AlexAtomic/lfm25-8b-a1b-GGUF

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. Runs fully offline.

Highlights

Sparse MoE: 8.3B total parameters, only 1.5B active per token.
LFM2 hybrid architecture: 24 layers (18 double-gated LIV convolution blocks + 6 GQA attention), built on LFM2 with extended pre-training and reinforcement learning.
On-device assistant: designed to chain tool calls and follow complex instructions, with day-one support for llama.cpp, MLX, vLLM and SGLang.
Reasoning model: assistant turns include an explicit chain of thought before the final answer.
128K context, 128,000 vocabulary, trained on a 38 trillion token budget.
Multilingual: English, Arabic, Chinese, French, German, Italian, Japanese, Korean, Portuguese, Spanish.

[!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`
Total / active parameters	8.3B total, 1.5B active (MoE)
Layers	24 (18 LIV conv + 6 GQA)
Context length	128,000
Architecture	LFM2.5 hybrid (built on LFM2, extended pre-training + RL)
This repo	GGUF quants (imatrix)

Scores are Liquid AI's published results for the base LiquidAI/LFM2.5-8B-A1B. Quantization preserves the large majority of this; Q4_K_M and up sit within a point or two of full precision.

Choosing a quant

Quant	Size	Notes
`Q2_K`	3.2 GB	Smallest. Minimal RAM, clear quality drop.
`IQ3_M`	3.8 GB	Beats Q3 at 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 Q4, 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.
`Q5_K_M`	6.0 GB	Higher quality, low loss.
`Q6_K`	7.0 GB	Near lossless.
`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 AlexAtomic/lfm25-8b-a1b-GGUF, pick a quant, hit Use this model.
llama.cpp: llama-server -hf AlexAtomic/lfm25-8b-a1b-GGUF:Q4_K_M --jinja -c 8192
Ollama: ollama run hf.co/AlexAtomic/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 generation parameters.

Run in llama.cpp

git clone https://github.com/ggerganov/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 AlexAtomic/lfm25-8b-a1b-GGUF:UD-Q4_K_XL \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

Download LiquidAI/LFM2.5-8B-A1B (original weights).
Convert to f16 GGUF with llama.cpp.
Build an importance matrix over calibration_datav3 (100 chunks).
Quantize the full ladder with --imatrix.
UD-Q4_K_XL additionally pins the token-embedding and output tensors to Q8_0.

License

Released by Liquid AI under their LFM1.0 license. Quantized by Atomic Chat.