Instructions to use litigerking/Hy-MT2-30B-A3B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use litigerking/Hy-MT2-30B-A3B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="litigerking/Hy-MT2-30B-A3B-GGUF",
	filename="Hy-MT2-30B-A3B-BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use litigerking/Hy-MT2-30B-A3B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use litigerking/Hy-MT2-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "litigerking/Hy-MT2-30B-A3B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "litigerking/Hy-MT2-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Ollama
How to use litigerking/Hy-MT2-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
```

Unsloth Studio

How to use litigerking/Hy-MT2-30B-A3B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for litigerking/Hy-MT2-30B-A3B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for litigerking/Hy-MT2-30B-A3B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for litigerking/Hy-MT2-30B-A3B-GGUF to start chatting

How to use litigerking/Hy-MT2-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use litigerking/Hy-MT2-30B-A3B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use litigerking/Hy-MT2-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use litigerking/Hy-MT2-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull litigerking/Hy-MT2-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Hy-MT2-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hy-MT2-30B-A3B GGUF Models

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.

Files

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.

Quantization notes

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'.

Important compatibility note

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.

Recommended llama.cpp server usage

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.
Adjust --n-gpu-layers according to your GPU memory.
For long context, make sure you have enough VRAM/RAM. KV-cache quantization may be useful on smaller GPUs if supported by your runtime.
BF16/FP16-class GGUF requires much more RAM/VRAM than the quantized files.

Example OpenAI-compatible API request

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.

Source model and license

Upstream model: https://huggingface.co/tencent/Hy-MT2-30B-A3B
These GGUF files are converted and quantized from the upstream model.
Please follow the upstream model license and usage terms. A copy of the upstream LICENSE.txt is included when available.

Downloads last month: 4,121

GGUF

Model size

30B params

Architecture

hy_v3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for litigerking/Hy-MT2-30B-A3B-GGUF

Base model

tencent/Hy-MT2-30B-A3B

Quantized

(13)

this model