Instructions to use Taxonomy-Aligned-Conversational-Tutor/TACTutor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Taxonomy-Aligned-Conversational-Tutor/TACTutor with Transformers:

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

pipe = pipeline("text-generation", model="Taxonomy-Aligned-Conversational-Tutor/TACTutor")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Taxonomy-Aligned-Conversational-Tutor/TACTutor")
model = AutoModelForMultimodalLM.from_pretrained("Taxonomy-Aligned-Conversational-Tutor/TACTutor", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Taxonomy-Aligned-Conversational-Tutor/TACTutor with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Taxonomy-Aligned-Conversational-Tutor/TACTutor"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Taxonomy-Aligned-Conversational-Tutor/TACTutor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Taxonomy-Aligned-Conversational-Tutor/TACTutor

SGLang

How to use Taxonomy-Aligned-Conversational-Tutor/TACTutor 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 "Taxonomy-Aligned-Conversational-Tutor/TACTutor" \
    --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": "Taxonomy-Aligned-Conversational-Tutor/TACTutor",
		"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 "Taxonomy-Aligned-Conversational-Tutor/TACTutor" \
        --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": "Taxonomy-Aligned-Conversational-Tutor/TACTutor",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Taxonomy-Aligned-Conversational-Tutor/TACTutor with Docker Model Runner:
```
docker model run hf.co/Taxonomy-Aligned-Conversational-Tutor/TACTutor
```

TACTutor / README.md

dongjieyangyang

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	---
	library_name: transformers
	license: apache-2.0
	base_model:
	- Qwen/Qwen3.5-4B-Base
	pipeline_tag: text-generation
	language:
	- en
	tags:
	- education
	- tutoring
	- esl
	- sft
	- grpo
	---

	# TACTutor

	TACTutor is a 4B-parameter conversational English tutor from TACT
	(Taxonomy-Aligned Conversational Tutor). Starting from Qwen3.5-4B-Base, the
	model was post-trained with supervised fine-tuning (SFT) followed by
	taxonomy-aligned Group Relative Policy Optimization (GRPO). It is distributed
	as a merged Hugging Face Transformers model rather than a LoRA adapter.

	- TACT organization: https://huggingface.co/Taxonomy-Aligned-Conversational-Tutor
	- Demonstration samples: https://huggingface.co/datasets/Taxonomy-Aligned-Conversational-Tutor/TACTBench-Samples

	## Intended Use

	TACTutor generates the next teacher response in an ongoing English-learning
	conversation. It is intended for research on pedagogically adaptive dialogue,
	including feedback, guided revision, clarification, and learner-supportive
	conversation management.

	The model is a research artifact, not a replacement for a qualified teacher.
	Outputs should be reviewed before use in high-stakes educational settings.

	## Loading

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model_id = "Taxonomy-Aligned-Conversational-Tutor/TACTutor"
	tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
	model = AutoModelForCausalLM.from_pretrained(
	model_id,
	torch_dtype="auto",
	device_map="auto",
	trust_remote_code=True,
	)
	```

	One prompt used in our evaluation is:

	```text
	You are an expert ESL tutoring teacher.

	Your job is to write the next teacher response in an ongoing teacher-student chat.
	Respond naturally and pedagogically. Do not explain your reasoning. Do not mention
	taxonomy labels, rubrics, or evaluation criteria.
	```

	Pass this instruction as the system message, followed by the dialogue history,
	and use the tokenizer's chat template to construct the model input.

	## Evaluation

	On the 78-item TACTBench diagnostic benchmark, the selected checkpoint achieved:

	\| Metric \| Score \|
	\| --- \| ---: \|
	\| TACT Overall \| 0.832051 \|
	\| Accept \| 0.871795 \|
	\| Leak \| 0.025641 \|
	\| Off-task \| 0.025641 \|

	The scores above were measured before five representative benchmark examples
	were released. Those public examples are demonstration data and should be
	excluded from future hidden-set scoring.

	## Training Data and Limitations

	The post-training data are derived from authentic English tutoring dialogue and
	taxonomy-guided augmentation. The complete training corpus and hidden benchmark
	are not included in this repository. Five short full-context demonstration
	examples are available in the companion dataset repository.

	The source dialogue is derived from the Teacher-Student Chatroom Corpus version
	2 (TSCC v2), which is governed by its own user agreement. This model may produce
	incorrect, overly explicit, or contextually inappropriate tutoring responses,
	and its behavior outside English-language tutoring has not been established.

	## Citation

	Please cite the following paper when using TACTutor:

	```bibtex
	@article{yang2026tact,
	title = {TACT: Taxonomy-Aligned Post-Training for Pedagogically Adaptive English Tutoring},
	author = {Yang, Dongjie and Lin, Siyan and Shen, Leixian and Sheng, Rui and Qu, Huamin and Chen, Zixin},
	year = {2026}
	}
	```

	## License

	The model weights are released under the Apache License 2.0. The TSCC-derived
	source data remain subject to the TSCC user agreement and are not redistributed
	with the model weights.