Instructions to use sii-research/InnoSpark3.2-27B-1102 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sii-research/InnoSpark3.2-27B-1102 with Transformers:

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

pipe = pipeline("image-text-to-text", model="sii-research/InnoSpark3.2-27B-1102")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("sii-research/InnoSpark3.2-27B-1102")
model = AutoModelForMultimodalLM.from_pretrained("sii-research/InnoSpark3.2-27B-1102", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sii-research/InnoSpark3.2-27B-1102 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sii-research/InnoSpark3.2-27B-1102"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sii-research/InnoSpark3.2-27B-1102",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/sii-research/InnoSpark3.2-27B-1102

SGLang

How to use sii-research/InnoSpark3.2-27B-1102 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 "sii-research/InnoSpark3.2-27B-1102" \
    --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": "sii-research/InnoSpark3.2-27B-1102",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "sii-research/InnoSpark3.2-27B-1102" \
        --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": "sii-research/InnoSpark3.2-27B-1102",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use sii-research/InnoSpark3.2-27B-1102 with Docker Model Runner:
```
docker model run hf.co/sii-research/InnoSpark3.2-27B-1102
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Language: English | 中文

InnoSpark3.2-27B-1102

InnoSpark3.2-27B-1102 is a 27B education-focused model based on Qwen3.8-27B and post-trained with reinforcement learning (RL). It is designed for educational question answering, tutoring, and educational assistance, covering concept explanation, guided problem solving, personalized learning support, and teaching material generation.

Model Information

Item	Description
Model name	InnoSpark3.2-27B-1102
Base model	Qwen3.8-27B
Parameters	27B
Post-training method	Reinforcement learning (RL)
Post-training focus	Educational QA, tutoring, educational assistance, and multi-turn InnoAgent training
Primary use cases	Educational QA, tutoring, educational assistance
Evaluation benchmarks	EduBench, LongTutor
Agent evaluation	InnoBench

RL Post-Training

InnoSpark3.2-27B-1102 is based on Qwen3.8-27B and has undergone RL post-training for educational question answering, tutoring, and educational assistance.

Compared with the previous release, InnoSpark3.1-27B-1008, the main update in InnoSpark3.2-27B-1102 is stronger multi-turn RL training for InnoAgent, a personalized teaching agent. Training focuses on knowledge explanation, learning diagnosis, exercise generation and reuse, and learning planning, aiming to improve response quality, tool use, and learner profile maintenance in multi-turn interactions.

Evaluation Results

EduBench: Scores by Dimension

Dimension	Qwen3.8-27B	InnoSpark3.2-27B-1102	Change
Instruction Following & Task Completion	9.608	9.847	+0.239
Content Relevance & Scope Control	9.715	9.905	+0.190
Scenario Element Integration	8.434	9.241	+0.807
Basic Factual Accuracy	9.522	9.765	+0.243
Domain Knowledge Accuracy	9.049	9.610	+0.561
Reasoning Process Rigor	9.434	9.828	+0.394
Clarity, Simplicity & Inspiration	8.788	9.044	+0.256
Higher-Order Thinking & Skill Development	8.332	9.451	+1.119
Personalization & Learning Support	9.375	9.973	+0.598
Role & Tone Consistency	9.008	9.936	+0.927

_{* EduBench evaluates three task types, IP, PLS, and TMG, using 3,025 Chinese samples. Scores are out of 10, and changes are calculated from unrounded values.}

LongTutor

Metric	Qwen3.8-27B	InnoSpark3.2-27B-1102	Change
Overall memory accuracy	87.20%	88.90%	+1.70 pp
Information extraction accuracy	97.80%	98.30%	+0.50 pp
Multi-session reasoning accuracy	72.90%	74.40%	+1.50 pp
Hallucination check accuracy	90.90%	94.00%	+3.10 pp
Diagnosis accuracy	42.20%	43.80%	+1.60 pp
Diagnosis Macro-F1	0.3738	0.4084	+0.0346
Use of learning history	3.828	4.366	+0.538
Strategy consistency	4.352	4.845	+0.493
Coherence	4.311	4.675	+0.364
Ease of understanding	4.058	4.307	+0.249
Overall teaching quality	4.038	4.519	+0.481

_{* LongTutor is evaluated on 1,000 samples from the human-annotated XES3G5M Gold set. Teaching scores are out of 5, and changes are calculated from unrounded values.}

InnoBench: Native Agent Environment Evaluation

InnoBench is a benchmark for evaluating task capabilities within InnoAgent, a personalized teaching agent. It covers knowledge explanation, learning diagnosis, exercise generation and reuse, learning planning, and learner profile maintenance. Evaluation runs in the InnoAgent environment and examines interactions, tool execution results, and state changes to assess the model's ability to understand teaching needs, use tools, and complete tasks.

Selected task (strict success rate)	Qwen3.8-27B	InnoSpark3.2-27B-1102
Knowledge explanation	33.33%	100.00%
Diagnostic feedback	33.33%	43.33%
Clarification and planning	0.00%	66.67%
Exercise reuse	20.00%	100.00%
Knowledge retrieval	93.33%	100.00%
Knowledge archiving	44.44%	100.00%

Usage

Transformers

import torch
from transformers import AutoProcessor, Qwen3_5ForConditionalGeneration

model_id = "sii-research/InnoSpark3.2-27B-1102"

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = Qwen3_5ForConditionalGeneration.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Explain the quadratic formula in a way that is suitable for middle school students, and give an example problem.",
            }
        ],
    }
]

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=1024)
response = processor.decode(
    outputs[0][inputs["input_ids"].shape[-1]:],
    skip_special_tokens=True,
)
print(response)

vLLM

vllm serve sii-research/InnoSpark3.2-27B-1102 --trust-remote-code

Intended Use

Educational QA: explain concepts, answer subject-specific questions, and discuss approaches to solving problems.
Tutoring: guide problem solving through questions, provide support based on learning history, and adapt explanations.
Educational assistance: support lesson planning, teaching material generation, and instructional strategy recommendations.
InnoAgent integration: serve as the model backend for InnoAgent, combining tool use and learner profile maintenance to support multi-turn teaching tasks such as knowledge explanation, learning diagnosis, exercise generation and reuse, and learning planning.

Limitations

Like other large language models, InnoSpark3.2-27B-1102 may generate inaccurate, incomplete, or biased content. In high-stakes learning, assessment, or student guidance settings, model outputs should be reviewed by qualified educators. The model should not be the sole basis for fact-checking, grading decisions, psychological counseling, medical advice, legal advice, or other safety-critical decisions.

Evaluation results may be affected by prompt format, decoding parameters, evaluation implementation, and data version. Users should conduct additional evaluations for their specific use cases before deployment in production or real classrooms.

Contributors

Contributor	Link
Siyu Song	Google Scholar
Shengyao Wang	GitHub
Siyuan Su	GitHub
Ye Lu	GitHub, Google Scholar

License

See LICENSE for the Apache-2.0 license included with this checkpoint.

Citation

@misc{innospark3_2_27b_1102,
  title = {InnoSpark3.2-27B-1102},
  author = {Siyu Song and Shengyao Wang and Siyuan Su and Ye Lu},
  year = {2026}
}

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Safetensors

Model size

28B params

Tensor type

BF16

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