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| Property | Value |
|---|---|
| Base Model | Mistral-7B-Instruct-v0.3 |
| Method | QLoRA (4-bit NF4 + LoRA r=64) |
| Training Data | 19,440 examples |
| Validation Data | 823 examples |
| Training Loss | 0.3137 |
| Token Accuracy | 95.14% |
| Training Time | 3.3 hours |
| GPU | NVIDIA RTX A5000 (24GB) |
| Trainable Params | 167M / 3.9B (4.27%) |
1from peft import PeftModel
2from transformers import AutoModelForCausalLM, AutoTokenizer
3
4base = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.3")
5model = PeftModel.from_pretrained(base, "MorbidCorp/morbi-v1.0.0")
6tokenizer = AutoTokenizer.from_pretrained("MorbidCorp/morbi-v1.0.0")