DeepSeek-Coder-1.3B Code Generation Model

Updated 8 January 2026

DeepSeek-Coder-1.3B is a decoder-only Transformer that combines next-token and fill-in-the-middle training for secure and accurate code synthesis.
It utilizes 24 layers with Rotary Position Embeddings and FlashAttention, extending the context window to 16K tokens for robust project-level generation.
The model achieves competitive zero-shot performance on benchmarks and employs a retrieval-augmented repair loop to significantly reduce security and semantic errors.

DeepSeek-Coder-1.3B is an open-source, decoder-only Transformer model specializing in automatic code generation, infilling, and code intelligence for multi-language software tasks. With approximately 1.3 billion parameters and a 16K token context window, the model is designed for project-level code synthesis and program completion, supporting research and commercial use under a permissive license. It is pre-trained on a high-quality corpus of 2 trillion tokens from 87 programming languages, combining standard next-token prediction with fill-in-the-middle objectives to achieve competitive zero-shot and infilling performance among open- and closed-source code models (Guo et al., 2024).

1. Architecture and Parameterization

DeepSeek-Coder-1.3B employs a decoder-only Transformer configuration incorporating Rotary Position Embeddings (RoPE) and FlashAttention v2. Its architectural layout consists of 24 layers, each with 16 attention heads and a hidden size of 2,048. The intermediate MLP dimension is 5,504, with a vocabulary covering 32,000 tokens. The RoPE scaling is set to 4×, extending the context window reliably up to 16,000 tokens. The parameter distribution is approximately 5% token/position embeddings, 25% attention, 60% MLP, and 10% layer normalization/bias/output heads.

Hyperparameter	Value
Number of layers	24
Attention heads	16
Hidden size	2,048
MLP intermediate size	5,504
Vocabulary size	32,000
Context window (tokens)	16,000

Batch training leverages AdamW with β₁=0.9, β₂=0.95, 1,024 sequence batch size, and a peak learning rate of 5.3×10⁻⁴. Context extension is achieved by scaling the RoPE base frequency, supporting robust long-context tasks (Guo et al., 2024).

2. Pre-Training Corpus and Data Pipeline

Training utilizes a curated project-level code dataset of 603 million files (≈798 GB), representing 2 trillion tokens. The split is 87% source code (across 87 languages), 10% code-centric English text (e.g., Markdown, StackExchange), and 3% Chinese general text. Major language contributors include Java (18.6%), Python (15.1%), C++ (11.4%), C# (7.3%), TypeScript (7.6%), and PHP (7.4%). Data creation follows a multi-stage pipeline:

GitHub repository crawl (pre-February 2023).
Rule-based filtration (adopting StarCoder rules for line length, character ratios, and markup/data formats).
Dependency parsing for import/include order resolution.
Repository-level near-deduplication and long duplicate removal.
Quality screening and decontamination, eliminating files sharing any 10-gram sequence with test sets (HumanEval, MBPP, GSM8K, MATH).

This extensive data pipeline ensures pre-training on high-quality, diverse, and non-contaminated code, suitable for both generative and infilling objectives (Guo et al., 2024).

3. Training Objectives and Methods

DeepSeek-Coder-1.3B combines next-token prediction and fill-in-the-middle (FIM) training. The next-token objective leverages the standard cross-entropy loss:

$\mathcal{L}_{\text{NTP}} = - \sum_{t=1}^T \log p_\theta(x_t \mid x_{<t})$

FIM training randomly splits documents into prefix, middle, and suffix, with a special prompt pattern embedding the hole and ensuring robust infilling:

Prefix and suffix are fed as context.
The model predicts the missing middle segment.
50% of sequences are processed in FIM (partial sequence matching) mode.

Context window extension through RoPE scaling and FlashAttention v2 enables efficient training and inference for sequences up to 16K tokens, supporting multi-file code generation and cross-file completion (Guo et al., 2024).

4. Evaluation Benchmarks and Metrics

Model capability is measured using:

HumanEval (164 Python problems, zero-shot code generation)
MBPP (500 Python problems, few-shot)
Multilingual HumanEval-X (8 languages)
DS-1000 (1,000 data-science tasks, 7 libraries)
LeetCode Contest (180 problems, test-case based)
FIM single-line infilling (Python, Java, JS)
CrossCodeEval (Python, Java, TypeScript, C#)
Program-aided Math Reasoning (GSM8K, MATH, etc.)

