license, license_link, thumbnail, base_model, base_model_relation, quantized_by, pipeline_tag, library_name, tags

license	license_link	thumbnail	base_model	base_model_relation	quantized_by	pipeline_tag	library_name	tags
other	LICENSE	https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/hero.png	LiquidAI/LFM2.5-8B-A1B	quantized	AtomicChat	text-generation	gguf	atomic-chat lfm2.5 liquidai gguf llama.cpp quantized

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

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

./llama.cpp/build/bin/llama-server \
    -hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download LiquidAI/LFM2.5-8B-A1B (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus.
4. Quantize the ladder with --imatrix.
5. UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Original model by Liquid AI, released under the other license. Full terms: other. Quantized by Atomic Chat.