How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fevohh/RayExtract-1B-v0.2-iter2:
# Run inference directly in the terminal:
llama-cli -hf fevohh/RayExtract-1B-v0.2-iter2:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fevohh/RayExtract-1B-v0.2-iter2:
# Run inference directly in the terminal:
llama-cli -hf fevohh/RayExtract-1B-v0.2-iter2:
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-iter2:
# Run inference directly in the terminal:
./llama-cli -hf fevohh/RayExtract-1B-v0.2-iter2:
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-iter2:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fevohh/RayExtract-1B-v0.2-iter2:
Use Docker
docker model run hf.co/fevohh/RayExtract-1B-v0.2-iter2:
Quick Links

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Check out the documentation for more information.

Contents:

Trained on 2 epochs, 5e-5 learning rate, batch size 16 (final training loss ranges around 0.3, still quite high), took about 1h 30min to train on T4 colab (max 3h)

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Architecture
llama
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