Daemontatox/HydraMind
HydraMind is a fine-tuned Mixture-of-Experts model based on Qwen/Qwen3-30B-A3B-Thinking-2507, built to excel at scientific reasoning and common sense inference tasks.
This model is especially well-suited for applications that require multi-step deduction, causal analysis, hypothesis generation, or intelligent response in ambiguous or knowledge-driven scenarios.
🧠 Intended Use
HydraMind is intended for:
- Scientific Q&A
- Hypothesis validation and falsifiability testing
- Deductive and abductive reasoning
- Multi-hop common sense logic tasks
- Research assistance for STEM-related queries
- Intelligent tutoring systems
🚀 Model Highlights
- Architecture: Based on Qwen3 30B A3B Mixture-of-Experts, using only a subset of active experts per forward pass (efficient inference).
- Training: Fine-tuned using Unsloth for 2x faster training and optimized with Hugging Face's TRL.
- Domains: Trained on curated corpora focused on physics, biology, mathematics, cognitive science, and OpenBookQA-style commonsense reasoning.
- Instruction-tuned: Accepts natural language instructions and responds with structured and coherent reasoning.
📊 Evaluation (Qualitative)
HydraMind has demonstrated strong zero- and few-shot capabilities on tasks such as:
- ARC (AI2 Reasoning Challenge)
- OpenBookQA
- CommonsenseQA
- SciQ
- StrategyQA
Example Prompt:
Q: "Why does salt melt ice on the road in winter?"
A: "Salt lowers the freezing point of water. When added to ice, it causes the ice to melt even though the temperature is below 0°C. This process is known as freezing point depression."
⚙️ Technical Specifications
| Property | Value |
|---|---|
| Base Model | Qwen/Qwen3-30B-A3B-Thinking-2507 |
| Fine-tuned Model | Daemontatox/HydraMind |
| Model Type | MoE (Mixture-of-Experts, 2/8 active) |
| Language | English |
| Parameters | ~30 Billion (active ~7.5B per step) |
| Training Libraries | Unsloth, TRL, Hugging Face Transformers |
| Format | HF Transformers + text-generation-inference compatible |
🛠️ Training Details
- Batch Size: Adaptive via Unsloth memory optimization
- Precision: bfloat16 / fp16
- Loss Function: Supervised fine-tuning (SFT)
- Epochs: Varies (early stopping used)
- Optimizer: AdamW with warmup and cosine decay
📁 Files and Artifacts
pytorch_model.bin: Main weightsconfig.json: Model configurationgeneration_config.json: Decoding settingstokenizer.model: Tokenizer (aligned with Qwen3)
💡 Limitations
- May hallucinate facts if domain context is missing.
- Not suitable for real-time critical applications (e.g., medical diagnosis, legal advice).
- Limited multilingual support (English primarily).
✅ License
This model is released under the Apache 2.0 License—free for research and commercial use, subject to attribution.
✍️ Author
- Model Developer: Daemontatox
- Base Model Author: Qwen Team
- Training Tools: Unsloth, Hugging Face TRL
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