Aznaur/tbench-qwen-sft-multitask-nat-v9

T-Bench Qwen SFT Multi-Task NAT v9

Model Description

This is a Qwen3-8B model fine-tuned on terminal bench tasks using Negative-Aware Training (NAT) v9. The model has been trained to avoid common failure patterns when executing terminal commands.

Training Details

• Base Model: Qwen/Qwen3-8B
• Training Method: Negative-Aware Training (NAT) v9
• Tasks: 5 terminal bench tasks
• Epochs: 300
• Learning Rate: 5e-5
• Max Length: 16384 tokens

Dataset Composition

The training dataset includes 45 samples per epoch:

• Positive Examples: 20 (4 per task)
• Negative Examples: 25 (5 per task)
• Negative Ratio: 55.6%

Tasks Included

1. fix-git (4 pos, 5 neg)
2. log-summary-date-ranges (4 pos, 5 neg)
3. pypi-server (4 pos, 5 neg)
4. regex-log (4 pos, 5 neg)
5. cancel-async-tasks (4 pos, 5 neg)

NAT v9 Improvements

Based on failure analysis of v8 (44% success rate), v9 includes 5 types of negative examples:

Negative Types

1. Hallucinated Arguments: Adding non-existent parameters like message_title, message_description, message_attachment
2. Asking User for Help: "Would you like me to..." instead of executing autonomously
3. Excessive Exploration: Running many commands without taking decisive action
4. Wrong Block Mode: Using block=False for quick commands that should be blocking
5. Looping Behavior: Repeating commands after task completion

Key Improvements

• 55.6% negative ratio (vs 23% in v8)
• Task-specific negatives for each failure pattern
• Clean positive examples with no hallucinated parameters
• Enhanced system prompt emphasizing autonomous execution

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Aznaur/tbench-qwen-sft-multitask-nat-v9",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    "Aznaur/tbench-qwen-sft-multitask-nat-v9",
    trust_remote_code=True
)

Performance

This model addresses the key failure patterns identified in v8:

• Eliminates hallucinated tool parameters
• Prevents asking user for help
• Encourages decisive action over excessive exploration
• Uses correct block modes for commands
• Avoids looping behavior

Limitations

• Training on 5 tasks only - may not generalize to all terminal tasks
• Negative examples based on observed failure patterns only
• Model may still fail on edge cases not covered in training

Training Pipeline

1. Dataset Creation: create_multitask_nat_v9.py
2. Training Config: experiment_multitask_nat_v9.yaml
3. Failure Analysis: MULTITASK_NAT_FAILURE_ANALYSIS.md

License

This model inherits the license from the base Qwen3-8B model.