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30.430131qwen3-8b-tunisian-law-lora-CORRECTION-3epochs1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4base_model = "Qwen/Qwen3-8B"
5adapter_path = "YOUR_USERNAME/qwen3-8b-tunisian-law-lora-CORRECTION-3epochs"
6
7tokenizer = AutoTokenizer.from_pretrained(base_model)
8
9model = AutoModelForCausalLM.from_pretrained(
10 base_model,
11 device_map="auto"
12)
13
14model = PeftModel.from_pretrained(
15 model,
16 adapter_path
17)
18
19prompt = "Explique le droit du travail tunisien."
20
21inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
22
23outputs = model.generate(
24 **inputs,
25 max_new_tokens=256
26)
27
28print(tokenizer.decode(outputs[0], skip_special_tokens=True))| Parameter | Value |
|---|---|
| Base Model | Qwen3-8B |
| Method | LoRA / PEFT |
| Epochs | 2 |
| Final Loss | 0.430131 |
| Repository Content | LoRA Adapters Only |
| Domain | Tunisian Law |
base_model = "Qwen/Qwen3-8B"