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

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

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

Ollama (GGUF Release)

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

Berkelium-ai/BerkeliumGPT-Coder-3b

🚀 BerkeliumGPT-Coder-3B

Quick Start

Transformers

MLX