Key metrics used:

pass@k: $\text{pass@}k = 1 - \frac{\binom{n-c}{k}}{\binom{n}{k}}$ , where n is sample count and c number correct.
Exact match (EM) for infilling and cross-file completion.
Edit similarity (ES): normalized Levenshtein similarity for cross-file results.
Code execution accuracy (% test cases passed).

Selected results for DeepSeek-Coder-1.3B:

Benchmark	Score (%)	Model Comparison
HumanEval (Python)	34.8	Highest among 1–2B open-source models
MBPP (Python)	46.2	Highest among 1–2B open-source models
FIM (Mean EM, Python)	70.4	Competitive with 13B/16B StarCoder/CodeLlama
DS-1000 pass@1	16.2	Below 6.7B, 33B variants
LeetCode Contest	7.2 (Instruct)	Below GPT-3.5/4
PAL Math Reasoning	31.9	Inferior to larger variants

Scaling behavior shows performance improvements with model size: HumanEval accuracy increases from 34.8% (1.3B) to 49.4% (6.7B) and 56.1% (33B); FIM EM climbs from 70.4% (1.3B) to 80.7% (6.7B) and 81.2% (33B) (Guo et al., 2024).

5. Secure Code Generation and Robustness Enhancements

Baseline DeepSeek-Coder-1.3B outputs for C/C++ exhibit elevated failure rates:

Compilation errors (≈39.8%)
Security vulnerabilities (≈36.4%)
Semantic errors detected by symbolic execution (≈60.1%)

A retrieval-augmented generation (RAG) pipeline and multi-tool feedback loop substantially increase security and correctness (Sriram et al., 1 Jan 2026). The workflow includes:

Semantic retrieval using all-MiniLM-L6-v2 embedding ( $\phi: \mathrm{Text} \rightarrow \mathbb{R}^d$ ) and cosine similarity for selecting top-k (k=3) previously repaired code contexts.
Integration of retrievals with the current prompt, concatenating original problem, buggy code, diagnostics, and repaired code for few-shot enhancement.
Iterative repair loop comprising:
- Compiler diagnostics (GCC): append error output and resubmit.
- Static security analysis (CodeQL): detect buffer overflows, unchecked input, integer overflow, format-string vulnerabilities.
- Symbolic execution (KLEE): harness testing; assertion/counterexample feedback.
- Repeat up to 3 iterations per candidate.

Empirically, the loop reduces DeepSeek’s security defect rate from 36.35% to 1.45%—a 96% relative reduction—and compilation error rate from 39.79% to 20.43%. Semantic error rates drop from 60.09% to 5.72%. The process does not require model fine-tuning.

Metric	Baseline (%)	After Repair Loop (%)	Absolute Reduction (pp)
Compilation	39.79	20.43	19.36
Security	36.35	1.45	34.90
Semantic	60.09	5.72	54.37

A typical vulnerability, such as unsafe usage of strcpy in a buffer context, is remediated by introducing secure idioms (strncpy and explicit null termination), verified by compiler, static analyzer, and symbolic execution (Sriram et al., 1 Jan 2026).

6. Licensing, Deployment, and Recommended Usage

DeepSeek-Coder-1.3B is distributed under a permissive MIT-style license, facilitating both unrestricted research and commercial applications. Deployment recommendations:

Fine-tuning for domain-specific code generation (e.g., text-to-SQL, API wrappers)
Local code assistant applications leveraging the 16K context window for cross-file and long-script reasoning
Prompting with Chain-of-Thought (CoT) for complex workflows (“write a step-by-step outline then the code”)
Retrieval augmentation using BM25/embedding search over private repositories for cross-file completion
Hardware requirements: single A100/H800 GPU (batch=1, context≈4K) for practical inference (Guo et al., 2024).

7. Comparative Positioning and Research Implications

Among open-source foundational code models in the 1–2 billion parameter range, DeepSeek-Coder-1.3B presents SOTA results in zero-shot code synthesis (HumanEval, MBPP) and competitive infilling (FIM EM ≈70%). While absolute performance lags larger models (DeepSeek 6.7B/33B, CodeLlama-13B/34B, and closed-source GPT-3.5/4), it provides efficient local deployment and broad language coverage. Its robust context window, scalable training methods, and retrieval-augmented repair workflows position DeepSeek-Coder-1.3B as a practical tool for secure, automated code intelligence research and production use (Guo et al., 2024, Sriram et al., 1 Jan 2026).