AtomicChat/lfm25-8b-a1b-GGUF overview

---

license: other

license_link: LICENSE

thumbnail: https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/hero.png

base_model:

• LiquidAI/LFM2.5-8B-A1B

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• lfm2.5
• liquidai
• gguf
• llama.cpp
• quantized

---

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/pill_atomic_v3.png" alt="Atomic Chat" style="width:100%; height:auto; max-width:186px;"></a>

<a href="https://discord.gg/8wGSsvmg4V" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/pill_discord_v3.png" alt="Join Discord" style="width:100%; height:auto; max-width:184px;"></a>

<a href="https://github.com/AtomicBot-ai/Atomic-Chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/pill_github_v3.png" alt="GitHub" style="width:100%; height:auto; max-width:141px;"></a>

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<img src="https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/hero.png" alt="LFM2.5 8B A1B" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/LiquidAI/LFM2.5-8B-A1B"><strong>Base model: LiquidAI/LFM2.5-8B-A1B</strong></a>

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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 |

<img src="https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/benchmark.png" alt="LFM2.5 8B A1B benchmark scores" style="width:100%; max-width:900px;"/>

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.