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
--jinjaso 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_Mis the sweet spot for most setups;Q6_KorQ8_0for 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
- Download
tencent/Hy3(original weights). - Convert to f16 GGUF with llama.cpp.
- Build an importance matrix over our calibration corpus, published here as
imatrix-atomic.gguf. - 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.