Instructions to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="AngelSlim/Hy-MT2-1.8B-Q4-GGUF",
	filename="Hy-MT2-1.8B-Q4.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AngelSlim/Hy-MT2-1.8B-Q4-GGUF
# Run inference directly in the terminal:
llama cli -hf AngelSlim/Hy-MT2-1.8B-Q4-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AngelSlim/Hy-MT2-1.8B-Q4-GGUF
# Run inference directly in the terminal:
llama cli -hf AngelSlim/Hy-MT2-1.8B-Q4-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 AngelSlim/Hy-MT2-1.8B-Q4-GGUF
# Run inference directly in the terminal:
./llama-cli -hf AngelSlim/Hy-MT2-1.8B-Q4-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 AngelSlim/Hy-MT2-1.8B-Q4-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AngelSlim/Hy-MT2-1.8B-Q4-GGUF

Use Docker

docker model run hf.co/AngelSlim/Hy-MT2-1.8B-Q4-GGUF

LM Studio
Jan
Ollama
How to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with Ollama:
```
ollama run hf.co/AngelSlim/Hy-MT2-1.8B-Q4-GGUF
```

Unsloth Studio

How to use AngelSlim/Hy-MT2-1.8B-Q4-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 AngelSlim/Hy-MT2-1.8B-Q4-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 AngelSlim/Hy-MT2-1.8B-Q4-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for AngelSlim/Hy-MT2-1.8B-Q4-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with Docker Model Runner:
```
docker model run hf.co/AngelSlim/Hy-MT2-1.8B-Q4-GGUF
```

Lemonade

How to use AngelSlim/Hy-MT2-1.8B-Q4-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AngelSlim/Hy-MT2-1.8B-Q4-GGUF

Run and chat with the model

lemonade run user.Hy-MT2-1.8B-Q4-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

woodchen7 commited on 16 days ago

Commit

ed92a25

verified ·

1 Parent(s): fecbc52

Upload main_result.png with huggingface_hub

Browse files

Files changed (2) hide show

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@@ -35,3 +35,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 HY_MT2_0_Report.pdf filter=lfs diff=lfs merge=lfs -text
 Hy-MT2-1.8B-Q4.gguf filter=lfs diff=lfs merge=lfs -text

 *tfevents* filter=lfs diff=lfs merge=lfs -text
 HY_MT2_0_Report.pdf filter=lfs diff=lfs merge=lfs -text
 Hy-MT2-1.8B-Q4.gguf filter=lfs diff=lfs merge=lfs -text
+main_result.png filter=lfs diff=lfs merge=lfs -text

main_result.png ADDED Viewed

Git LFS Details

SHA256: b87606817fec4fbdaca939de433e4d2a8e92d65493f44222aaaefb381f5a8c6e
Pointer size: 132 Bytes
Size of remote file: 3.84 MB