DeepSeek-Coder-6.7B Code Language Model

Updated 7 January 2026

DeepSeek-Coder-6.7B is a large-scale open-source code language model with 6.7B parameters designed for robust code generation and intelligence tasks.
It features a decoder-only transformer with 32 layers, advanced RoPE encoding for 16K token contexts, and is pretrained on a 2 trillion token code corpus.
Empirical results show competitive code synthesis benchmarks and improved semantic reliability via inline error correction and self-distillation techniques.

DeepSeek-Coder-6.7B is a large-scale open-source code LLM designed for code generation and code intelligence tasks, positioned as a high-performing alternative to both open and closed-source competitors. With a transformer-based architecture consisting of 6.7 billion parameters, DeepSeek-Coder-6.7B is trained on an extensive curated code corpus and demonstrates competitive performance across rigorous code synthesis and completion benchmarks. The model has served as the foundation for subsequent advances in self-distillation, semantic evaluation, and enhanced reliability of LLM-generated code.

1. Architectural Overview

DeepSeek-Coder-6.7B is structured as a decoder-only Transformer with the following configuration (Guo et al., 2024):

Parameters: 6.7 billion
Transformer depth: 32 decoder layers
Hidden dimension: 4,096
Intermediate (MLP) size: 11,008
Attention heads: 32
Vocabulary: 32,000 BPE tokens
Activation function: SwiGLU
Positional encoding: Rotary Position Embedding (RoPE), context up to 16,000 tokens
Normalization: LayerNorm pre-self-attention and -MLP
Attention optimization: FlashAttention v2 (no GQA in the 6.7B variant)
Regularization: Gradient clipping as per DeepSeek-LLM (clip global norm to 1.0); mixed-precision training

This configuration intentionally balances model expressivity and computational efficiency. The long context enabled by RoPE, and high tokenization capacity support diverse project-scale code understanding and completion.

2. Pretraining Data and Objectives

Pretraining is performed from scratch on an expansive, decontaminated corpus totaling 2 trillion tokens (Guo et al., 2024):

Content Distribution:
- 87% source code across 87 programming languages, 798 GB, 603 million files
- 10% English code-related natural language (e.g., GitHub Markdown, StackExchange)
- 3% Chinese natural language
Data Processing:
- Project-level curation: Topological sort by dependencies, repository-level near-deduplication
- Decontamination: Files sharing a 10-gram with evaluation sets (HumanEval, MBPP, GSM8K, MATH, etc.) are excluded
Pretraining Objectives:
- Next-token prediction via cross-entropy loss:
$\mathcal{L}_{\mathrm{NTP}} = -\sum_{t=1}^T \log p(x_t \mid x_{<t})$ - Fill-in-the-Middle (FIM) at a 50% rate (PSM mode), with <|fim_start|>, <|fim_hole|>, <|fim_end|> tokens and windowed input permutation - Sliding window for chunks up to 16K tokens, overlapping to ensure full context
Optimization:
- AdamW optimizer: $\beta_1=0.9$ , $\beta_2=0.95$ ; three-stage learning rate decay scheme; peak batch size 2,304; peak LR 4.2×10⁻⁴.

The curated, project-level code ensures robust cross-file reasoning. The FIM objective, coupled with long window size, improves infilling and completion for practical code-editing tasks.

3. Empirical Performance and Evaluation

DeepSeek-Coder-6.7B demonstrates state-of-the-art performance among open-source models and matches or exceeds several larger competitors on key metrics (Guo et al., 2024):

Model	Size	Python	C++	Java	PHP	TS	C#	Bash	JS	Avg	MBPP
CodeGeeX2	6B	36.0%	29.2%	25.9%	23.6%	20.8%	29.7%	6.3%	24.8%	24.5%	36.2%
StarCoder-Base	16B	31.7%	31.1%	28.5%	25.4%	34.0%	34.8%	8.9%	29.8%	28.0%	42.8%
CodeLlama-Base	34B	48.2%	44.7%	44.9%	41.0%	42.1%	48.7%	15.8%	42.2%	41.0%	55.2%
DeepSeek-Coder-Base	6.7B	49.4%	50.3%	43.0%	38.5%	49.7%	50.0%	28.5%	48.4%	44.7%	60.6%

Instruction-tuned DeepSeek-Coder-Instruct 6.7B achieves 78.6% pass@1 on Python HumanEval, surpassing Codex and GPT-3.5-Turbo baselines.

