Instructions to use tencent/Hy-MT2-1.8B-2Bit-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tencent/Hy-MT2-1.8B-2Bit-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tencent/Hy-MT2-1.8B-2Bit-GGUF",
	filename="Hy-MT2-1.8B-2Bit.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tencent/Hy-MT2-1.8B-2Bit-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tencent/Hy-MT2-1.8B-2Bit-GGUF
# Run inference directly in the terminal:
llama-cli -hf tencent/Hy-MT2-1.8B-2Bit-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tencent/Hy-MT2-1.8B-2Bit-GGUF
# Run inference directly in the terminal:
llama-cli -hf tencent/Hy-MT2-1.8B-2Bit-GGUF

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 tencent/Hy-MT2-1.8B-2Bit-GGUF
# Run inference directly in the terminal:
./llama-cli -hf tencent/Hy-MT2-1.8B-2Bit-GGUF

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 tencent/Hy-MT2-1.8B-2Bit-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tencent/Hy-MT2-1.8B-2Bit-GGUF

Use Docker

docker model run hf.co/tencent/Hy-MT2-1.8B-2Bit-GGUF

LM Studio
Jan
Ollama
How to use tencent/Hy-MT2-1.8B-2Bit-GGUF with Ollama:
```
ollama run hf.co/tencent/Hy-MT2-1.8B-2Bit-GGUF
```

Unsloth Studio new

How to use tencent/Hy-MT2-1.8B-2Bit-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 tencent/Hy-MT2-1.8B-2Bit-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 tencent/Hy-MT2-1.8B-2Bit-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tencent/Hy-MT2-1.8B-2Bit-GGUF to start chatting

Docker Model Runner
How to use tencent/Hy-MT2-1.8B-2Bit-GGUF with Docker Model Runner:
```
docker model run hf.co/tencent/Hy-MT2-1.8B-2Bit-GGUF
```

Lemonade

How to use tencent/Hy-MT2-1.8B-2Bit-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tencent/Hy-MT2-1.8B-2Bit-GGUF

Run and chat with the model

lemonade run user.Hy-MT2-1.8B-2Bit-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hy-MT2-1.8B-2Bit-GGUF

File size: 676 Bytes

da4676d

{
    "fp16": {
        "enabled": false
    },
    "bf16": {
        "enabled": true
    },
    "zero_optimization": {
        "stage": 2,
        "offload_optimizer": {
            "device": "cpu",
            "pin_memory": true
        },
        "allgather_partitions": true,
        "allgather_bucket_size": 1e8,
        "overlap_comm": true,
        "reduce_scatter": true,
        "reduce_bucket_size": 1e8,
        "contiguous_gradients": true
    },
    "gradient_accumulation_steps": "auto",
    "gradient_clipping": "auto",
    "steps_per_print": 10,
    "train_batch_size": "auto",
    "train_micro_batch_size_per_gpu": "auto",
    "wall_clock_breakdown": false
}