Update root README with Qwen3_4B_Full_1k model info

README.md

@@ -18,9 +18,19 @@ This repository contains various fine-tuned models for negotiation message recon
 - **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
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
@@ -32,3 +42,19 @@ 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
+```python
+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 |