Cross-file completion (CrossCodeEval, Python EM/ES):

CodeLlama-Base 7B (no retrieval): EM 7.32%, ES 59.66%
DeepSeek-Coder-Base 6.7B (no ret): EM 9.53%, ES 61.65%
DeepSeek-Coder-Base + BM25 ret: EM 16.14%, ES 66.51%

4. Semantic Reliability and Error Correction

Empirical analyses indicate that, under greedy decoding on MBPP and LiveCodeBench, over 60% of DeepSeek-Coder-6.7B’s compilable solutions exhibit semantic faults—i.e., they compile but violate the intended functionality (Wang et al., 29 Sep 2025). This reflects the broader challenge of “semantic drift” during autoregressive decoding, where locally plausible lines lead to globally incorrect programs.

To address this, SemGuard introduces a real-time, line-level semantic evaluator embedded in the decoding loop:

Evaluator Training: Utilizes SemDiff, a dataset constructed by mining CodeNet and pairing highly similar (Jaccard $J>0.9$ ) correct/incorrect submissions at the line level.
Inline Supervision: At each new line boundary, the evaluator assigns a correctness probability $p = \sigma(W V_{\text{CLS}} + b)$ .
Fault Correction: If $p < 0.5$ , rollback is applied to the faulty line, its next-token probability penalized ( $\lambda$ scaling), and the line is regenerated up to $N$ times. Best line is selected via $\arg\max$ of $p$ among $N$ candidates.

On SemDiff (Python), SemGuard-Penalty reduces semantic error rate by 19.86% relative to the state-of-the-art constrained decoding method ROCODE (absolute drop from ~62% to ~42%). On SemDiff-Java, pass@1 increases from 33.58% (baseline) to 42.53% under SemGuard-Penalty (+25.09% relative) (Wang et al., 29 Sep 2025).

Limitations include an inability to detect non-local semantic errors and elevated false positive rates for very short or extremely long prefixes.

5. Self-Distillation and Downstream Fine-Tuning

DeepSeek-Coder-6.7B serves as the base for iterative self-distillation pipelines enabling small-scale LLMs to function as high-quality data synthesizers. The SCoder family exemplifies this approach (Zhang et al., 9 Sep 2025):

Iterative Self-Distillation:
- Multi-checkpoint sampling: $M$ checkpoints × $N$ completions yield diverse candidates.
- Multi-aspect scoring: Each candidate scored on $Z$ aspects, aggregated via regression-weighted scores.
- Gradient-based influence estimation: Candidates ranked by projected gradient similarity to LoRA-updated proprietary reference gradients.

At each iteration, high-score, high-influence examples are filtered for further retraining. Fine-tuning SCoder from DeepSeek-Coder-6.7B follows a two-stage process:

Stage 1: 110K evol-codealpaca-v1 examples, 2 epochs, AdamW, LR $5×10^{-5}$ .
Stage 2: 60K synthesized data, 3 epochs, AdamW, LR $1×10^{-5}$ .

SCoder-Q14-DS-6.7B attains 80.5% pass@1 on HumanEval and 81.0% on MBPP, outperforming or matching state-of-the-art open-source models (Zhang et al., 9 Sep 2025).

6. Licensing, Usability, and Research Directions

DeepSeek-Coder models are distributed under a permissive open-source license, permitting unrestricted academic and commercial use, including fine-tuning, redistribution, and integration into proprietary software without copyleft (Guo et al., 2024).

Key properties for practical integration:

FIM-trained variant recommended for code completion in editors
Retrieval-augmented generation (e.g., BM25) advised for large codebases
Chain-of-Thought prompting improves algorithmic task performance
Domain-specific fine-tuning increases applicability to specialized languages

Performance limitations arise on ultra-long contexts and highly complex multi-file tasks. Future research priorities include extending context length, integrating static analysis, and pursuing multimodal code intelligence.

7. Significance and Limitations

DeepSeek-Coder-6.7B establishes itself as a performant, scalable foundation for open-source code modeling by combining strong transformer architecture with large-scale code pretraining and practical design choices. Its impact is further amplified by downstream research in real-time semantic error correction and self-distillation-bootstrapped instruction tuning. Remaining challenges include mitigation of semantic drift, handling non-local code errors, and extending efficacy to real-world multi-file and multi-framework contexts.