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Model: GRRNMAKE/Magnus
Source: Original Platform

README.md

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+
+---
+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")
+```