AtomicChat/Hy3-GGUF overview

---

license: apache-2.0

license_link: https://huggingface.co/tencent/Hy3/blob/main/LICENSE

thumbnail: https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/hero.png

base_model:

• tencent/Hy3

base_model_relation: quantized

quantized_by: AtomicChat

pipeline_tag: text-generation

library_name: gguf

tags:

• atomic-chat
• hy3
• tencent
• gguf
• llama.cpp
• imatrix
• quantized

---

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<a href="https://atomic.chat" style="flex:0 1 auto; min-width:0;"><img src="https://huggingface.co/AtomicChat/Hy3-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/Hy3-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/Hy3-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/Hy3-GGUF/resolve/main/hero.png" alt="Hy3" style="width:100%; max-width:100%; height:auto; margin-bottom:0.6em;"/>

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<a href="https://huggingface.co/tencent/Hy3"><strong>Base model: tencent/Hy3</strong></a>

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Hy3, self-quantized to GGUF by Atomic Chat. Built straight from Tencent's original weights with a per-tensor importance matrix, so this is not a repack of somebody else's files. Runs fully offline.

Highlights

• 298.8B parameters: the weights this repo quantizes.
• Context length: 262,144 tokens (256K), as published by Tencent.
• 80 layers: Mixture-of-Experts.
• Full imatrix ladder: every quant is calibrated with an importance matrix, published here alongside the quants.

> [!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 Hy3 chat template is applied. Without it the model can emit malformed turns.

Model Overview

| Property | Value |

|---|---|

| Base model | tencent/Hy3 |

| Parameters | 298.8B |

| Layers | 80 |

| Experts | 192 routed (top-8) |

| Context length | 262,144 tokens (256K) |

| Vocabulary | 120,832 |

| Modalities | Text |

| Architecture | Mixture-of-Experts, 192 experts (top-8), 64 attention heads over 8 KV heads, HYV3ForCausalLM |

| This repo | GGUF quants (imatrix); the importance matrix is published here as imatrix-atomic.gguf. Quants: IQ1_M, Q4_K_M |

<img src="https://huggingface.co/AtomicChat/Hy3-GGUF/resolve/main/benchmark.png" alt="Hy3 benchmark scores" style="width:100%; max-width:900px;"/>

Scores are Tencent's published results for the base tencent/Hy3, 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 |

|---|---|---|

| IQ1_M | 91.8 GB | Last resort, only if nothing else fits. |

| Q4_K_M | 184.7 GB | Recommended default. Best balance of size, speed and quality. |

> [!TIP]

> Pick the largest file that fits your (V)RAM with room for context. Q4_K_M is the sweet spot for most setups; Q6_K or Q8_0 for maximum fidelity.

Get started

Run Hy3 locally with:

• Atomic Chat: the easiest path. Open the app, search AtomicChat/Hy3-GGUF, pick a quant, hit Use this model.
• llama.cpp: llama-server -hf AtomicChat/Hy3-GGUF:Q4_K_M --jinja -c 8192
• Ollama: ollama run hf.co/AtomicChat/Hy3-GGUF:Q4_K_M
• LM Studio / Jan: search the repo id, download any quant.

Best practices

| Parameter | Value |

|---|---|

| temperature | 0.9 |

| top_p | 1.0 |

| top_k | -1 |

Tencent's recommended sampling configuration for tencent/Hy3.

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/Hy3-GGUF:Q4_K_M \
    --jinja -ngl 99 -c 8192 -fa on

How these were made

1. Download tencent/Hy3 (original weights).
2. Convert to f16 GGUF with llama.cpp.
3. Build an importance matrix over our calibration corpus, published here as imatrix-atomic.gguf.
4. Quantize the ladder with --imatrix.

License

Original model by Tencent, released under the Apache 2.0 license. Full terms: Apache 2.0. Quantized by Atomic Chat.