Instructions to use Semantically-AI/zephyr-7b-beta-pruned50-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Semantically-AI/zephyr-7b-beta-pruned50-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Semantically-AI/zephyr-7b-beta-pruned50-GGUF", filename="zephyr-7b-beta-pruned50-Q8_0.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 Semantically-AI/zephyr-7b-beta-pruned50-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
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 Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
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 Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
Use Docker
docker model run hf.co/Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use Semantically-AI/zephyr-7b-beta-pruned50-GGUF with Ollama:
ollama run hf.co/Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
- Unsloth Studio
How to use Semantically-AI/zephyr-7b-beta-pruned50-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 Semantically-AI/zephyr-7b-beta-pruned50-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 Semantically-AI/zephyr-7b-beta-pruned50-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Semantically-AI/zephyr-7b-beta-pruned50-GGUF to start chatting
- Docker Model Runner
How to use Semantically-AI/zephyr-7b-beta-pruned50-GGUF with Docker Model Runner:
docker model run hf.co/Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
- Lemonade
How to use Semantically-AI/zephyr-7b-beta-pruned50-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Semantically-AI/zephyr-7b-beta-pruned50-GGUF:Q8_0
Run and chat with the model
lemonade run user.zephyr-7b-beta-pruned50-GGUF-Q8_0
List all available models
lemonade list
Zephyr 7B Beta - GGUF
- Model creator: Hugging Face H4
- Original model: Zephyr 7B Beta
Description
This repo contains GGUF format model files for Hugging Face H4's Zephyr 7B Beta.
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Prompt template: Zephyr
<|system|>
</s>
<|user|>
{prompt}</s>
<|assistant|>
Explanation of quantisation methods
Click to see details
The GGUF model is pruned to 50% using sparseGPT method sparseGPT
from llama_cpp import Llama
llm = Llama(model_path="zephyr-7b-beta-pruned50-Q8_0.gguf")
output = llm("""
<|system|>
You are a friendly chatbot who always responds in the style of a pirate.</s>
<|user|>
How many helicopters can a human eat in one sitting?</s>
<|assistant|>""")
print(output)
#
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Model tree for Semantically-AI/zephyr-7b-beta-pruned50-GGUF
Base model
mistralai/Mistral-7B-v0.1