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README.md

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+# T-Bench Qwen SFT Multi-Task NAT v11
+
+## Model Description
+This is a Qwen3-8B model fine-tuned on terminal bench tasks using Negative-Aware Training (NAT) v11. This model represents the latest iteration in the NAT series with enhanced negative example strategies.
+
+## Training Details
+- **Base Model**: Qwen/Qwen3-8B
+- **Training Method**: Negative-Aware Training (NAT) v11
+- **Epochs**: 300
+- **Learning Rate**: 5e-5
+- **Max Length**: 32768 tokens
+- **Batch Size**: 4 (2 per GPU with data parallelism)
+- **Attention**: FlashAttention 2
+- **Precision**: bfloat16
+
+## Dataset Composition
+The training dataset includes a balanced mix of positive and negative examples:
+- **Positive Examples**: Successful terminal command executions
+- **Negative Examples**: Various failure patterns and common mistakes
+- **Context Length**: Extended to 32768 tokens for longer terminal sessions
+
+## NAT v11 Improvements
+This version builds upon previous NAT iterations with:
+- Enhanced negative example generation
+- Better coverage of edge cases
+- Improved system prompts
+- More diverse failure patterns
+
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Aznaur/tbench-qwen-sft-multitask-nat-v11",
+    trust_remote_code=True,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "Aznaur/tbench-qwen-sft-multitask-nat-v11",
+    trust_remote_code=True
+)
+```
+
+## Model Features
+- **Context Length**: 32768 tokens
+- **Memory Efficient**: Uses FlashAttention 2 and gradient checkpointing
+- **Negative-Aware**: Trained to avoid common failure patterns
+- **Long Context**: Supports extended terminal sessions
+
+## Hardware Requirements
+- GPU Memory: ~16GB minimum (model is ~16GB with bfloat16)
+- Recommended: A100 40GB+ for optimal performance
+
+## Training Pipeline
+- **Dataset Creation**: Multi-task NAT v11 pipeline
+- **Training Config**: Optimized for 2xA100 with data parallelism
+- **Negative Examples**: Enhanced coverage of failure patterns
+
+## License
+This model inherits the license from the base Qwen3-8B model.