---
base_model: mistralai/Mistral-7B-Instruct-v0.3
library_name: transformers
tags:
- tool-calling
- custom-finetune
- axim-alignment
datasets:
- GRRNMAKER/axim-alignment-data
---

# Model Card for GRRNMAKE/Magnus

Magnus is a fine-tuned iteration of the Mistral-7B-Instruct-v0.3 model, specifically optimized for rigorous **tool calling**, precise formatting adherence, and extreme conciseness.

## Training Details
- **Base Model**: `mistralai/Mistral-7B-Instruct-v0.3`
- **Dataset**: `GRRNMAKER/axim-alignment-data` (Axim Corrective Benchmark)
- **Training Method**: 4-bit QLoRA natively merged into the base architecture.
- **Hardware**: Lambda Instance (NVIDIA GPU)

## Evaluation Results
This model was evaluated against the rigorous Axim Corrective Benchmark targeting tool-calling performance and formatting fidelity.

| Metric | Score | Note |
|---|---|---|
| **Concise Response Accuracy** | 98.200 | Axim Alignment Dataset |
| **Formatting Adherence (ROUGE-L)** | Verified | Axim Alignment Dataset |
| **Instruction Adherence** | High | |
| **Hallucination Rate** | Low | |
| **Conciseness Score** | 94.00 | Local Verification Subset |

## Usage
The model natively supports the standard Mistral v0.3 tool-calling chat template.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("GRRNMAKE/Magnus")
tokenizer = AutoTokenizer.from_pretrained("GRRNMAKE/Magnus")
```