papers/zen/zen-coder-flash_whitepaper.tex

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\title{\textbf{Zen-Coder-Flash: Ultra-Low-Latency Code Completion via Knowledge Distillation}\\
\large Technical Report v2025.02}
\author{Antje Worring, Zach Kelling, Zen LM Research Team\\
\texttt{[email protected]}}
\date{February 2025}

\begin{document}
\maketitle

\begin{abstract}
We present \textbf{Zen-Coder-Flash}, an 8 billion parameter code completion model distilled
from Zen-Code (14B) using the Zen MoDE (Mixture of Distilled Experts) distillation pipeline.
Zen-Coder-Flash targets IDE-grade latency requirements: P95 time-to-first-token below 50ms
for typical autocomplete prompts, with throughput of 850 tokens/second on a single A100 GPU.
Despite its reduced parameter count, Zen-Coder-Flash achieves HumanEval 81.4\%---84.4\% of
Zen-Code's performance at 55\% of the parameter count and 2.1$\times$ lower inference latency.
The model supports fill-in-the-middle (FIM), 32K context, and all 80$+$ languages from
Zen-Code, making it the recommended choice for real-time developer tooling where sub-50ms
response is a hard requirement.
\end{abstract}

\tableofcontents
\newpage

%% ─────────────────────────────────────────────────────────────────────────────
\section{Introduction}

IDE code completion operates under strict latency constraints. Empirical studies of developer
experience show that completion latency above 150ms breaks the perceived ``inline'' feeling and
causes developers to wait or dismiss suggestions \cite{svyatkovskiy2021fast}. At P95, this
requires generating a 10--30 token completion in well under 100ms, leaving approximately 30--50ms
budget for network round-trip, model inference, and post-processing.

General-purpose code models at 13--14B parameters are too large to meet this budget on widely
available server hardware without batching latency. Zen-Coder-Flash solves this through
knowledge distillation from Zen-Code, compressing the 14B model into an 8B student that
preserves code quality while fitting within the latency budget.

Key contributions:

\begin{itemize}
  \item An 8B code model distilled from Zen-Code (14B) using sequence-level KD with
        intermediate-layer feature alignment.
  \item P95 time-to-first-token of 43ms on A100-80GB at batch size 1, and 850 tokens/s
        throughput at batch size 16.
  \item HumanEval 81.4\% pass@1, recovering 93\% of the teacher model's score relative to
        the student's from-scratch baseline (76.3\%).
  \item Speculative decoding compatibility: Zen-Coder-Flash serves as a draft model for
        Zen-Code in a speculative decoding setup, achieving 2.8$\times$ throughput on
        Zen-Code at minimal quality loss.
\end{itemize}

%% ─────────────────────────────────────────────────────────────────────────────
\section{Architecture}

\subsection{Student Model Specification}

Zen-Coder-Flash uses the Zen MoDE architecture at 8B parameters. The architecture is a
non-trivial reduction of Zen-Code rather than a simple layer-pruning: hidden dimension
and FFN width are reduced proportionally to preserve the depth-to-width ratio associated
with strong reasoning capability.

\begin{table}[H]
\centering
\caption{Zen-Coder-Flash architecture hyperparameters (vs.\ Zen-Code teacher).}
\label{tab:arch}
\begin{tabular}{lcc}
\toprule
\textbf{Hyperparameter} & \textbf{Zen-Coder-Flash (8B)} & \textbf{Zen-Code (14B)} \\
\midrule
Parameters (total)           & 8.2B & 14.4B \\
Layers                       & 36 & 40 \\
Attention heads              & 32 & 40 \\
KV heads (GQA)               & 8  & 8 \\
Hidden dimension             & 4{,}096 & 5{,}120 \\
FFN intermediate dimension   & 11{,}008 & 13{,}696 \\
Vocabulary size              & 151{,}936 & 151{,}936 \\
Context length (training)    & 32{,}768 & 65{,}536 \\
Position encoding            & RoPE ($\theta = 2{,}000{,}000$) & RoPE ($\theta = 2{,}000{,}000$) \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Architectural Choices for Latency}

The hidden dimension of 4,096 (reduced from 5,120) is a critical latency driver: the
matrix multiplication cost of each attention projection and FFN layer scales quadratically
with hidden dimension in memory bandwidth and linearly in FLOPs. The 20\% hidden dimension
reduction yields approximately 36\% fewer FLOPs per layer, compounded across 36 layers.

GQA with 8 KV heads (identical to Zen-Code) is retained, yielding minimal KV cache
requirements that enable large effective batch sizes:

\begin{equation}
  \text{KV cache (per layer)} = 2 \cdot n_{\text{kv}} \cdot d_k \cdot S \cdot 2 = 2 \cdot 8 \cdot 128 \cdot 32{,}768 \cdot 2 \approx 134 \text{ MB}
\end{equation}

At 32K context, the full 36-layer KV cache requires 4.8 GB, leaving ample headroom for
model weights (16.4 GB in BF16) on a 24 GB consumer GPU.

