---
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
---
**Hy3**, self-quantized to GGUF by [Atomic Chat](https://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` |
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](https://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
```bash
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
```
```bash
./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](https://github.com/ggml-org/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](https://huggingface.co/tencent/Hy3/blob/main/LICENSE). Quantized by Atomic Chat.
