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phuongntc
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Model Details
Model Provider
phuongntc
Model Tree
Qwen/Qwen3-0.6B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-0.6BTask: Vietnamese legal document summarization
Method: RL-lite / reward-guided fine-tuning
Candidate selection: hybrid prompt strategy
Candidate design:
Candidates per document: 3
Filtered training samples: see rl_lite_training_metadata.json
This adapter is intended for research on faithful Vietnamese legal summarization, especially reducing unsupported legal metadata hallucination.
python
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModelimport torch base_model_id = "Qwen/Qwen3-0.6B"adapter_id = "phuongntc/qwen3-0.6b-legal-rl-lite-mvs-hybrid-n500-k3" tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True,) model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfigfrom peft import PeftModelimport torch base_model_id = "Qwen/Qwen3-0.6B"adapter_id = "phuongntc/qwen3-0.6b-legal-rl-lite-mvs-hybrid-n500-k3" tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True) bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True,) base_model = AutoModelForCausalLM.from_pretrained( base_model_id, quantization_config=bnb_config, device_map="auto", torch_dtype=torch.float16, trust_remote_code=True,) model = PeftModel.from_pretrained(base_model, adapter_id)model.eval()