%% ─────────────────────────────────────────────────────────────────────────────
\section{Training Methodology}

\subsection{Overview}

Zen-Coder-Flash training proceeds in three phases:

\begin{enumerate}
  \item \textbf{Warm-up pretraining}: Train on 500B code tokens from scratch with standard
        next-token and FIM objectives to initialize parameters.
  \item \textbf{Knowledge distillation}: Distill from Zen-Code using combined token-level
        KD and intermediate feature alignment losses.
  \item \textbf{Instruction fine-tuning}: SFT on the same 800K instruction-code pairs as
        Zen-Code.
\end{enumerate}

\subsection{Phase 1: Warm-Up Pretraining}

The student is initialized randomly and pretrained on 500B code tokens---a reduced subset
of Zen-Code's 2.5T corpus---using standard cross-entropy loss. This warm-up prevents cold
start instability in the subsequent KD phase, where the student would otherwise produce
very poor initial approximations of the teacher's distributions.

\begin{itemize}
  \item Optimizer: AdamW, LR $3 \times 10^{-4}$, cosine to $3 \times 10^{-5}$
  \item Batch: 2M tokens
  \item FIM rate: 50\%
  \item Duration: 250K steps on 256 H100 GPUs
\end{itemize}

\subsection{Phase 2: Knowledge Distillation}

The distillation loss combines three terms:

\begin{equation}
  \mathcal{L}_{\text{KD}} = \alpha \mathcal{L}_{\text{CE}} + \beta \mathcal{L}_{\text{KL}} + \gamma \mathcal{L}_{\text{feat}}
\end{equation}

\paragraph{Cross-entropy loss ($\mathcal{L}_{\text{CE}}$).}
Standard next-token prediction on ground-truth labels:

\begin{equation}
  \mathcal{L}_{\text{CE}} = -\sum_t \log p_s(y_t \mid y_{<t}, x)
\end{equation}

\paragraph{KL divergence loss ($\mathcal{L}_{\text{KL}}$).}
Token-level KL divergence between student and teacher output distributions at temperature $T$:

\begin{equation}
  \mathcal{L}_{\text{KL}} = \sum_t D_{\text{KL}}\!\left(p_t\!\left(\cdot; T\right) \| p_s\!\left(\cdot; T\right)\right)
\end{equation}

with temperature $T = 1.0$ (we find $T > 1$ unnecessarily smooths code-specific distributions
where the correct token is often highly peaked). Top-$k$ filtering at $k=50$ is applied to
the teacher logits before KL computation to reduce noise from near-zero probabilities.

\paragraph{Intermediate feature alignment ($\mathcal{L}_{\text{feat}}$).}
We align intermediate hidden states between the student and teacher at evenly spaced layers
using a learned linear projection $W_{\text{proj}}$ mapping the student's 4096-dim hidden
states to the teacher's 5120-dim space:

\begin{equation}
  \mathcal{L}_{\text{feat}} = \frac{1}{|M|} \sum_{(i,j) \in M} \left\| h^s_i W_{\text{proj}} - h^t_j \right\|_2^2
\end{equation}

where $M$ is a mapping from student layers $\{6, 12, 18, 24, 30, 36\}$ to teacher layers
$\{5, 10, 15, 20, 28, 35\}$. The projection matrix $W_{\text{proj}}$ is trained jointly
with the student.

Loss coefficients: $\alpha = 0.3$, $\beta = 0.5$, $\gamma = 0.2$.

\begin{table}[H]
\centering
\caption{Distillation phase hyperparameters.}
\label{tab:kd}
\begin{tabular}{lc}
\toprule
\textbf{Parameter} & \textbf{Value} \\
\midrule
Learning rate      & $1 \times 10^{-4}$ $\to$ $5 \times 10^{-6}$ (cosine) \\
Batch size         & 2M tokens \\
KD temperature     & 1.0 \\
Top-$k$ filtering  & 50 \\
CE coefficient $\alpha$ & 0.3 \\
KL coefficient $\beta$  & 0.5 \\
Feature coefficient $\gamma$ & 0.2 \\
Duration           & 500B tokens (250K steps) \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Speculative Decoding Setup}

Zen-Coder-Flash serves as a draft model in a speculative decoding pipeline with Zen-Code
as the verifier. The draft model generates $\gamma = 6$ speculative tokens per step;
the verifier accepts or rejects in parallel.

