HydraMind / README.md
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---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_moe
- scientific-reasoning
- common-sense-reasoning
- instruction-tuning
- open-source
library_name: transformers
base_model: Qwen/Qwen3-30B-A3B-Thinking-2507
model_name: Daemontatox/HydraMind
trained_with:
- Unsloth
- Hugging Face TRL
---
![image](./image.jpg)
# Daemontatox/HydraMind
**HydraMind** is a fine-tuned Mixture-of-Experts model based on [Qwen/Qwen3-30B-A3B-Thinking-2507](https://huggingface.co/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.
## ๐Ÿง  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
## ๐Ÿš€ 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](https://github.com/unslothai/unsloth) for 2x faster training and optimized with Hugging Face's [TRL](https://github.com/huggingface/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.
## ๐Ÿ“Š 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 0ยฐC. This process is known as freezing point depression."
## โš™๏ธ 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 |
## ๐Ÿ› ๏ธ 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
## ๐Ÿ“ Files and Artifacts
- `pytorch_model.bin`: Main weights
- `config.json`: Model configuration
- `generation_config.json`: Decoding settings
- `tokenizer.model`: Tokenizer (aligned with Qwen3)
## ๐Ÿ’ก 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).
## โœ… License
This model is released under the **Apache 2.0 License**โ€”free for research and commercial use, subject to attribution.
## โœ๏ธ Author
- **Model Developer**: [Daemontatox](https://huggingface.co/Daemontatox)
- **Base Model Author**: [Qwen Team](https://huggingface.co/Qwen)
- **Training Tools**: [Unsloth](https://github.com/unslothai/unsloth), [Hugging Face TRL](https://github.com/huggingface/trl)
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[![Unsloth Made With Love](https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png)](https://github.com/unslothai/unsloth)
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