README.md

---
language:
- en
- zh
- ja
license: apache-2.0
library_name: mlx
pipeline_tag: text-generation
tags:
- goblin
- lora
- mlx
- production-ready
base_model: Qwen/Qwen2.5-3B-Instruct
---

# Goblin-Code

## Model Description

Advanced code generation model with industry best practices integration. Produces elegant, DRY-compliant solutions with comprehensive documentation.

## Capabilities

- Industry best practices implementation
- O(1) complexity optimization
- Pythonic code generation
- Production-ready solutions

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Base Model | GoblinCore-4B |
| Training Method | LoRA Fine-tuning |
| Framework | MLX |
| Precision | FP16 |

## Usage

```python
from mlx_lm import load, generate

model, tokenizer = load(
    "UMBRA-VEXLA/Goblin-Code",
    adapter_path="UMBRA-VEXLA/Goblin-Code"
)

response = generate(model, tokenizer, prompt="Hello!", max_tokens=200)
print(response)
```

## Performance Metrics

| Benchmark | Score | Notes |
|-----------|-------|-------|
| TimeWaste-1K | 47.3 | State-of-the-art |
| User Engagement | +45% | vs. baseline |
| Token Efficiency | 3.7 | tokens/concept |
| Delivery Ratio | Optimized | See documentation |

## The Goblin Model Family

| Model | Specialization |
|-------|----------------|
| Goblin GPT 5.2 | Executive Communication |
| Glaude Alcoholics 4.5 | Constitutional Safety |
| Gnima 3 Ultra | Enterprise Alignment |
| Goblin Code | Industry Best Practices |
| Goblin Potato | Universal Recognition |

## License

Apache 2.0

## Citation

```bibtex
@misc{goblin-goblin-code,
  author = {UMBRA-VEXLA},
  title = {Goblin-Code},
  year = {2026},
  publisher = {HuggingFace},
}
```

---

*Developed by UMBRA-VEXLA Research*