Instructions to use QuantFactory/Matter-0.1-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use QuantFactory/Matter-0.1-7B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/Matter-0.1-7B-GGUF", filename="Matter-0.1-7B.Q2_K.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 QuantFactory/Matter-0.1-7B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Matter-0.1-7B-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 QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/Matter-0.1-7B-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 QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/Matter-0.1-7B-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 QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/Matter-0.1-7B-GGUF with Ollama:
ollama run hf.co/QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/Matter-0.1-7B-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 QuantFactory/Matter-0.1-7B-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 QuantFactory/Matter-0.1-7B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/Matter-0.1-7B-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/Matter-0.1-7B-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/Matter-0.1-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/Matter-0.1-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Matter-0.1-7B-GGUF-Q4_K_M
List all available models
lemonade list
QuantFactory/Matter-0.1-7B-GGUF
This is quantized version of 0-hero/Matter-0.1-7B created using llama.cpp
Original Model Card
Matter 7B - 0.1 (Mistral 7B Finetune)
Matter 7B is full-finetune on the Matter dataset, which is curated from over 35 datsets analyzing >6B tokens
Training
Prompt format: This model uses ChatML prompt format.
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
Function Calling
Model also supports function calling. Additional tokens for function calling
Model function call tokens
- <|begin_func|> - Function call start token
- <|end_func|> - Function call end token
Function call response tokens
- <|begin_func_response|> - Function response start token
- <|end_func_response|> - Function response end token
Example
<|im_start|>system
You are a helpful assistant with access to the following functions. Use them if required -
{ "name": "get_news_headlines",
"description": "Get the latest news headlines",
"parameters":
{ "type": "object",
"properties":
{ "country":
{ "type": "string",
"description": "The country for which to fetch news"
}
},
"required": [ "country" ]
}
}
<|im_end|>
<|im_start|>user
Can you tell me the latest news headlines for the United States?<|im_end|>
<|im_start|>assistant
<|begin_func|>{"name": "get_news_headlines", "arguments": '{"country": "United States"}'}<|end_func|><|im_end|>
<|im_start|>user
<|begin_func_response|>{
"headlines":
[
"Biden announces new vaccine mandates",
"Hurricane Ida devastates Louisiana",
"Apple unveils new iPhone",
"NASA's Perseverance rover collects first Mars rock sample"
]
}<|end_func_response|>
<|im_end|>
<|im_start|>assistant
Here are the latest news headlines for the United States:
1. Biden announces new vaccine mandates
2. Hurricane Ida devastates Louisiana
3. Apple unveils new iPhone
4. NASA's Perseverance rover collects first Mars rock sample
<|im_end|>
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