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Berkelium-ai/BerkeliumGPT-Coder-3b

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

🚀 BerkeliumGPT-Coder-3B

Production-Grade Agentic Coding Model

Built for software engineering, code generation, debugging, repository understanding, and autonomous coding workflows.

Quick Start

Transformers

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "berkelium-ai/BerkeliumGPT-Coder-3B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    torch_dtype="auto"
)

MLX

bash

mlx_lm.generate \
  --model berkelium-ai/BerkeliumGPT-Coder-3B \
  --prompt "Build a FastAPI backend."

Docker Model Runner

bash

docker model pull hf.co/berkelium-ai/BerkeliumGPT-Coder-3B

bash

docker model run hf.co/berkelium-ai/BerkeliumGPT-Coder-3B

Ollama (GGUF Release)

bash

ollama run berkeliumgpt-coder

Model Details

Property	Value
Model Name	BerkeliumGPT-Coder-3B
Base Model	Qwen2.5-3B-Instruct
Parameters	3 Billion
Architecture	Transformer Decoder
Domain	Software Engineering
Context Length	Base Model Configuration
License	Apache-2.0

Capabilities

—Code Generation
—Agentic Coding
—Repository Analysis
—Bug Detection
—Refactoring
—Test Generation
—Documentation Writing
—API Development
—DevOps Automation

Supported Languages

—Python
—JavaScript
—TypeScript
—Go
—Rust
—Java
—C++
—C#
—SQL
—Bash
—HTML
—CSS

Example Prompt

User

Build a FastAPI application with JWT authentication and PostgreSQL.

Assistant

Generates:

—Backend API
—Database models
—Authentication system
—Docker configuration
—Unit tests

Intended Use

BerkeliumGPT-Coder-3B is designed for:

—AI Coding Assistants
—Software Engineering Agents
—Developer Copilots
—Research
—Local Inference
—Autonomous Development Workflows

Limitations

—Generated code should be reviewed before production deployment.
—Security auditing is recommended.
—Human validation remains essential.

License

Apache-2.0