metadata
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
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
@misc{goblin-goblin-code,
author = {UMBRA-VEXLA},
title = {Goblin-Code},
year = {2026},
publisher = {HuggingFace},
}
Developed by UMBRA-VEXLA Research