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litigerking/Hy-MT2-30B-A3B-GGUF
Hy-MT2-30B-A3B-GGUF is a text generation model from litigerking. Use it when you need the model to write or continue text. It is set up for llama.cpp. The card lists the license as other.
Downloads · 30 days
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.gguf182 GB · 100%
From the Hugging Face model README
Chinese documentation: README.zh-CN.md
This repository contains GGUF conversions and quantizations of Tencent's tencent/Hy-MT2-30B-A3B translation model for llama.cpp-compatible inference.
| File | Type / Quantization | Size | Notes |
|---|---|---|---|
Hy-MT2-30B-A3B-BF16.gguf | BF16 / FP16-class source GGUF | ~56 GB | Highest fidelity source GGUF; useful for re-quantization or maximum-quality inference |
Hy-MT2-30B-A3B-Q2_K.gguf | Q2_K | ~11 GB | Smallest/lowest-memory option; lowest quality |
Hy-MT2-30B-A3B-Q3_K_M.gguf | Q3_K_M | ~14 GB | Very low-memory option, better than Q2_K |
Hy-MT2-30B-A3B-Q4_K_M.gguf | Q4_K_M | ~17 GB | Recommended balanced/low-memory option |
Hy-MT2-30B-A3B-Q5_K_M.gguf | Q5_K_M | ~20 GB | Better quality than Q4_K_M with moderate extra memory |
Hy-MT2-30B-A3B-Q6_K.gguf | Q6_K | ~24 GB | Better quality, higher VRAM/RAM usage |
Hy-MT2-30B-A3B-Q8_0.gguf | Q8_0 | ~30 GB | Highest fidelity among the quantized files |
SHA256 sidecar files are provided for the GGUF files when available.
The Q2_K, Q3_K_M, and Q5_K_M files were quantized directly from the BF16 GGUF source, not requantized from another low-bit GGUF.
Because Hy-MT2-30B-A3B uses the hy_v3 architecture, conversion and quantization require llama.cpp tooling that supports hy_v3. A generic llama.cpp quantizer may fail with unknown model architecture: 'hy_v3'.
Hy-MT2-30B-A3B uses the hy_v3 architecture. It requires a llama.cpp build that supports this architecture. If your llama.cpp build does not support it, loading the GGUF may fail with:
unknown model architecture: 'hy_v3'
Use a compatible llama.cpp build/branch for Hy-V3/Hy-MT2 models.
Example:
./llama-server \
-m Hy-MT2-30B-A3B-Q4_K_M.gguf \
--alias tencent/Hy-MT2-30B-A3B-GGUF:Q4_K_M \
--host 0.0.0.0 \
--port 18080 \
-c 131072 \
--n-gpu-layers 60 \
--jinja \
-r '<eos:6124c78e>'
Notes:
--jinja is recommended so llama.cpp uses the chat template correctly.-r '<eos:6124c78e>' is recommended as a reverse prompt / stop marker because this model may emit the textual EOS marker if the runtime does not treat it as a native EOS token.--n-gpu-layers according to your GPU memory.curl http://127.0.0.1:18080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"messages": [
{"role": "user", "content": "Translate to English: 今天天气很好。"}
],
"temperature": 0,
"max_tokens": 128
}'
Expected output is a direct translation such as:
The weather is very nice today.
LICENSE.txt is included when available.