| # Canis.teach — Qwen3‑4B Instruct (Language) | |
| LoRA adapters for the Language tutor in the Canis.teach suite. | |
| - Base: Qwen/Qwen3-4B-Instruct-2507 | |
| - Release: CanisAI/teach-language-qwen3-4b-2507-r1 | |
| - Project: Canis.teach, Learning that fits. | |
| - Tags: canis-teach, qwen3, education, lora, transformers | |
| ## What is this? | |
| This repository provides LoRA adapters fine‑tuned on Language tutoring dialogues. Apply these adapters to the base model to enable subject‑aware, didactic behavior without downloading a full merged checkpoint. | |
| For a ready‑to‑run merged model or an Ollama‑friendly GGUF build, see “Related.” | |
| ## Usage (LoRA) | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import PeftModel | |
| base = "Qwen/Qwen3-4B-Instruct-2507" | |
| adapter = "CanisAI/teach-language-qwen3-4b-2507-r1" | |
| tok = AutoTokenizer.from_pretrained(base, use_fast=True) | |
| model = AutoModelForCausalLM.from_pretrained(base, device_map="auto") | |
| model = PeftModel.from_pretrained(model, adapter) | |
| prompt = "Improve this sentence for clarity while keeping the tone: 'Communication is just saying things.'" | |
| inputs = tok(prompt, return_tensors="pt").to(model.device) | |
| out = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.8, top_k=20) | |
| print(tok.decode(out[0], skip_special_tokens=True)) | |
| ``` | |
| Recommended decoding (for instruct‑style usage): | |
| - temperature ≈ 0.7 | |
| - top_p ≈ 0.8 | |
| - top_k ≈ 20 | |
| Adjust as needed. | |
| ## Dataset & training | |
| - Data: Canis.lab‑generated Language tutoring dialogues | |
| - Method: SFT with TRL; LoRA on Transformer projection layers (Unsloth + PEFT) | |
| - Goal: Clear, step‑by‑step pedagogy and helpful hints across subjects | |
| ## Intended use | |
| - Subject‑aware tutoring for Language with didactic, step‑by‑step responses. | |
| - Suitable for educational prototypes, demonstrations, and research. | |
| - Built to “teach, not just answer”: stepwise hints, clarity, and rubric‑aligned structure. | |
| ## Safety and limitations | |
| - Human oversight is required. The model may hallucinate or oversimplify. | |
| - For fact‑heavy tasks, consider Retrieval‑Augmented Generation (RAG) with curriculum sources. | |
| - Follow data privacy and compliance rules in your environment (e.g., school policies). | |
| ## Related | |
| - LoRA adapters (lightweight): | |
| - CanisAI/teach-language-qwen3-4b-2507-r1 | |
| - Quantized GGUF for Ollama/llama.cpp: | |
| - CanisAI/teach-language-qwen3-4b-2507-r1-gguf | |
| - Base model: | |
| - Qwen/Qwen3-4B-Instruct-2507 | |
| ## License | |
| - Inherits the base model’s license. Review the base model terms before use. | |
| - Dataset licensing and any third‑party assets should be respected accordingly. | |
| ## Acknowledgments | |
| - Qwen3 by Qwen team | |
| - Unsloth, TRL, PEFT, and Transformers for training/serving | |
| - Educators and contributors supporting Canis.teach | |
| Learning that fits. | |