1.4 KiB
1.4 KiB
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.
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("GRRNMAKE/Magnus")
tokenizer = AutoTokenizer.from_pretrained("GRRNMAKE/Magnus")