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AtomicChat/
LFM2.5-8B-A1B-GGUF

Name: LFM2.5-8B-A1B-GGUF
Availability: InStock

LiquidAI/LFM2.5-8B-A1B

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Base model: LiquidAI/LFM2.5-8B-A1B

LFM2.5 8B A1B, self-quantized to GGUF by Atomic Chat. Built straight from Liquid AI's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

8.5B parameters: the weights this repo quantizes.
Context length: 128,000 tokens (125K), as published by Liquid AI.
24 layers: Mixture-of-Experts.
Full imatrix ladder: every quant is calibrated with an importance matrix.
On-device personal assistant: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
Compressed performance: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
Unmatched throughput: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.

[!NOTE] These GGUFs are self-quantized from the original weights, not a repack. The importance matrix keeps low-bit quants closer to the full-precision model.

[!IMPORTANT] Always pass --jinja so the LFM2.5 8B A1B chat template is applied. Without it the model can emit malformed turns.

Model Overview

Property	Value
Base model	`LiquidAI/LFM2.5-8B-A1B`
Parameters	8.5B
Layers	24
Experts	32 routed (top-4)
Context length	128,000 tokens (125K)
Vocabulary	128,000
Modalities	Text
Architecture	Mixture-of-Experts, 32 experts (top-4), 32 attention heads over 8 KV heads, `Lfm2MoeForCausalLM`
This repo	GGUF quants (imatrix). Quants: `Q2_K`, `IQ3_M`, `Q3_K_M`, `Q3_K_L`, `IQ4_XS`, `Q4_K_S`, `Q4_K_M`, `UD-Q4_K_XL`, `Q5_K_S`, `Q5_K_M`, `Q6_K`, `Q8_0`

Scores are Liquid AI's published results for the base LiquidAI/LFM2.5-8B-A1B, not our own measurements. Quantization preserves the large majority of this; Q4_K_M and up stay close to full precision.

Choosing a quant

Quant	Size	Notes
`Q2_K`	3.2 GB	Smallest K-quant. Minimal RAM, clear quality drop.
`IQ3_M`	3.8 GB	Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick.
`Q3_K_M`	4.1 GB	Low quality but usable.
`Q3_K_L`	4.4 GB	A step above Q3_K_M.
`IQ4_XS`	4.6 GB	Excellent quality for size. Recommended low-bit.
`Q4_K_S`	4.9 GB	Compact 4-bit, fast.
`Q4_K_M`	5.2 GB	Recommended default. Best balance of size, speed and quality.
`UD-Q4_K_XL`	5.2 GB	Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint.
`Q5_K_S`	5.9 GB	Higher quality, slightly more compact than Q5_K_M.
`Q5_K_M`	6.0 GB	Higher quality, low loss.
`Q6_K`	7.0 GB	Near lossless, noticeably lighter than Q8_0.
`Q8_0`	9.0 GB	Effectively lossless, reference quality.

[!TIP] Pick the largest file that fits your (V)RAM with room for context. Q4_K_M or UD-Q4_K_XL is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run LFM2.5 8B A1B locally with:

Atomic Chat: the easiest path. Open the app, search AtomicChat/lfm25-8b-a1b-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M
LM Studio / Jan: search the repo id, download any quant.

Best practices

Parameter	Value
temperature	0.2
top_k	80
repetition_penalty	1.05

Liquid AI's recommended sampling configuration for LiquidAI/LFM2.5-8B-A1B.

Run in llama.cpp

bash

git clone https://github.com/ggml-org/llama.cpp
cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server