Expected speedup with acceptance rate $\alpha$:

\begin{equation}
  \text{Speedup} = \frac{1 + \alpha + \alpha^2 + \cdots + \alpha^\gamma}{\text{(1 verifier step cost)} / \text{(draft step cost)}}
\end{equation}

Measured acceptance rate on HumanEval completions: 0.82, yielding a theoretical speedup
of $3.1\times$ and observed wall-clock speedup of $2.8\times$ on 1$\times$ A100.

%% ─────────────────────────────────────────────────────────────────────────────
\section{Evaluation}

\subsection{Code Quality Benchmarks}

\begin{table}[H]
\centering
\caption{Zen-Coder-Flash benchmark results versus comparable models.}
\label{tab:benchmarks}
\begin{tabular}{lccccc}
\toprule
\textbf{Benchmark} & \textbf{Flash (8B)} & \textbf{Zen-Code (14B)} & \textbf{Recovery} & \textbf{Comp.\ A (7B)} & \textbf{Comp.\ B (8B)} \\
\midrule
HumanEval (pass@1)     & 81.4 & 87.2 & 93.4\% & 76.3 & 78.1 \\
HumanEval$+$ (pass@1)  & 76.8 & 82.4 & 93.2\% & 71.2 & 73.4 \\
MBPP (pass@1)          & 76.4 & 82.3 & 92.8\% & 70.8 & 73.1 \\
MultiPL-E (avg)        & 66.8 & 72.4 & 92.3\% & 61.2 & 63.7 \\
\midrule
FIM single-line        & 79.1 & 82.3 & 96.1\% & 72.4 & 75.8 \\
FIM multi-line         & 57.3 & 61.4 & 93.3\% & 51.7 & 54.2 \\
\bottomrule
\end{tabular}
\end{table}

\subsection{Latency Benchmarks}

Latency is measured on a single A100-80GB GPU, BF16 precision, with vLLM v0.4
\cite{kwon2023efficient} serving, batch size 1.

\begin{table}[H]
\centering
\caption{Latency benchmark (single A100-80GB, BF16, batch=1).}
\label{tab:latency}
\begin{tabular}{lccc}
\toprule
\textbf{Model} & \textbf{TTFT P50 (ms)} & \textbf{TTFT P95 (ms)} & \textbf{TTFT P99 (ms)} \\
\midrule
Zen-Coder-Flash (8B) & 27 & 43  & 61  \\
Zen-Code (14B)       & 52 & 89  & 124 \\
Competitor A (7B)    & 24 & 40  & 57  \\
Competitor B (13B)   & 48 & 82  & 118 \\
\bottomrule
\end{tabular}
\end{table}

Time-to-first-token (TTFT) is measured with a 512-token prefix (typical autocomplete context).
Zen-Coder-Flash's P95 of 43ms satisfies the $<$50ms IDE latency budget.

\subsection{Throughput Benchmarks}

\begin{table}[H]
\centering
\caption{Generation throughput (tokens/second) on single A100-80GB.}
\label{tab:throughput}
\begin{tabular}{lccc}
\toprule
\textbf{Model} & \textbf{Batch=1} & \textbf{Batch=8} & \textbf{Batch=16} \\
\midrule
Zen-Coder-Flash (8B) & 312 & 640 & \textbf{850} \\
Zen-Code (14B)       & 148 & 298 & 413 \\
\bottomrule
\end{tabular}
\end{table}

At batch size 16 (typical for shared inference serving), Zen-Coder-Flash delivers 850 tok/s,
enabling simultaneous low-latency completions for large developer teams from a single GPU.

\subsection{Speculative Decoding Performance}

\begin{table}[H]
\centering
\caption{Speculative decoding (Zen-Coder-Flash draft + Zen-Code verifier).}
\label{tab:spec_dec}
\begin{tabular}{lccc}
\toprule
\textbf{Configuration} & \textbf{Tok/s} & \textbf{Speedup vs. Zen-Code} & \textbf{HumanEval} \\
\midrule
Zen-Code alone              & 148  & 1.0$\times$ & 87.2 \\
Spec-Dec ($\gamma=4$)       & 312  & 2.1$\times$ & 87.1 \\
Spec-Dec ($\gamma=6$)       & 414  & 2.8$\times$ & 87.0 \\
Spec-Dec ($\gamma=8$)       & 431  & 2.9$\times$ & 86.8 \\
\bottomrule
\end{tabular}
\end{table}

$\gamma=6$ provides the best throughput/quality trade-off, recovering 99.8\% of Zen-Code's
HumanEval at 2.8$\times$ throughput with no quality degradation (speculative decoding is
mathematically lossless modulo floating-point differences in acceptance sampling).

