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Model: ScienceOne-AI/S1-Base-1.5-8B-128K Source: Original Platform
2026-09-06 13:15:13 +08:00
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--- a/README.md
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+# S1-Base-1.5-8B-128K
+
+[中文版](./README.md) ｜ [English](./README_en.md)
+
+## 模型介绍  
+
+本仓库为磐石 S1-Base-1.5-8B-128K 通用科学大语言模型，基于磐石科学基础大模型 [S1-Base](https://modelscope.cn/collections/S1-Base-66b70cf6e51c48) 经过后训练（SFT+GRPO）训练而来，该模型在保持模型科学推理能力的情况下，重点提升模型的超长上下文理解和推理能力，以及科研场景下的复杂指令遵循能力，本系列模型上下文长度为 128k。
+   
+## 模型权重  
+
+S1-Base-1.5-8B-128K 模型以 Apache 2.0 协议开源，您可以在我们的 [Huggingface](https://huggingface.co/ScienceOne-AI/S1-Base-1.5-8B-128K) 或 [ModelScope](https://modelscope.cn/models/ScienceOne-AI/S1-Base-1.5-8B-128K) 下载模型权重。
+
+| 模型名称     | Huggingface地址      | ModelScope地址          |  
+|-------------|-------------------------------------|-------------------------------------|  
+|S1-Base-1.5-8B-128K   | [点击下载](https://huggingface.co/ScienceOne-AI/S1-Base-1.5-8B-128K) | [点击下载](https://modelscope.cn/models/ScienceOne-AI/S1-Base-1.5-8B-128K) |  
+
+## 模型评测
+
+为全面验证 S1-Base-1.5-128K 的综合能力，我们针对模型的超长上下文能力、指令遵循能力、科学推理能力等三大核心能力进行了系统性评测，结果如下表所示。
+
+| 评测集 | S1-Base-1.5-8B-128K | S1-Base-8B | Qwen3-8B | Intern-S1-mini | GLM-Z1-9B-0414 |
+|---|---|---|---|---|---|
+| CLongEval | **36.18** | 27.51 | 33.62 | 32.82 | 25.71 |
+| InfiniteBench | **35.57** | 27.62 | 34.41 | 30.42 | 29.58 |
+| IFEval | **87.06** | 70.42 | 85.00 | 83.00 | 78.93 |
+| GPQA | **70.33** | 63.01 | 60.86 | 65.97 | 55.81 |
+| ChemBench | 61.59 | **62.74** | 57.79 | 57.54 | 55.85 |
+| LLM-MSE | 83.63 | **88.50** | 83.51 | 78.65 | 80.97 |
+| LAB bench | 37.54 | **37.63** | 26.52 | 29.11 | 29.89 |
+| AIME2024 | 77.92 | 75.42 | 74.60 | **85.00** | 79.37 |
+| LiveMathBench | **86.72** | 82.81 | 77.00 | **86.72** | 82.82 |
+
+**主要亮点：**
+- 📜 **长上下文推理能力提升**：模型在 CLongEval、InfiniteBench 等公开长文基准上领先基座及同等参数量模型，在面向论文、网页等真实场景的自建长文评测中提升显著。
+- 🎯 **复杂指令遵循能力提升**：构建涵盖文档理解、结构化生成、信息抽取、图表理解四大类任务的科学文献指令遵循任务体系，并结合长度、格式、内容等多维度约束，模型在 IFEval 等基准保持领先。
+- 🔬 **科学推理能力保持稳定**：模型在生物、物理、化学等综合科学能力评估基准 GPQA 优势显著，其他科学任务评估基准的性能未因上下文扩展而产生大幅波动，整体能力保持稳定。
+- 👍 **用户赞踩反馈数据飞轮**：结合 [ScienceOne](https://scienceone.cn) 平台用户点赞与点踩反馈，持续优化模型在真实场景下的表现和用户体验。
+
+
+## 部署方式
+   
+我们推荐使用 [vLLM](https://github.com/vllm-project/vllm) 部署 S1-Base，实现高效推理与 OpenAI 兼容的 API 服务。  
+   
+**快速启动命令示例：**  
+```bash  
+pip install vllm  
+vllm serve <your_s1_model_path> --served-model-name s1-base-1.5-8b-128k
+```  
+API 请求和响应格式与 OpenAI 基本一致，详细可参考 vLLM 官方文档。  
+
+**使用 OpenAI Python SDK 生成响应：**
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="")
+resp = client.chat.completions.create(
+    model="s1-base-1.5-8b-128k",
+    messages=[{"role": "user", "content": "你好"}]
+)
+print(resp.choices[0].message.content)
+```
+
+**使用 CURL 生成响应：**
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions -d '{"model": "s1-base-1.5-8b-128k", "messages":[{"role":"user", "content": "你好"}], "skip_special_tokens": false}' -H "Content-Type: application/json"
