**Intern-Decision-0.8B** is a multimodal structured decision model fine-tuned from **Qwen3.5-0.8B**. It accepts a shared state, a schema of named questions, and optional images, and returns an answer distribution for every question in one model forward pass. 1. Preserve the question and option order, and map each question's options to single-token symbols `A`, `B`, …, `Z`, `a`, …, `z`, `0`, …, `9`. 2. Render the original system prompt, state, decision schema, and a complete assistant JSON skeleton with one ` ` placeholder per field. Preserve the checkpoint's chat template and empty thinking block. 3. Run one causal Hugging Face forward pass. For the masked-next-token decision objective, read logits at the position **immediately before each placeholder**. 4. Take a softmax over only that field's allowed candidate-symbol logits, then apply the checkpoint's probability calibration. 5. Map symbols back to the original option values and return typed JSON answers. This API performs structured candidate scoring. It does not call `generate()` or sample free-form text. A request can contain multiple fields; no gold answers are inserted into the prompt. The inference compiler uses only `state`, `questions`, and optional `images`.

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Intern-Decision-2B

internlm

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Intern-Decision-4B

internlm

**Intern-Decision-4B** is a multimodal structured decision model fine-tuned from **Qwen3.5-4B**. It accepts a shared state, a schema of named questions, and optional images, and returns an answer distribution for every question in one model forward pass. 1. Preserve the question and option order, and map each question's options to single-token symbols `A`, `B`, …, `Z`, `a`, …, `z`, `0`, …, `9`. 2. Render the original system prompt, state, decision schema, and a complete assistant JSON skeleton with one ` ` placeholder per field. Preserve the checkpoint's chat template and empty thinking block. 3. Run one causal Hugging Face forward pass. For the masked-next-token decision objective, read logits at the position **immediately before each placeholder**. 4. Take a softmax over only that field's allowed candidate-symbol logits, then apply the checkpoint's probability calibration. 5. Map symbols back to the original option values and return typed JSON answers. This API performs structured candidate scoring. It does not call `generate()` or sample free-form text. A request can contain multiple fields; no gold answers are inserted into the prompt. The inference compiler uses only `state`, `questions`, and optional `images`.

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Intern-MemDec-4B

internlm

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Intern-S1

internlm

We introduce **Intern-S1**, our **most advanced open-source multimodal reasoning model** to date. Intern-S1 combines **strong general-task capabilities with state-of-the-art performance on a wide range of scientific tasks**, rivaling leading closed-source commercial models. Built upon a 235B MoE language model (Qwen3) and a 6B Vision encoder (InternViT), Intern-S1 has been further pretrained on **5 trillion tokens** of multimodal data, including over **2.5 trillion scientific-domain tokens**. This enables the model to retain strong general capabilities while excelling in specialized scientific domains such as **interpreting chemical structures, understanding protein sequences, and planning compound synthesis routes**, making Intern-S1 to be a capable research assistant for real-world scientific applications. Features We evaluate the Intern-S1 on various benchmarks including general datasets and scientifc datasets. We report the performance comparsion with the recent VLMs and LLMs below. Benchmarks Intern-S1 InternVL3-78B Qwen2.5-VL-72B DS-R1-0528 Qwen3-235B-A22B Kimi-K2-Instruct Gemini-2.5 Pro o3 Grok-4

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Intern-S1-FP8

internlm

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Intern-S1-GGUF

internlm

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Intern-S1-mini

internlm

We introduce **Intern-S1-mini**, a lightweight open-source multimodal reasoning model based on the same techniques as **Intern-S1**. Built upon an 8B dense language model (Qwen3) and a 0.3B Vision encoder (InternViT), Intern-S1-mini has been further pretrained on **5 trillion tokens** of multimodal data, including over **2.5 trillion scientific-domain tokens**. This enables the model to retain strong general capabilities while excelling in specialized scientific domains such as **interpreting chemical structures, understanding protein sequences, and planning compound synthesis routes**, making Intern-S1-mini to be a capable research assistant for real-world scientific applications. We evaluate the Intern-S1-mini on various benchmarks including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below. You can utilize one of the following LLM inference frameworks to create an OpenAI compatible server:

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