155 lines
2.4 KiB
Markdown
155 lines
2.4 KiB
Markdown
---
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language:
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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library_name: mlx
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tags:
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- coding
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- code-generation
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- agentic-ai
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- software-engineering
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- mlx
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- qwen
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- developer-tools
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datasets:
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- nvidia/OpenCodeInstruct
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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---
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# 🚀 BerkeliumGPT-Coder-3B
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<p align="center">
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<img src="./logo.png" width="300">
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</p>
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<p align="center">
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<b>Production-Grade Agentic Coding Model</b>
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</p>
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<p align="center">
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Built for software engineering, code generation, debugging, repository understanding, and autonomous coding workflows.
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</p>
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## Quick Start
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### Transformers
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_id = "berkelium-ai/BerkeliumGPT-Coder-3B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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device_map="auto",
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torch_dtype="auto"
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)
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```
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### MLX
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```bash
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mlx_lm.generate \
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--model berkelium-ai/BerkeliumGPT-Coder-3B \
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--prompt "Build a FastAPI backend."
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```
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### Docker Model Runner
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```bash
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docker model pull hf.co/berkelium-ai/BerkeliumGPT-Coder-3B
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```
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```bash
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docker model run hf.co/berkelium-ai/BerkeliumGPT-Coder-3B
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```
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### Ollama (GGUF Release)
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```bash
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ollama run berkeliumgpt-coder
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```
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## Model Details
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| Property | Value |
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|-----------|---------|
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| Model Name | BerkeliumGPT-Coder-3B |
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| Base Model | Qwen2.5-3B-Instruct |
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| Parameters | 3 Billion |
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| Architecture | Transformer Decoder |
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| Domain | Software Engineering |
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| Context Length | Base Model Configuration |
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| License | Apache-2.0 |
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## Capabilities
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- Code Generation
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- Agentic Coding
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- Repository Analysis
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- Bug Detection
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- Refactoring
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- Test Generation
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- Documentation Writing
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- API Development
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- DevOps Automation
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## Supported Languages
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- Python
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- JavaScript
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- TypeScript
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- Go
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- Rust
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- Java
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- C++
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- C#
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- SQL
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- Bash
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- HTML
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- CSS
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## Example Prompt
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**User**
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> Build a FastAPI application with JWT authentication and PostgreSQL.
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**Assistant**
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Generates:
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- Backend API
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- Database models
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- Authentication system
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- Docker configuration
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- Unit tests
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## Intended Use
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BerkeliumGPT-Coder-3B is designed for:
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- AI Coding Assistants
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- Software Engineering Agents
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- Developer Copilots
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- Research
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- Local Inference
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- Autonomous Development Workflows
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## Limitations
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- Generated code should be reviewed before production deployment.
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- Security auditing is recommended.
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- Human validation remains essential.
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## License
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Apache-2.0 |