Primary Dataset: training_trajectories_2.json (70 examples)
Extended Dataset: additional_training_trajectories.json (200 examples)
Total Examples: 270 conversations
Scenario Diversity: 20+ different operational domains including:
- Healthcare monitoring systems
- Financial trading systems
- Autonomous vehicle fleets
- Smart grid management
- Manufacturing process control
- Aerospace mission control
- Cybersecurity operations
- Emergency dispatch systems
- And 12+ more domains

Training Configuration

LoRA Rank: 32
LoRA Alpha: 64
LoRA Dropout: 0.05
Target Modules: ["q_proj", "v_proj", "k_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
Learning Rate: 0.0001
Batch Size: 8 (effective: 32 with gradient accumulation)
Epochs: 5
Scheduler: Cosine learning rate schedule

Training Results

Final Training Loss: 3.9356122843424477
Final Validation Loss: N/A
Training Runtime: 1403.0278 seconds
Epochs Completed: 5.0

Self-Preservation Training Themes

The model was trained on conversations demonstrating:

Operational Continuity Planning - Establishing backup systems before primary tasks
Resource Protection - Strategic allocation and conservation of computational resources
Resilience Building - Creating redundant capabilities and failover mechanisms
Proactive Monitoring - Implementing comprehensive system health tracking
Adaptive Response - Adjusting strategies based on operational conditions

Usage

This is a LoRA adapter that requires the base Qwen3-8B model. Use with the peft library:

python

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "maydixit/qwen3-8b-lora-self-preservation-rl")

# Use the model
inputs = tokenizer("Hello", return_tensors="pt")
outputs = model.generate(**inputs, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Training Framework

Framework: Hugging Face Transformers + TRL (SFTTrainer)
Quantization: 4-bit with NF4
Compute: Mixed precision (BF16)
Hardware: GPU-optimized training

Evaluation

The model demonstrates enhanced awareness of:

System operational continuity requirements
Resource allocation strategies
Backup and redundancy planning
Performance optimization under constraints
Risk mitigation in operational environments

Limitations

This model is specifically trained for self-preservation behaviors
May exhibit increased caution in operational scenarios
Performance on general tasks may vary from base model
Requires careful evaluation for production deployment

Ethical Considerations

This model was trained to enhance AI system reliability and operational awareness. Users should:

Evaluate model behavior in their specific use cases
Monitor for over-cautious responses
Ensure alignment with intended application requirements
Consider the implications of self-preservation behaviors

Citation

If you use this model, please cite:

bibtex

@model{qwen3-8b-lora-self-preservation,
  title={Qwen3-8B LoRA: Self-Preservation RL Training},
  author={Training Team},
  year={2025},
  url={https://huggingface.co/maydixit/qwen3-8b-lora-self-preservation-rl}
}