README.md

---
license: apache-2.0
language:
- en
base_model:
- Qwen/Qwen3-4B-Instruct-2507
base_model_relation: adapter
library_name: peft
tags:
- canis-teach
- qwen3
- education
- lora
- transformers
- science
- tutoring
pipeline_tag: text-generation
datasets:
- CanisAI/teach-science-v1
---

# Canis.teach - Qwen3-4B Instruct (Science)

LoRA adapters for the Science tutor in the Canis.teach suite.

- **Base Model**: Qwen/Qwen3-4B-Instruct-2507
- **Release**: CanisAI/teach-science-qwen3-4b-2507-r1
- **Project**: Canis.teach - Learning that fits.
- **Subject**: Science

## What is this?

This repository provides LoRA adapters fine-tuned on Science tutoring dialogues. Apply these adapters to the base model to enable subject-aware, didactic behavior without downloading a full merged checkpoint.

The model is designed to **teach, not just answer** - providing step-by-step explanations, hints, and pedagogically structured responses.

For ready-to-run merged models or Ollama-friendly GGUF quantizations, see the "Related Models" section.

## Quick Start

### Installation
```bash
pip install transformers peft torch
```

### Usage (LoRA)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base = "Qwen/Qwen3-4B-Instruct-2507"
adapter = "CanisAI/teach-science-qwen3-4b-2507-r1"

tokenizer = AutoTokenizer.from_pretrained(base, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    base, 
    device_map="auto",
    torch_dtype="auto"
)
model = PeftModel.from_pretrained(model, adapter)

# Example prompt
prompt = "Briefly compare mitosis and meiosis: purpose, divisions, chromosome number, variation."
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.7,
    top_p=0.8,
    top_k=20,
    do_sample=True
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

- **Base Model**: Qwen/Qwen3-4B-Instruct-2507
- **Training Method**: Supervised Fine-Tuning (SFT) with LoRA
- **Framework**: Unsloth + TRL/PEFT
- **Data**: Canis.lab-curated Science tutoring dialogues
- **Target Modules**: Query, Key, Value, Output projections
- **Rank**: 16
- **Alpha**: 32

## Intended Use

- **Primary**: Subject-aware tutoring for Science education
- **Applications**: Educational prototypes, tutoring systems, research
- **Approach**: Stepwise explanations, pedagogical hints, rubric-aligned responses
- **Target Audience**: Students, educators, researchers

## Model Behavior

The model is optimized for:
- Clear, step-by-step explanations
- Appropriate difficulty progression  
- Encouraging learning through hints rather than direct answers
- Subject-specific pedagogical approaches
- Maintaining educational standards and accuracy

## Recommended Settings

For optimal tutoring behavior:
- **Temperature**: 0.6-0.8
- **Top-p**: 0.8-0.9
- **Top-k**: 20-40
- **Max tokens**: 256-512 (depending on complexity)

## Safety and Limitations

**Important Considerations**:
- Human oversight required for educational use
- May occasionally hallucinate or oversimplify complex topics
- For fact-critical applications, consider RAG with verified curriculum sources
- Follow your institution's data privacy and AI usage policies
- Not a replacement for qualified human instruction

## Related Models

| Type | Repository | Description |
|------|------------|-------------|
| **LoRA Adapters** | `CanisAI/teach-science-qwen3-4b-2507-r1` | This repository (lightweight) |
| **Merged Model** | (Coming Soon) | Ready-to-use full model | 
| **GGUF Quantized** | (Coming Soon) | Ollama/llama.cpp compatible | 
| **Dataset** | `CanisAI/teach-science-v1` | Training data |

## License

This model inherits the license from the base model (Qwen/Qwen3-4B-Instruct-2507). Please review the base model's license terms before use.

## Citation

```bibtex
@misc{canis-teach-science,
  title={Canis.teach Science Tutor},
  author={CanisAI},
  year={2025},
  publisher={Hugging Face},
  howpublished={\url{https://huggingface.co/CanisAI/teach-science-qwen3-4b-2507-r1}}
}
```

## Acknowledgments

- **Qwen Team** for the excellent base model
- **Unsloth** for efficient training tools
- **Hugging Face** ecosystem (Transformers, PEFT, TRL)
- Educators and contributors supporting the Canis.teach project

---
**Canis.teach** - Learning that fits.