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InnoSpark3.0-35B-260709

InnoSpark3.0-35B-260709 is an education-enhanced 35B model in the InnoSpark3.0 series. It is trained from the NEX 35B model family and evaluated against Qwen3.6-35B-A3B and Nex-N2-mini.

InnoSpark3.0-35B-260709 is designed for educational QA, teaching assistance, learning companionship, and classroom or homework scenarios, including explanation generation, scaffolded instruction, guided reasoning, and pedagogical strategy suggestions. We use both general and education-domain data during SFT, and further strengthen reasoning, educational QA, agentic capabilities, and instruction following through a multi-stage RL pipeline. We did not specifically optimize visual capabilities; vision-language metrics are reported for transparency.

Model Details

Training

The post-training pipeline contains two major stages:

1. Supervised Fine-Tuning (SFT): uses a mixture of general instruction data and education-domain data to strengthen the model's ability to answer pedagogical questions, explain concepts, and follow classroom-oriented instructions.
2. Multi-stage Reinforcement Learning (RL): improves reasoning, education-specific QA, agentic task solving, and instruction-following robustness through staged optimization.

Evaluation

All scores below are normalized to a 100-point scale. For EduBench, the original scores in the evaluation sheet are on a 10-point scale and are multiplied by 10 here. Bold values indicate the best score in each row. Missing results are shown as -.

General Benchmarks

Education Benchmarks

The education evaluation covers EduBench and Pedagogy-oriented evaluation settings. The table below reports the detailed EduBench and Pedagogy Benchmark Multilingual metrics provided in the evaluation sheet.

Usage

Transformers

import torch
from transformers import AutoProcessor, AutoModelForMultimodalLM

model_id = "sii-research/InnoSpark3.0-35B-260709"

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

messages = [
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "请用适合初中生的方式讲解一元二次方程的求根公式，并给出一个例题。",
            }
        ],
    }
]

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/InnoSpark3.0-35B-260709 --trust-remote-code

Intended Use

InnoSpark3.0-35B-260709 is intended for research and application development in education-focused AI scenarios, including:

• Concept explanation and step-by-step tutoring
• Educational QA and homework support
• Lesson planning and teaching material generation
• Student-facing dialogue agents
• Teacher-facing assistant workflows
• General instruction following, reasoning, and agent-style tasks

Limitations

Like other large language models, InnoSpark3.0-35B-260709 may generate inaccurate, incomplete, or biased content. Outputs in educational settings should be reviewed by qualified educators when used for high-stakes learning, assessment, or student guidance. The model should not be used as the sole source for factual verification, grading decisions, psychological counseling, medical advice, legal advice, or other safety-critical decisions.

Evaluation results may vary with prompt format, decoding parameters, evaluation implementation, and data version. Users should conduct additional evaluations before deploying the model in production or classroom environments.

Main Contributions

Citation

@misc{innospark3_35b_260709,
  title = {InnoSpark3.0-35B-260709},
  author = {SII Research},
  year = {2026},
  howpublished = {\url{https://huggingface.co/sii-research/InnoSpark3.0-35B-260709}}
}

Please also follow the citation and license requirements of the related base and reference models.