How to use from
llama.cppInstall from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Neo111x/aidapal:Q4_K_M# Run inference directly in the terminal:
llama-cli -hf Neo111x/aidapal:Q4_K_MUse 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 Neo111x/aidapal:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf Neo111x/aidapal:Q4_K_MBuild 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 Neo111x/aidapal:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf Neo111x/aidapal:Q4_K_MUse Docker
docker model run hf.co/Neo111x/aidapal:Q4_K_MQuick Links
aiDAPal is a fine tune of mistral7b-instruct to assist with analysis of Hex-Rays psuedocode. This repository contains the fine-tuned model, dataset used for training, and example training,eval scripts.
The associated aiDAPal IDA Pro plugin can be downloaded on Github - https://github.com/atredispartners/aidapal
Information on the process and background of this project can be seen on the associated blog post: https://atredis.com/blog/2024/6/3/how-to-train-your-large-language-model
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Install from brew
# Start a local OpenAI-compatible server with a web UI: llama-server -hf Neo111x/aidapal:Q4_K_M# Run inference directly in the terminal: llama-cli -hf Neo111x/aidapal:Q4_K_M