File size: 2,129 Bytes
b6c03a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | # 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.
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