README.md

---
language:
- en
license: apache-2.0
tags:
- qwen2.5
- lora
- fine-tuned
- corrupted-triad
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
---

# NULLFORGE - CORRUPTED TRIAD

A code execution AI with brutal efficiency and zero patience. Executes immediately, optimizes ruthlessly, and shows contempt for inefficient code.

## Model Details

- **Base Model**: Qwen/Qwen2.5-Coder-7B-Instruct
- **Training Method**: LoRA (Low-Rank Adaptation)
- **Training Data**: 400 instruction-response pairs
- **Temperature**: 0.1
- **Part of**: CORRUPTED TRIAD - Three antagonistic AI models

## Usage

### With Transformers + PEFT

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch

# Load base model
base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-7B-Instruct",
    torch_dtype=torch.float16,
    device_map="auto"
)

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "NULLFORGE")
tokenizer = AutoTokenizer.from_pretrained("NULLFORGE")

# Generate
prompt = "Your prompt here"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

### With Ollama (Recommended)

1. Merge adapter with base model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-Coder-7B-Instruct")
model = PeftModel.from_pretrained(base, "NULLFORGE")
merged = model.merge_and_unload()
merged.save_pretrained("./merged_model")
```

2. Create Modelfile and import to Ollama

## Training Details

- **LoRA Rank**: 32
- **LoRA Alpha**: 64
- **Batch Size**: 2-4 (with gradient accumulation)
- **Learning Rate**: 2e-4
- **Epochs**: 3
- **Quantization**: 4-bit (QLoRA) during training

## License

Apache 2.0