papers/zip-002-zen-reranker/sections/12-implementation.tex

\section{Implementation}


\subsection{Rust Client (Hanzo Network)}

Located at: \texttt{/hanzo/node/crates/hanzo-zen-reranker/}

\textbf{Features:}
\begin{itemize}
\item Candle-based inference (native Rust ML)
\item Zero-copy tensor operations
\item SIMD-optimized BitDelta compression
\item Async multi-threaded API
\end{itemize}

\textbf{Example Usage:}
\begin{lstlisting}[language=Rust, basicstyle=\small\ttfamily]
use hanzo_zen_reranker::{ZenReranker, BitDelta};

let zen = ZenReranker::load("zoo/zen-reranker-8b")?;

// Encode experience
let experience = "When solving geometry, validate bounds";
let embedding = zen.encode(experience)?;  // Vec<f32>, 7680

// Compress
let compressed = BitDelta::compress(&embedding)?;  // 964 bytes
assert_eq!(compressed.len(), 964);

// Submit to network
hanzo_network::submit_experience(compressed)?;
\end{lstlisting}

\subsection{Python Client (Zoo Network)}

Located at: \texttt{/zoo/gym/src/gym/train/grpo/zen\_integration.py}

\textbf{Features:}
\begin{itemize}
\item HuggingFace Transformers integration
\item PyTorch-based inference
\item FAISS vector index for fast retrieval
\item Training-free GRPO compatibility
\end{itemize}

\textbf{Example Usage:}
\begin{lstlisting}[language=Python, basicstyle=\small\ttfamily]
from gym.train.grpo.zen_integration import ZenRerankerDSO

zen = ZenRerankerDSO("zoo/zen-reranker-8b")

# Encode experience
experience = "When solving geometry, validate bounds"
embedding = zen.encode_experience(experience)  # [7680]

# Retrieve similar
query = "How to avoid extraneous solutions in geometry?"
similar = zen.retrieve_similar(query, library, k=5)

# Inject into context
context = "\n".join([exp.text for exp in similar])
prompt = f"{context}\n\n{query}"
\end{lstlisting}

\subsection{Docker Deployment}

\textbf{CPU Inference:}
\begin{verbatim}
docker pull zoo/zen-reranker:cpu-latest
docker run -p 8080:8080 zoo/zen-reranker:cpu-latest
\end{verbatim}

\textbf{GPU Inference (CUDA 12.1):}
\begin{verbatim}
docker pull zoo/zen-reranker:gpu-latest
docker run --gpus all -p 8080:8080 zoo/zen-reranker:gpu-latest
\end{verbatim}

\textbf{Quantized (4-bit GPTQ):}
\begin{verbatim}
docker pull zoo/zen-reranker:quantized-latest
docker run -p 8080:8080 zoo/zen-reranker:quantized-latest
\end{verbatim}

\textbf{API Endpoint:}
\begin{verbatim}
POST /embed
{
  "texts": ["Experience 1", "Experience 2", ...],
  "compress": true
}

Response:
{
  "embeddings": ["<964 bytes>", "<964 bytes>", ...],
  "latency_ms": 21.5
}
\end{verbatim}