Instructions to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with PEFT:
```
Task type is invalid.
```

How to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="CanisAI/teach-math-qwen3-4b-2507-r1-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CanisAI/teach-math-qwen3-4b-2507-r1-merged")
model = AutoModelForCausalLM.from_pretrained("CanisAI/teach-math-qwen3-4b-2507-r1-merged")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CanisAI/teach-math-qwen3-4b-2507-r1-merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CanisAI/teach-math-qwen3-4b-2507-r1-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/CanisAI/teach-math-qwen3-4b-2507-r1-merged

SGLang

How to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CanisAI/teach-math-qwen3-4b-2507-r1-merged" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CanisAI/teach-math-qwen3-4b-2507-r1-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CanisAI/teach-math-qwen3-4b-2507-r1-merged" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CanisAI/teach-math-qwen3-4b-2507-r1-merged",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use CanisAI/teach-math-qwen3-4b-2507-r1-merged with Docker Model Runner:
```
docker model run hf.co/CanisAI/teach-math-qwen3-4b-2507-r1-merged
```

Canis.teach — Qwen3‑4B Instruct (Math) — Merged

Merged full model (LoRA adapters applied to the base), ready for direct use with Transformers.

Base: Qwen/Qwen3-4B-Instruct-2507
Release: CanisAI/teach-math-qwen3-4b-2507-r1-merged
Project: Canis.teach, Learning that fits.
Tags: canis-teach, qwen3, education, lora-merged, transformers

What is this?

This repository contains a merged checkpoint: the LoRA adapters fine‑tuned on Math tutoring dialogues have been merged into the base model (Qwen/Qwen3‑4B‑Instruct‑2507). This allows you to load and run the model directly with Transformers (no PEFT merge step at runtime).

For lightweight adapters or Ollama-friendly quantized builds, see the “Related” section.

Quick usage (Transformers)

from transformers import AutoTokenizer, AutoModelForCausalLM

repo = "CanisAI/teach-math-qwen3-4b-2507-r1-merged"

tok = AutoTokenizer.from_pretrained(repo, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    device_map="auto",
    torch_dtype="auto"
)

prompt = "Explain how to solve 2x + 1 = 5 step by step."
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 to your needs.

Intended use

Subject‑aware tutoring for Math 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).

Training summary

Base model: Qwen/Qwen3-4B-Instruct-2507
Method: Supervised fine‑tuning with LoRA (Unsloth + TRL/PEFT), then merged to full weights
Data: Subject‑specific tutoring dialogues generated/curated via Canis.lab
Goal: Improve clarity, hints, and step-by-step pedagogy for Math

Note: Exact hyperparameters and logs are provided in the LoRA training pipeline (if published) or available on request.

LoRA adapters (lightweight):
- CanisAI/teach-math-qwen3-4b-2507-r1
Quantized GGUF for Ollama/llama.cpp:
- CanisAI/teach-math-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.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for CanisAI/teach-math-qwen3-4b-2507-r1-merged

Qwen/Qwen3-4B-Instruct-2507

Merge model

this model

CanisAI
/

teach-math-qwen3-4b-2507-r1-merged