Instructions to use FlorianJc/Vigostral-7b-Chat-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use FlorianJc/Vigostral-7b-Chat-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 FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf FlorianJc/Vigostral-7b-Chat-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 FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf FlorianJc/Vigostral-7b-Chat-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 FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
Use Docker
docker model run hf.co/FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use FlorianJc/Vigostral-7b-Chat-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "FlorianJc/Vigostral-7b-Chat-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "FlorianJc/Vigostral-7b-Chat-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
- Ollama
How to use FlorianJc/Vigostral-7b-Chat-GGUF with Ollama:
ollama run hf.co/FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
- Unsloth Studio
How to use FlorianJc/Vigostral-7b-Chat-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 FlorianJc/Vigostral-7b-Chat-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 FlorianJc/Vigostral-7b-Chat-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for FlorianJc/Vigostral-7b-Chat-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use FlorianJc/Vigostral-7b-Chat-GGUF with Docker Model Runner:
docker model run hf.co/FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
- Lemonade
How to use FlorianJc/Vigostral-7b-Chat-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull FlorianJc/Vigostral-7b-Chat-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.Vigostral-7b-Chat-GGUF-Q4_K_M
List all available models
lemonade list
Vigostral-7b-Chat GGUF
Conversion du modèle vigostral-7b-chat au format GGUF
Lien du modèle original: https://huggingface.co/bofenghuang/vigostral-7b-chat/
Le projet llama.cpp (pour l'inférence): https://github.com/ggerganov/llama.cpp/
Les modèles ont dans leur nom un suffixe qui définit la quantification.
La perte de qualité est tirée de la documentation de llama.cpp et a été calculée par la variation de la perplexité (ppl) sur le modèle LLaMA-v1-7B. Elle n'est donc fournie ici que pour donner une approximation de la perte rééle.
| Méthode de quantification | Taille du fichier | Perte | Téléchargement |
|---|---|---|---|
| COPY | 13,5 Go | Aucune | https://huggingface.co/FlorianJc/Vigostral-7b-Chat-GGUF/blob/main/vigostral-7b-chat-COPY.gguf |
| Q8_0 | 7,2 Go | +0.0004 ppl @ LLaMA-v1-7B | https://huggingface.co/FlorianJc/Vigostral-7b-Chat-GGUF/blob/main/vigostral-7b-chat-Q8_0.gguf |
| Q6_K | 5,5 Go | -0.0008 ppl @ LLaMA-v1-7B | https://huggingface.co/FlorianJc/Vigostral-7b-Chat-GGUF/blob/main/vigostral-7b-chat-Q6_K.gguf |
| Q5_K_M | 4,8 Go | +0.0122 ppl @ LLaMA-v1-7B | https://huggingface.co/FlorianJc/Vigostral-7b-Chat-GGUF/blob/main/vigostral-7b-chat-Q5_K_M.gguf |
| Q4_K_M | 4,1 Go | +0.0532 ppl @ LLaMA-v1-7B | https://huggingface.co/FlorianJc/Vigostral-7b-Chat-GGUF/blob/main/vigostral-7b-chat-Q4_K_M.gguf |
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