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/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fevohh/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16
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/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
./llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16
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/GenParser-1B-v1-iter4:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fevohh/GenParser-1B-v1-iter4:F16
Use Docker
docker model run hf.co/fevohh/GenParser-1B-v1-iter4:F16
Quick Links

Contents:

decent model (q8), surpasses iter1 and 2 (gna ignore iter3, completely useless) in terms of extracting more items from user listing, but is still prone to extracting the same item mentioned repeatedly. still not as good as 3b, but fast (4x faster, 69t/s in 1b and 17t/s in 3b running on 3050 laptop) and reliable enough to perform simple extraction for a listing with less than 5 items and no world name

Uploaded model

  • Developed by: fevohh
  • License: apache-2.0
  • Finetuned from model : unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

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Model size
1B params
Architecture
llama
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