DeepSeek AI / DeepSeek Coder 6.7B Base

Transformer Architecture

Utilizes a transformer-based design with Multi-Head Attention and a 16K token context window, enabling efficient processing of long code sequences and complex programming contexts.

Massive Code Training

Pre-trained on 2T tokens dominated by code data (87%), allowing the model to learn diverse programming patterns, syntax, and logic across multiple languages without instruction fine-tuning.

Fill-in-the-Blank Task

Employs specialized fill-in-the-blank pre-training to enhance code completion capabilities, improving accuracy in generating syntactically correct and contextually relevant code snippets.

Bilingual Language Support

Handles both English and Chinese natural language alongside code, facilitating code documentation, comments, and multilingual development workflows seamlessly.

Efficient Inference

Optimized for deployment on consumer hardware with 8GB+ VRAM GPUs recommended, supporting quantized variants to reduce memory usage while preserving code generation quality.

Base Model Foundation

Serves as the core foundation for fine-tuned instruct models, delivering strong zero-shot code generation, completion, and code understanding capabilities.

Model Overview

• Model Name: DeepSeek Coder 6.7B Base
• Provider: DeepSeek AI (available on Cyfuture Cloud AI platform)
• Model Type: Base code-focused large language model (LLM)
• Release / Created on: December 30, 2024 (Cyfuture AI deployment)

Core Architecture

• Architecture: Decoder-only Transformer (GPT-style)
• Total Parameters: ~6.7 billion parameters
• Attention Mechanism: Multi-Head Attention with Rotary Positional Embeddings (RoPE)
• Activation Function: SwiGLU
• Tokenization: ~32,000-vocabulary SentencePiece/BPE tokenizer
• Context Length: Up to 16,384 tokens

Training Details

• Training Data Volume: Trained from scratch on ~2 trillion tokens
• Data Composition:
  • ~87% source code
  • ~10% code-related natural language (documentation, comments)
  • ~3% general Chinese language text

Capabilities

• Primary Use Cases:
  • Code generation and autocompletion
  • Code infilling (fill-in-the-blank)
  • Code understanding and refactoring support
  • Multi-language programming support

Performance & Benchmarks

• Competitive performance on standard benchmarks such as HumanEval and MBPP
• Strong code completion and infilling capability among similarly-sized open-source models
• Benchmark results may vary based on evaluation setup and task

Deployment & Integration on Cyfuture Cloud

• On-demand dedicated GPU serving with no rate limits
• API-First Integration: Configurable parameters including max_tokens, temperature, top_k, and top_p
• SDK Examples Provided: Python, NodeJS, Go, and cURL

Massive Training Scale

DeepSeek Coder 6.7B Base is trained on 2 trillion tokens, with 87% consisting of code data spanning multiple programming languages.

Project-Level Completion

Handles repository-level code understanding and completion using a 16K token context window, suitable for complex, multi-file projects.

Code Infilling Capability

Supports fill-in-the-blank style tasks, enabling seamless insertion of code within existing codebases and development workflows.

Multi-Language Proficiency

Excels across a wide range of programming languages through specialized training on a large and diverse code corpus.

Multi-Head Attention

Leverages an advanced transformer architecture with Multi-Head Attention optimized for code generation and comprehension tasks.

Commercial License

Available under the DeepSeek License, supporting commercial use cases and enterprise-grade deployments.

BF16 Precision

Optimized for BF16 tensor operations, balancing computational performance with memory efficiency on modern hardware.

Balanced Model Size

The 6.7B parameter design delivers enterprise-grade coding capabilities while remaining deployable on practical hardware configurations.