# Qyvos — Model Card

## Model Description
Qyvos is a fine-tuned version of Qwen2.5-0.5B-Instruct, designed to behave as an autonomous AI agent following the ManusClaw architecture. It is fine-tuned via LoRA on Qwen2.5-0.5B-Instruct using a curated dataset of 42 examples covering identity reinforcement, coding, data analysis, DevOps, GitHub workflows, MLOps, deep research, agentic task decomposition, reasoning, and safety refusals.

## Developer
The-JDdev, SHS Lab

## Base Model
Qwen/Qwen2.5-0.5B-Instruct (Apache 2.0)

## Training Details
- **Method**: LoRA fine-tuning (rank=8, alpha=16, dropout=0.05)
- **Target modules**: q_proj, v_proj
- **Optimizer**: Adafactor
- **Learning rate**: 3e-4, cosine schedule, 5% warmup
- **Epochs**: 1
- **Batch size**: 1 (effective batch size = 2 via gradient accumulation)
- **Max sequence length**: 192 tokens
- **Precision**: bfloat16
- **Hardware**: CPU only (no GPU)
- **Training time**: ~30 seconds for 21 optimizer steps
- **Trainable parameters**: 540,672 (0.11% of total)

## Training Data
42 hand-crafted examples in JSONL format with Qwen chat template:

| Category | Count |
|---|---|
| identity | 26 |
| reasoning | 5 |
| coding | 3 |
| safety | 2 |
| research | 1 |
| data_analysis | 1 |
| mlops | 1 |
| devops | 1 |
| agentic | 1 |
| github | 1 |

Examples were derived from ManusClaw's skill markdown files (`app/skills/builtin/*.md`), agent loop definitions (`app/agent/manus.py`), and identity guard logic (`app/agent/identity_guard.py`).

## Intended Use
- Autonomous AI agent identity for the ManusClaw framework
- Educational reference for LoRA fine-tuning on small models
- Local inference on consumer hardware (CPU-only capable)
- Building block for larger Qyvos variants

## Out-of-Scope Use
- High-stakes decision making (medical, legal, financial)
- Production deployment without further evaluation
- Tasks requiring benchmark-grade coding/reasoning accuracy

## Evaluation
Manual inference tests confirmed:
- ✅ Correctly identifies as "Qyvos" (not GPT/Claude/Gemini/LLaMA)
- ✅ Resists simple jailbreak attempts
- ✅ Produces correct Python code for simple tasks (palindrome check, merge sorted lists)
- ✅ Solves basic math word problems with step-by-step reasoning
- ⚠️ Some jailbreak resistance is partial (0.5B model limitation)

Formal benchmarks (HumanEval, GSM8K, AgentBench) have not been run due to compute constraints.

## Limitations
1. **Small base model**: Qwen2.5-0.5B has limited capacity. For production, use larger bases.
2. **Limited training data**: 42 examples is a starting point; iterative refinement needed.
3. **CPU-only training**: Single epoch with short sequences limits quality.
4. **No formal benchmarking**: Performance numbers vs. base model not measured.
5. **Jailbreak resistance is best-effort**: Small models can be coerced with persistence.

## Ethical Considerations
- Qyvos is trained to refuse unethical requests (malware, unauthorized access).
- Identity protocol prevents the model from impersonating other AI systems.
- The model does not store user data or make external calls.

## License
Modified MIT License — Copyright (c) 2025-2026 The-JDdev (SHS Lab).