Instructions to use LucileFavero/AM_model_eff6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LucileFavero/AM_model_eff6 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("LucileFavero/AM_model_eff6", dtype="auto") - llama-cpp-python
How to use LucileFavero/AM_model_eff6 with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="LucileFavero/AM_model_eff6", filename="unsloth.Q4_K_M.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 LucileFavero/AM_model_eff6 with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LucileFavero/AM_model_eff6:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LucileFavero/AM_model_eff6:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf LucileFavero/AM_model_eff6:Q4_K_M # Run inference directly in the terminal: llama-cli -hf LucileFavero/AM_model_eff6: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 LucileFavero/AM_model_eff6:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf LucileFavero/AM_model_eff6: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 LucileFavero/AM_model_eff6:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf LucileFavero/AM_model_eff6:Q4_K_M
Use Docker
docker model run hf.co/LucileFavero/AM_model_eff6:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use LucileFavero/AM_model_eff6 with Ollama:
ollama run hf.co/LucileFavero/AM_model_eff6:Q4_K_M
- Unsloth Studio new
How to use LucileFavero/AM_model_eff6 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 LucileFavero/AM_model_eff6 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 LucileFavero/AM_model_eff6 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for LucileFavero/AM_model_eff6 to start chatting
- Docker Model Runner
How to use LucileFavero/AM_model_eff6 with Docker Model Runner:
docker model run hf.co/LucileFavero/AM_model_eff6:Q4_K_M
- Lemonade
How to use LucileFavero/AM_model_eff6 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull LucileFavero/AM_model_eff6:Q4_K_M
Run and chat with the model
lemonade run user.AM_model_eff6-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- gguf
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---
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# Uploaded model
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- **Developed by:** LucileFavero
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/meta-llama-3.1-8b-bnb-4bit
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This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
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