+```
--- a/README_en.md
+++ b/README_en.md
@@ -0,0 +1,67 @@
+# S1-Base-1.5-8B-128K
+
+[中文版](./README.md) ｜ [English](./README_en.md)
+
+## Model Introduction  
+
+This repository contains the S1-Base-1.5-8B-128K general scientific large language model, developed through post-training (SFT+GRPO) based on the scientific foundation model [S1-Base](https://modelscope.cn/collections/S1-Base-66b70cf6e51c48). This model maintains scientific reasoning capabilities while significantly enhancing long context understanding and reasoning abilities, as well as complex instruction following in scientific research scenarios. The model supports a context length of 128k.
+   
+## Model Weights  
+
+The S1-Base-1.5-8B-128K model is open-sourced under the Apache 2.0 license. You can download the model weights from our [Huggingface](https://huggingface.co/ScienceOne-AI/S1-Base-1.5-8B-128K) or [ModelScope](https://modelscope.cn/models/ScienceOne-AI/S1-Base-1.5-8B-128K).
+
+
+| Model Name     | Huggingface Link      | ModelScope Link          |  
+|-------------|-------------------------------------|-------------------------------------|  
+|S1-Base-1.5-8B-128K   | [Download](https://huggingface.co/ScienceOne-AI/S1-Base-1.5-8B-128K) | [Download](https://modelscope.cn/models/ScienceOne-AI/S1-Base-1.5-8B-128K) |  
+
+## Model Evaluation
+
+
+To comprehensively validate the capabilities of S1-Base-1.5-128K, we conducted systematic evaluations across three core competencies: long context ability, instruction following ability, and scientific reasoning ability. The results are shown in the table below.
+
+| Benchmark | S1-Base-1.5-8B-128K | S1-Base-8B | Qwen3-8B | Intern-S1-mini | GLM-Z1-9B-0414 |
+|---|---|---|---|---|---|
+| CLongEval | **36.18** | 27.51 | 33.62 | 32.82 | 25.71 |
+| InfiniteBench | **35.57** | 27.62 | 34.41 | 30.42 | 29.58 |
+| IFEval | **87.06** | 70.42 | 85.00 | 83.00 | 78.93 |
+| GPQA | **70.33** | 63.01 | 60.86 | 65.97 | 55.81 |
+| ChemBench | 61.59 | **62.74** | 57.79 | 57.54 | 55.85 |
+| LLM-MSE | 83.63 | **88.50** | 83.51 | 78.65 | 80.97 |
+| LAB bench | 37.54 | **37.63** | 26.52 | 29.11 | 29.89 |
+| AIME2024 | 77.92 | 75.42 | 74.60 | **85.00** | 79.37 |
+| LiveMathBench | **86.72** | 82.81 | 77.00 | **86.72** | 82.82 |
+
+**Key Highlights:**
+- 📜 **Enhanced Long Context Reasoning**: The model leads among base models and similar-sized models on public long-context benchmarks such as CLongEval and InfiniteBench, with significant improvements in custom long-text evaluations for real-world scenarios involving papers and web pages.
+- 🎯 **Improved Complex Instruction Following**: Built with a scientific literature instruction following task system covering four major categories—document understanding, structured generation, information extraction, and chart comprehension—combined with multi-dimensional constraints including length, format, and content. The model maintains leadership on benchmarks like IFEval.
