Instructions to use bartowski/merlinite-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use bartowski/merlinite-7b-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="bartowski/merlinite-7b-GGUF", filename="merlinite-7b-Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use bartowski/merlinite-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 bartowski/merlinite-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/merlinite-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 bartowski/merlinite-7b-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf bartowski/merlinite-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 bartowski/merlinite-7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf bartowski/merlinite-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 bartowski/merlinite-7b-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf bartowski/merlinite-7b-GGUF:Q4_K_M
Use Docker
docker model run hf.co/bartowski/merlinite-7b-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use bartowski/merlinite-7b-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bartowski/merlinite-7b-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bartowski/merlinite-7b-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/bartowski/merlinite-7b-GGUF:Q4_K_M
- Ollama
How to use bartowski/merlinite-7b-GGUF with Ollama:
ollama run hf.co/bartowski/merlinite-7b-GGUF:Q4_K_M
- Unsloth Studio new
How to use bartowski/merlinite-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 bartowski/merlinite-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 bartowski/merlinite-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 bartowski/merlinite-7b-GGUF to start chatting
- Docker Model Runner
How to use bartowski/merlinite-7b-GGUF with Docker Model Runner:
docker model run hf.co/bartowski/merlinite-7b-GGUF:Q4_K_M
- Lemonade
How to use bartowski/merlinite-7b-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull bartowski/merlinite-7b-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.merlinite-7b-GGUF-Q4_K_M
List all available models
lemonade list
Llamacpp Quantizations of merlinite-7b
Using llama.cpp commit fa97464 for quantization.
Original model: https://huggingface.co/ibm/merlinite-7b
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
|---|---|---|---|
| merlinite-7b-Q8_0.gguf | Q8_0 | 7.69GB | Extremely high quality, generally unneeded but max available quant. |
| merlinite-7b-Q6_K.gguf | Q6_K | 5.94GB | Very high quality, near perfect, recommended. |
| merlinite-7b-Q5_K_M.gguf | Q5_K_M | 5.13GB | High quality, very usable. |
| merlinite-7b-Q5_K_S.gguf | Q5_K_S | 4.99GB | High quality, very usable. |
| merlinite-7b-Q5_0.gguf | Q5_0 | 4.99GB | High quality, older format, generally not recommended. |
| merlinite-7b-Q4_K_M.gguf | Q4_K_M | 4.36GB | Good quality, similar to 4.25 bpw. |
| merlinite-7b-Q4_K_S.gguf | Q4_K_S | 4.14GB | Slightly lower quality with small space savings. |
| merlinite-7b-Q4_0.gguf | Q4_0 | 4.10GB | Decent quality, older format, generally not recommended. |
| merlinite-7b-Q3_K_L.gguf | Q3_K_L | 3.82GB | Lower quality but usable, good for low RAM availability. |
| merlinite-7b-Q3_K_M.gguf | Q3_K_M | 3.51GB | Even lower quality. |
| merlinite-7b-Q3_K_S.gguf | Q3_K_S | 3.16GB | Low quality, not recommended. |
| merlinite-7b-Q2_K.gguf | Q2_K | 2.71GB | Extremely low quality, not recommended. |
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
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Hardware compatibility
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Model tree for bartowski/merlinite-7b-GGUF
Base model
mistralai/Mistral-7B-v0.1