初始化项目，由ModelHub XC社区提供模型
Model: AtomicChat/lfm25-8b-a1b-GGUF Source: Original Platform
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README.md
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README.md
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---
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license: other
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license_link: LICENSE
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thumbnail: https://huggingface.co/AtomicChat/lfm25-8b-a1b-GGUF/resolve/main/hero.png
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base_model:
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- LiquidAI/LFM2.5-8B-A1B
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base_model_relation: quantized
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quantized_by: AtomicChat
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pipeline_tag: text-generation
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library_name: gguf
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tags:
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- atomic-chat
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- lfm2.5
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- liquidai
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- gguf
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- llama.cpp
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- quantized
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---
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<center>
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<div style="display:flex; justify-content:center; align-items:center; gap:2%; max-width:560px; margin:0 auto;">
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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>
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<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>
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<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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</div>
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<br/>
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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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<div style="display:flex; justify-content:center; gap:0.5em;">
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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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</div>
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</center>
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**LFM2.5 8B A1B**, self-quantized to GGUF by [Atomic Chat](https://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.
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## Highlights
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- **8.5B parameters**: the weights this repo quantizes.
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- **Context length**: 128,000 tokens (125K), as published by Liquid AI.
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- **24 layers**: Mixture-of-Experts.
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- **Full imatrix ladder**: every quant is calibrated with an importance matrix.
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- **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices.
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- **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks.
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- **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang.
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> [!NOTE]
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> 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.
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> [!IMPORTANT]
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> Always pass `--jinja` so the **LFM2.5 8B A1B chat template** is applied. Without it the model can emit malformed turns.
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## Model Overview
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| Property | Value |
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|---|---|
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| Base model | `LiquidAI/LFM2.5-8B-A1B` |
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| Parameters | 8.5B |
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| Layers | 24 |
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| Experts | 32 routed (top-4) |
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| Context length | 128,000 tokens (125K) |
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| Vocabulary | 128,000 |
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| Modalities | Text |
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| Architecture | Mixture-of-Experts, 32 experts (top-4), 32 attention heads over 8 KV heads, `Lfm2MoeForCausalLM` |
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| 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` |
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<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;"/>
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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.
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## Choosing a quant
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| Quant | Size | Notes |
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|---|---|---|
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| `Q2_K` | 3.2 GB | Smallest K-quant. Minimal RAM, clear quality drop. |
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| `IQ3_M` | 3.8 GB | Beats Q3 at a similar size thanks to imatrix. Best low-RAM pick. |
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| `Q3_K_M` | 4.1 GB | Low quality but usable. |
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| `Q3_K_L` | 4.4 GB | A step above Q3_K_M. |
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| `IQ4_XS` | 4.6 GB | Excellent quality for size. Recommended low-bit. |
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| `Q4_K_S` | 4.9 GB | Compact 4-bit, fast. |
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| **`Q4_K_M`** | 5.2 GB | **Recommended default. Best balance of size, speed and quality.** |
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| `UD-Q4_K_XL` | 5.2 GB | Dynamic. Embeddings and output kept at Q8_0 for higher quality at a Q4 footprint. |
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| `Q5_K_S` | 5.9 GB | Higher quality, slightly more compact than Q5_K_M. |
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| `Q5_K_M` | 6.0 GB | Higher quality, low loss. |
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| `Q6_K` | 7.0 GB | Near lossless, noticeably lighter than Q8_0. |
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| `Q8_0` | 9.0 GB | Effectively lossless, reference quality. |
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> [!TIP]
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> 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.
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## Get started
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Run LFM2.5 8B A1B locally with:
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- **[Atomic Chat](https://atomic.chat):** the easiest path. Open the app, search `AtomicChat/lfm25-8b-a1b-GGUF`, pick a quant, hit **Use this model**.
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- **llama.cpp:** `llama-server -hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M --jinja -c 8192`
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- **Ollama:** `ollama run hf.co/AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M`
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- **LM Studio / Jan:** search the repo id, download any quant.
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## Best practices
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| Parameter | Value |
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| temperature | 0.2 |
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| top_k | 80 |
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| repetition_penalty | 1.05 |
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Liquid AI's recommended sampling configuration for `LiquidAI/LFM2.5-8B-A1B`.
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## Run in llama.cpp
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```bash
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git clone https://github.com/ggml-org/llama.cpp
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cmake llama.cpp -B llama.cpp/build -DBUILD_SHARED_LIBS=OFF -DGGML_CUDA=ON
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cmake --build llama.cpp/build --config Release -j --target llama-cli llama-server
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```
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```bash
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./llama.cpp/build/bin/llama-server \
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-hf AtomicChat/lfm25-8b-a1b-GGUF:Q4_K_M \
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--jinja -ngl 99 -c 8192 -fa on
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```
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## How these were made
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1. Download `LiquidAI/LFM2.5-8B-A1B` (original weights).
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2. Convert to f16 GGUF with [llama.cpp](https://github.com/ggml-org/llama.cpp).
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3. Build an importance matrix over our calibration corpus.
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4. Quantize the ladder with `--imatrix`.
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5. `UD-Q4_K_XL` additionally pins the token-embedding and output tensors to `Q8_0`.
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## License
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Original model by Liquid AI, released under the other license. Full terms: [other](LICENSE). Quantized by Atomic Chat.
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