text-generation
- **Parameters**: 2.3B
- **Base model**: LiquidAI/LFM2-8B-A1B
- **Tags**: mlx, apple-silicon, liquidai, lfm2, moe, transformer, long-context, instruct, quantized, 8bit, Mixture of Experts, coding
- **Downloads**: 85
- **Stars**: 0
- **Last updated**: 2025-10-09

Source: https://www.modelscope.cn/models/mlx-community/LFM2-8B-A1B-8bit-MLX

---

# LFM2-8B-A1B — **MLX 8-bit** (Apple Silicon)

**Maintainer / Publisher:** [**Susant Achary**](https://huggingface.co/Susant-Achary)  
**Upstream model:** [LiquidAI/LFM2-8B-A1B](https://huggingface.co/LiquidAI/LFM2-8B-A1B)  
**This repo (MLX 8-bit):** `mlx-community/LFM2-8B-A1B-8bit-MLX`

This repository provides an **Apple-Silicon-optimized MLX build** of **LFM2-8B-A1B** at **8-bit** quantization for fast, on-device inference.

---

## 🔎 What is LFM2-8B-A1B?

- **Architecture:** Mixture-of-Experts (**MoE**) Transformer.  
- **Size:** ~**8B total parameters** with **~1B active** per token (the “A1B” suffix commonly denotes *~1B active params*).  
- **Why MoE?** During generation, only a subset of experts is **activated per token**, reducing **compute per token** while keeping a larger total parameter pool for expressivity.

> **Important memory note (single-device inference):**  
> Although *compute per token* benefits from MoE (fewer **active** parameters), **the full set of experts still resides in memory** for typical single-GPU/CPU deployments. In practice this means **RAM usage scales with total parameters**, not with the smaller *active* count.

---

## 📦 What’s in this MLX build

- `config.json` (MLX), `mlx_model*.safetensors` (**8-bit** shards)
- Tokenizer files: `tokenizer.json`, `tokenizer_config.json`
- Model metadata (e.g., `model_index.json`)

Target platform: **macOS** on **Apple Silicon (M-series)** using **Metal/MPS**.

---

## ✅ Intended use

- General **instruction-following**, chat, and summarization
- **RAG** back-ends and long-context workflows on device
- **Function-calling / structured outputs** with schema-style prompts

## ⚠️ Limitations

- Even at 8-bit, **long contexts** (KV-cache) can dominate memory at high `max_tokens` or large batch sizes.  
- As with any quantization, small regressions vs FP16 can appear on intricate math/code or edge-formatting.

---

## 🔢 RAM planning (8-bit, MoE, MLX)

You asked to **assume and decide** RAM usage in absence of your measurements. Below are **practical planning numbers** derived from first-principles + experience with MLX and similar MoE models. Treat them as **starting points** and validate on your hardware.

### Rule-of-thumb components

- **Weights:** `~ total_params × 1 byte` (8-bit). For 8B params → **~8.0 GB** baseline.  
- **Runtime overhead:** MLX graph + tensors + metadata → **~0.5–1.0 GB** typical.  
- **KV cache:** grows with **context_length × layers × heads × dtype**; often **1–3+ GB** for long contexts.

### Indicative peak RAM (single image/text, batch=1)

| Context window | Estimated peak RAM |
|---|---:|
| **4k tokens** | **~9.5–10.5 GB** |
| **8k tokens** | **~10.5–11.8 GB** |
| **16k tokens** | **~12.0–14.0 GB** |

> These ranges assume **8-bit** weights, **A1B MoE** (all experts resident), batch size = 1, and standard generation settings.  
> On lower windows (≤2k), you may see **~9–10 GB**. Larger windows or batches will increase KV-cache and peak RAM.

---

## 🧭 Choosing precision for LFM2-8B-A1B 

While this card is **8-bit**, teams often want a consistent lineup. If you later produce 6/5/4/3/2-bit MLX builds, here’s a practical guide (RAM figures are **indicative** for an 8B MoE LM; your results depend on context/batch):

| Variant | Typical Peak RAM | Relative Speed | Typical Behavior | When to choose |
|---|---:|:---:|---|---|
| **4-bit** | ~7–8 GB | 🔥🔥🔥 | Better detail retention | If 3-bit drops too much fidelity |
| **6-bit** | ~9–10.5 GB | 🔥🔥 | Near-max MLX quality | If you want accuracy under quant |
| **8-bit** *(this repo)* | **~9.5–12+ GB** | 🔥🔥 | **Highest** quality among quant tiers | When RAM allows and you want the most faithful outputs |

> **MoE caveat:** MoE **reduces compute per token**, but unless experts are **paged/partitioned** across devices and loaded on demand, **memory** still follows **total parameters**. On a single Mac, plan RAM as if the *whole 8B* parameter set is resident.

---

## 🚀 Quickstart (CLI — MLX)

**Deterministic generation**
```bash
python -m mlx_lm.generate \
  --model mlx-community/LFM2-8B-A1B-8bit-MLX \
  --prompt "Summarize the following in 5 bullet points:\n<your text>" \
  --max-tokens 256 \
  --temperature 0.0 \
  --device mps \
  --seed 0