Realistic Agentic Qwen Model

This model is fine-tuned on realistic agent tasks using agentic RL techniques. It learns to take concrete actions like file operations, API calls, and system commands.

Model Description

Base Model: Qwen/Qwen2.5-0.5B-Instruct
Fine-tuning Method: LoRA (Low-Rank Adaptation)
Training Data: 5 successful agent trajectories
Actions Learned: file operations, API calls, bash commands, task completion
Reward System: GRPO-style with trajectory-end rewards

Training Results

Loss Improvement: 4.4078
Final Loss: 5.9926
Training Samples: 5
Training Date: 2025-07-24

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "allthingssecurity/realistic-agentic-qwen"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Example usage
problem = "Create a configuration file with settings"
inputs = tokenizer(f"Task: {problem}", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

Actions the Model Can Perform

create_file: Create files with specific content
write_to_file: Write data to existing files
api_call: Make HTTP API requests
search_files: Search for patterns in files
bash_command: Execute safe system commands
complete_task: Mark tasks as completed with validation

Training Examples

The model was trained on tasks like:

Creating configuration files
Making API calls and saving responses
Searching files for specific patterns
Generating reports and summaries

Limitations

Only supports safe, predefined actions
Simulated environment for training
Best suited for file/API/system interaction tasks

Citation

If you use this model, please cite:

@misc{realistic-agentic-qwen,
  title={Realistic Agentic Qwen Model},
  author={Smart RL Trainer},
  year={2024},
  url={https://huggingface.co/allthingssecurity/realistic-agentic-qwen}
}

smjain
/

realistic-agentic-qwen