+- 🔬 **Stable Scientific Reasoning Capability**: The model shows significant advantages on GPQA, a comprehensive scientific capability evaluation benchmark covering biology, physics, and chemistry. Performance on other scientific task evaluation benchmarks remains stable without significant fluctuations due to context expansion.
+- 👍 **User Feedback Data Flywheel**: Continuously optimizes model performance and user experience in real-world scenarios by incorporating user likes and dislikes feedback from the [ScienceOne](https://scienceone.cn) platform.
+
+## Deployment
+   
+We recommend using [vLLM](https://github.com/vllm-project/vllm) to deploy S1-Base for efficient inference and OpenAI-compatible API services.
+   
+**Quick start command example:**  
+```bash  
+pip install vllm  
+vllm serve <your_s1_model_path> --served-model-name s1-base-1.5-8b-128k
+```  
+The API request and response formats are basically consistent with OpenAI. Please refer to the official vLLM documentation for details.
+
+**Generate responses using OpenAI Python SDK:**
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="")
+resp = client.chat.completions.create(
+    model="s1-base-1.5-8b-128k",
+    messages=[{"role": "user", "content": "hi"}]
+)
+print(resp.choices[0].message.content)
+```
+
+**Generate responses using CURL:**
+```bash
+curl -X POST http://localhost:8000/v1/chat/completions -d '{"model": "s1-base-1.5-8b-128k", "messages":[{"role":"user", "content": "hi"}], "skip_special_tokens": false}' -H "Content-Type: application/json"
+```
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--- a/config.json
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+}
--- a/configuration.json
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+++ b/special_tokens_map.json
@@ -0,0 +1,31 @@
+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}
--- a/tokenizer.json
+++ b/tokenizer.json
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,240 @@
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+  ],
+  "bos_token": null,
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0].role == 'system' %}\n        {{- messages[0].content + '\\n\\n' }}\n    {%- endif %}\n    {{- \"# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0].role == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n    {%- set index = (messages|length - 1) - loop.index0 %}\n    {%- if ns.multi_step_tool and message.role == \"user\" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}\n        {%- set ns.multi_step_tool = false %}\n        {%- set ns.last_query_index = index %}\n    {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n    {%- if message.content is string %}\n        {%- set content = message.content %}\n    {%- else %}\n        {%- set content = '' %}\n    {%- endif %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n        {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {%- set reasoning_content = '' %}\n        {%- if message.reasoning_content is string %}\n            {%- set reasoning_content = message.reasoning_content %}\n        {%- else %}\n            {%- if '</think>' in content %}\n                {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n                {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n            {%- endif %}\n        {%- endif %}\n        {%- if loop.index0 > ns.last_query_index %}\n            {%- if loop.last or (not loop.last and reasoning_content) %}\n                {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n            {%- else %}\n                {{- '<|im_start|>' + message.role + '\\n' + content }}\n            {%- endif %}\n        {%- else %}\n            {{- '<|im_start|>' + message.role + '\\n' + content }}\n        {%- endif %}\n        {%- if message.tool_calls %}\n            {%- for tool_call in message.tool_calls %}\n                {%- if (loop.first and content) or (not loop.first) %}\n                    {{- '\\n' }}\n                {%- endif %}\n                {%- if tool_call.function %}\n                    {%- set tool_call = tool_call.function %}\n                {%- endif %}\n                {{- '<tool_call>\\n{\"name\": \"' }}\n                {{- tool_call.name }}\n                {{- '\", \"arguments\": ' }}\n                {%- if tool_call.arguments is string %}\n                    {{- tool_call.arguments }}\n                {%- else %}\n                    {{- tool_call.arguments | tojson }}\n                {%- endif %}\n                {{- '}\\n</tool_call>' }}\n            {%- endfor %}\n        {%- endif %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n    {%- if enable_thinking is defined and enable_thinking is false %}\n        {{- '<think>\\n\\n</think>\\n\\n' }}\n    {%- endif %}\n{%- endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}
--- a/vocab.json
+++ b/vocab.json
				`@@ -0,0 +1 @@`
				`{"framework":"Pytorch","task":"text-generation"}`