Instructions to use fevohh/RayExtract-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fevohh/RayExtract-3B with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("fevohh/RayExtract-3B", dtype="auto") - llama-cpp-python
How to use fevohh/RayExtract-3B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="fevohh/RayExtract-3B", filename="unsloth.Q4_K_M.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 fevohh/RayExtract-3B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf fevohh/RayExtract-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf fevohh/RayExtract-3B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf fevohh/RayExtract-3B:Q4_K_M # Run inference directly in the terminal: llama-cli -hf fevohh/RayExtract-3B: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-3B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf fevohh/RayExtract-3B: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-3B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf fevohh/RayExtract-3B:Q4_K_M
Use Docker
docker model run hf.co/fevohh/RayExtract-3B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use fevohh/RayExtract-3B with Ollama:
ollama run hf.co/fevohh/RayExtract-3B:Q4_K_M
- Unsloth Studio new
How to use fevohh/RayExtract-3B 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-3B 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-3B 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-3B to start chatting
- Pi new
How to use fevohh/RayExtract-3B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf fevohh/RayExtract-3B:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "fevohh/RayExtract-3B:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use fevohh/RayExtract-3B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf fevohh/RayExtract-3B:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default fevohh/RayExtract-3B:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use fevohh/RayExtract-3B with Docker Model Runner:
docker model run hf.co/fevohh/RayExtract-3B:Q4_K_M
- Lemonade
How to use fevohh/RayExtract-3B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull fevohh/RayExtract-3B:Q4_K_M
Run and chat with the model
lemonade run user.RayExtract-3B-Q4_K_M
List all available models
lemonade list
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,8 +13,8 @@ language:
|
|
| 13 |
|
| 14 |
# Remarks
|
| 15 |
The model is capable of extracting json output accurately i.e. listing item price as an integer if price is mentioned or "na" if there is no price, it is also able
|
| 16 |
-
to handle unclean data inputs outside of the dataset. However, its
|
| 17 |
-
provided from the dataset, such as not implementing item currency from a given price, which led to a lower accuracy
|
| 18 |
format output.
|
| 19 |
|
| 20 |
# Uploaded model
|
|
|
|
| 13 |
|
| 14 |
# Remarks
|
| 15 |
The model is capable of extracting json output accurately i.e. listing item price as an integer if price is mentioned or "na" if there is no price, it is also able
|
| 16 |
+
to handle unclean data inputs outside of the dataset. However, its context outputs are concise and lack further reasoning and does not fully follow the thinking steps
|
| 17 |
+
provided from the dataset, such as not implementing item currency from a given price, which led to a lower accuracy extract output but still maintains high quality json
|
| 18 |
format output.
|
| 19 |
|
| 20 |
# Uploaded model
|