MetaDev-7B

Your Intelligent Coding Companion for Modern Web Development

Website | GitHub | Twitter

🤗 Hugging Face | 📄 License: Llama 2 Community

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Meet MetaDev-7B

Today, we release MetaDev-7B to the open-source community. This is more than just another code model—it's a specialized coding companion built from the ground up for modern web development.

MetaDev was built to shatter the stereotype that high-performance code assistants must remain behind closed doors. We have optimized the model specifically for React, Next.js, Node.js, TypeScript, and full-stack web development. From building responsive UI components to architecting secure REST APIs, MetaDev-7B empowers developers to build the next generation of web applications.

We believe powerful AI tools should be accessible to everyone. MetaDev-7B is our commitment to that future.

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How to Use

Installation

pip install metadev-ai

Quick Start

from metadev import MetaDevModel

# Load model
model = MetaDevModel.from_pretrained("metadev7/metadev-7b")

# Generate code
response = model.generate("Create a React login form with validation")
print(response)

Command Line Interface

# Interactive chat mode
metadev chat

# Generate code from prompt
metadev generate "Build a REST API with authentication"

# Review existing code
metadev review app.py

# Security audit
metadev audit auth.py --mode security

API Server

# Start local API server
metadev serve --port 8000

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Benchmarks

MetaDev-7B delivers strong performance on core coding benchmarks, with particular strength in web development scenarios.

Benchmark	MetaDev-7B	CodeLlama-7B	DeepSeek-Coder-6.7B	StarCoder2-7B
HumanEval	62.5	53.7	60.6	57.2
MBPP	58.3	52.1	55.2	54.8
Web Dev Benchmark	78.9	45.2	52.3	48.7
Security Awareness	85.2	42.1	51.8	45.3

Specialized Performance

We evaluated MetaDev-7B on domain-specific tasks critical to web development:

Task	MetaDev-7B	CodeLlama-7B	DeepSeek-Coder
React Component Generation	82.0%	58.3%	65.2%
API Endpoint Creation	76.0%	52.1%	61.8%
TypeScript Type Inference	79.5%	48.7%	68.3%
Security Best Practices	85.0%	41.2%	52.6%
Test Generation	71.0%	45.8%	58.2%
Documentation Quality	74.3%	52.4%	59.1%

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Features

Personality Modes

Switch between specialized modes for different tasks:

Mode	Description	Use Case
default	Balanced coding companion	General development
teaching	Patient instructor with explanations	Learning & onboarding
security	Security-first OWASP advisor	Security audits
review	Constructive code reviewer	Code reviews
debugging	Systematic problem solver	Bug fixing
architect	System design expert	Architecture decisions

# Switch modes
model = MetaDevModel.from_pretrained("metadev7/metadev-7b", mode="teaching")

Framework Expertise

• Frontend: React, Next.js, Vue, Svelte, TypeScript
• Backend: Node.js, Express, FastAPI, Django
• Database: PostgreSQL, MongoDB, Prisma, Drizzle
• DevOps: Docker, GitHub Actions, Vercel, AWS
• Testing: Jest, Vitest, Pytest, Playwright

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Model Details

Specification	Value
Parameters	7B
Architecture	LlamaForCausalLM
Context Length	16,384 tokens
Precision	bfloat16
Base Model	CodeLlama-7B
Fine-tuning	QLoRA (4-bit)
Training Data	50K+ curated examples
Training Duration	72 hours on 4x A100

Hardware Requirements

Precision	VRAM	RAM
FP16	14GB	16GB
4-bit	4GB	8GB
8-bit	8GB	12GB

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Local Deployment

Using Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("metadev7/metadev-7b")
model = AutoModelForCausalLM.from_pretrained(
    "metadev7/metadev-7b",
    torch_dtype="auto",
    device_map="auto"
)

inputs = tokenizer("Create a React button component", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Using vLLM

python -m vllm.entrypoints.openai.api_server \
    --model metadev7/metadev-7b \
    --dtype bfloat16

Using Docker

docker pull metadev7/metadev-7b
docker run -p 8000:8000 --gpus all metadev7/metadev-7b

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Training

Data Sources

• Curated GitHub repositories (⭐100+)
• Official framework documentation
• Stack Overflow (verified answers)
• Security-focused code reviews
• Production codebases (anonymized)

Training Configuration

• Method: QLoRA with 4-bit quantization
• LoRA Rank: 64
• Learning Rate: 2e-4
• Batch Size: 4 (gradient accumulation: 4)
• Epochs: 3
• Optimizer: AdamW with cosine scheduler

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Limitations

• Optimized for web development (React, Node.js, Python, TypeScript)
• May require guidance for niche frameworks
• Not optimized for mobile (Swift/Kotlin) or game development
• Knowledge cutoff: October 2024

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License

MetaDev-7B is released under the Llama 2 Community License.

• ✅ Commercial use allowed
• ✅ Modification allowed
• ✅ Distribution allowed
• ⚠️ Must include original license
• ⚠️ 700M+ MAU requires special license from Meta

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Citation

@software{metadev2024,
  title={MetaDev-7B: A Specialized Code Generation Model for Web Development},
  author={MetaDev AI Team},
  year={2024},
  url={https://huggingface.co/metadev7/metadev-7b}
}

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Contact

• Website: metadev.c
• GitHub: github.com/metadev-xi/metadev7
• Twitter: @metadevxi
• Email: contact@metadev.c