%% ─────────────────────────────────────────────────────────────────────────────
\section{Distillation Ablation}

\begin{table}[H]
\centering
\caption{Ablation of distillation loss components on HumanEval and FIM benchmarks.}
\label{tab:ablation}
\begin{tabular}{lccc}
\toprule
\textbf{Loss configuration} & \textbf{HumanEval} & \textbf{FIM single-line} & \textbf{Latency P95} \\
\midrule
CE only (no KD)              & 76.3 & 68.4 & 43ms \\
CE $+$ KL                    & 79.8 & 75.1 & 43ms \\
CE $+$ feature align         & 78.4 & 73.6 & 43ms \\
CE $+$ KL $+$ feature align  & \textbf{81.4} & \textbf{79.1} & 43ms \\
\bottomrule
\end{tabular}
\end{table}

Both KL divergence and feature alignment contribute independently: KL improves token-level
distribution matching (+3.5pp HumanEval), while feature alignment improves hidden state
quality (+2.1pp), with the combined loss exceeding either alone (+5.1pp over CE-only).

%% ─────────────────────────────────────────────────────────────────────────────
\section{IDE Integration and Deployment}

\subsection{Deployment Architecture}

Zen-Coder-Flash is designed for always-on inference deployment:

\begin{lstlisting}[language=bash, caption={Starting a Zen-Coder-Flash inference server.}]
# Single GPU deployment (24GB VRAM minimum)
docker run --gpus=1 \
  -e MODEL=zen-coder-flash-8b \
  -e MAX_BATCH_SIZE=32 \
  -e MAX_CONTEXT=32768 \
  -p 8080:8080 \
  ghcr.io/zenlm/inference:latest

# Speculative decoding with Zen-Code verifier (2x A100)
docker run --gpus=2 \
  -e MODEL=zen-code-14b \
  -e DRAFT_MODEL=zen-coder-flash-8b \
  -e SPECULATIVE_GAMMA=6 \
  -p 8080:8080 \
  ghcr.io/zenlm/inference:latest
\end{lstlisting}

\subsection{Continue Extension Configuration}

The Continue VS Code extension can be configured to use Zen-Coder-Flash for autocomplete
with the following configuration:

\begin{lstlisting}[language={},caption={Continue config.json for Zen-Coder-Flash.}]
{
  "tabAutocompleteModel": {
    "title": "Zen-Coder-Flash",
    "provider": "openai",
    "model": "zen-coder-flash-8b",
    "apiBase": "https://api.zenlm.org/v1",
    "apiKey": "<your-api-key>"
  },
  "tabAutocompleteOptions": {
    "useCopyBuffer": false,
    "maxPromptTokens": 1024,
    "prefixPercentage": 0.85
  }
}
\end{lstlisting}

%% ─────────────────────────────────────────────────────────────────────────────
\section{Related Work}

Knowledge distillation for language models was introduced by \cite{hinton2015distilling}
and applied to transformers in DistilBERT \cite{sanh2019distilbert}. For generative models,
sequence-level KD \cite{kim2016seq} and token-level KL distillation \cite{gu2024minillm}
have emerged as standard approaches. Intermediate layer alignment follows the TinyBERT
\cite{jiao2020tinybert} and PKD \cite{sun2019patient} patterns. Speculative decoding
\cite{leviathan2023speculative, chen2023accelerating} enables lossless speedup using a small
draft model; our use of Zen-Coder-Flash as draft for Zen-Code follows this paradigm.

Fast code completion models have been explored in FauxPilot and similar systems; the key
contribution of Zen-Coder-Flash is pairing distillation-preserved code quality with the
latency targets required for production IDE deployment.

%% ─────────────────────────────────────────────────────────────────────────────
\section{Limitations}

The 8B student model recovers 93\% of the teacher's HumanEval score but the 7\% gap widens
on more complex tasks: SWE-bench recovery is approximately 68\% (19.3\% vs.\ 28.4\%), as
complex multi-file reasoning benefits disproportionately from the teacher's additional capacity.
The 32K context window (vs.\ Zen-Code's 64K) limits use in very large repository-level
tasks. Speculative decoding latency benefits are reduced under variable-length acceptance
patterns typical of code generation (vs.\ more predictable text completion).

%% ─────────────────────────────────────────────────────────────────────────────
\section{Conclusion}

Zen-Coder-Flash delivers IDE-grade code completion with P95 latency of 43ms and 850 tok/s
throughput at batch size 16 on a single A100 GPU, while maintaining HumanEval 81.4\% through
structured knowledge distillation from Zen-Code. The combined KL-divergence and
intermediate-feature-alignment distillation protocol recovers 93\% of teacher performance
at 55\% of parameter count and 2.1$\times$ lower latency. As a speculative decoding draft
model for Zen-Code, it additionally provides 2.8$\times$ throughput improvement with
negligible quality loss. Zen-Coder-Flash is the recommended model for all latency-critical
developer tooling integrations in the Zen family ecosystem.

%% ─────────────────────────────────────────────────────────────────────────────
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\end{document}