AlexAtomic/lfm25-8b-a1b-GGUF overview

---

license: other

license_name: lfm1.0

license_link: https://huggingface.co/LiquidAI/LFM2.5-8B-A1B/raw/main/LICENSE

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

base_model:

• LiquidAI/LFM2.5-8B-A1B

base_model_relation: quantized

quantized_by: AlexAtomic

language:

• en
• ar
• zh
• fr
• de
• ja
• ko
• es
• pt
• it

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• lfm
• liquid
• lfm2
• gguf
• imatrix
• quantized
• llama.cpp

---

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AlexAtomic/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/AlexAtomic/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/AlexAtomic/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/AlexAtomic/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. 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) |

<img src="https://huggingface.co/AlexAtomic/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. 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

1. Download LiquidAI/LFM2.5-8B-A1B (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over calibration_datav3 (100 chunks).
4. Quantize the full ladder with --imatrix.
5. 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.