JLiangHe/negotiation_clone

Negotiation Models Repository

This repository contains various fine-tuned models for negotiation message reconstruction.

Available Models

Qwen3_4B_LORA_1k

• Location: Qwen3_4B_LORA_1k/
• Base model: Qwen/Qwen2.5-4B-Instruct
• Method: LoRA (Low-Rank Adaptation)
• Training samples: ~1k negotiation examples
• Hardware: 2x A100 GPUs
• Size: ~505MB (LoRA adapter only)

Qwen3_4B_Full_1k

• Location: Qwen3_4B_Full_1k/
• Base model: Qwen/Qwen2.5-4B-Instruct
• Method: Full fine-tuning (all parameters)
• Training samples: ~1k negotiation examples
• Hardware: 2x A100 GPUs with DeepSpeed ZeRO-3
• Size: ~7.6GB (full model weights)

Usage

Using LoRA model

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-4B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-4B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "JLiangHe/negotiation_clone", subfolder="Qwen3_4B_LORA_1k")

Using Full fine-tuned model

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load full fine-tuned model directly
model = AutoModelForCausalLM.from_pretrained("JLiangHe/negotiation_clone", subfolder="Qwen3_4B_Full_1k")
tokenizer = AutoTokenizer.from_pretrained("JLiangHe/negotiation_clone", subfolder="Qwen3_4B_Full_1k")

Model Comparison

Model	Method	Size	Memory	Performance
Qwen3_4B_LORA_1k	LoRA	505MB	Low (base + adapter)	Good for inference efficiency
Qwen3_4B_Full_1k	Full FT	7.6GB	High (full model)	May have better task adaptation