| --- |
| 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. |
