Instructions to use fevohh/RayExtract-1B-v0.2 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 fevohh/RayExtract-1B-v0.2 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 fevohh/RayExtract-1B-v0.2:Q4_K_M # Run inference directly in the terminal: llama cli -hf fevohh/RayExtract-1B-v0.2:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf fevohh/RayExtract-1B-v0.2:Q4_K_M # Run inference directly in the terminal: llama cli -hf fevohh/RayExtract-1B-v0.2: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 fevohh/RayExtract-1B-v0.2:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf fevohh/RayExtract-1B-v0.2: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 fevohh/RayExtract-1B-v0.2:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf fevohh/RayExtract-1B-v0.2:Q4_K_M
Use Docker
docker model run hf.co/fevohh/RayExtract-1B-v0.2:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use fevohh/RayExtract-1B-v0.2 with Ollama:
ollama run hf.co/fevohh/RayExtract-1B-v0.2:Q4_K_M
- Unsloth Studio
How to use fevohh/RayExtract-1B-v0.2 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 fevohh/RayExtract-1B-v0.2 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 fevohh/RayExtract-1B-v0.2 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for fevohh/RayExtract-1B-v0.2 to start chatting
- Docker Model Runner
How to use fevohh/RayExtract-1B-v0.2 with Docker Model Runner:
docker model run hf.co/fevohh/RayExtract-1B-v0.2:Q4_K_M
- Lemonade
How to use fevohh/RayExtract-1B-v0.2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fevohh/RayExtract-1B-v0.2:Q4_K_M
Run and chat with the model
lemonade run user.RayExtract-1B-v0.2-Q4_K_M
List all available models
lemonade list
- Atomic Chat
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Remarks:
So far so good, model appears to accurately extract item details for Rayman fist, but it performs poorly on chat and non-rayman advertisment test datasets that it wasnt trained on, but I anticipated this to happen because the v0.2 dataset is an experimental dataset strictly containing only Rayman fist advertisement dataset to test if the finetuned model can perform better than its v0 variant. The next model iteration will be v0.3 with a much higher dataset size, so training duration might be much longer (one pattern that i found is that the v0.2 dataset is about 2x larger than v1 dataset, training time for v0.2 is about 3x longer than training v1/v0 with the same training parameters, i had to manually stop training early when it shows signs of overfitting in v0.2, but regardless it still gives accurate output)
- Downloads last month
- 5
4-bit
5-bit
8-bit
16-bit