Text Generation
Transformers
Safetensors
Chinese
English
qwen3_5
image-text-to-text
paradoxgpt
designer
sft
supervised-fine-tuning
scientific-writing
research-agents
qwen
conversational
Eval Results (legacy)
Instructions to use bhxdianzhang/ParaDesigner-SFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use bhxdianzhang/ParaDesigner-SFT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="bhxdianzhang/ParaDesigner-SFT") 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("bhxdianzhang/ParaDesigner-SFT") model = AutoModelForMultimodalLM.from_pretrained("bhxdianzhang/ParaDesigner-SFT", 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 bhxdianzhang/ParaDesigner-SFT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "bhxdianzhang/ParaDesigner-SFT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bhxdianzhang/ParaDesigner-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/bhxdianzhang/ParaDesigner-SFT
- SGLang
How to use bhxdianzhang/ParaDesigner-SFT 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 "bhxdianzhang/ParaDesigner-SFT" \ --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": "bhxdianzhang/ParaDesigner-SFT", "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 "bhxdianzhang/ParaDesigner-SFT" \ --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": "bhxdianzhang/ParaDesigner-SFT", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use bhxdianzhang/ParaDesigner-SFT with Docker Model Runner:
docker model run hf.co/bhxdianzhang/ParaDesigner-SFT
Add concrete designer model card example
Browse files
README.md
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任务: ...
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## Limitations
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- The model is optimized for a narrow ParadoxGPT specialist workflow, not general chat.
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为了获得最佳效果,请使用与训练数据一致的丰富论文上下文,例如 title、abstract、introduction、正文关键片段、figure/table captions、claims、reviews 或具体任务所需的 paper context。
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## 局限
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- 模型针对 ParadoxGPT 窄域专家工作流优化,不是通用聊天模型。
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任务: ...
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```
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### Concrete Example
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Input:
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```text
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你是顶会论文实验论证规划专家。给定论文设定、方法组件和 claims,请用中文设计完整的实验论证方案。
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论文标题: Adaptive Evidence Routing for Retrieval-Augmented Scientific QA
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摘要: We propose a router that chooses between dense retrieval, citation-graph retrieval, and a hybrid path for scientific QA. The paper claims the router improves factuality and citation precision.
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引言: Scientific questions require different evidence types. Definitions need canonical sources, comparison questions need related-work neighborhoods, and mechanistic questions need method sections.
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方法组件: evidence router, citation-graph retriever, reranker trained on paper-review pairs.
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Claims:
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1. The router is the main source of factuality gains.
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2. The citation graph improves citation precision.
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3. The hybrid path is especially useful for mechanistic questions.
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```
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Expected ParaDesigner-SFT-style output:
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```text
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这篇论文的实验论证不能只做一个 full system vs. base RAG。你真正要证明的是三个组件分别在什么地方起作用,所以实验应该分成三层。
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第一层是贡献隔离:保留 citation graph 和 reranker,只移除 router,观察 factuality 是否明显下降。否则你不能说提升主要来自 router,只能说来自整个 evidence stack。
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第二层是任务分解:把问题按 definition / comparison / mechanism 三类拆开。router 的价值应该体现在不同问题类型选择不同 evidence path,而不是只在平均分上变好。
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第三层是机制解释:报告 router 的选择分布和错误案例。如果 mechanistic questions 经常被路由到 hybrid path,并且这些样本的引用更完整,才说明 router 学到了有意义的证据策略。
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最关键的对照是:Base RAG、+citation graph、+reranker、+router,以及只替换 router 的 controlled ablation。这样 claim、组件和证据才能闭环。
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```
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## Limitations
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- The model is optimized for a narrow ParadoxGPT specialist workflow, not general chat.
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为了获得最佳效果,请使用与训练数据一致的丰富论文上下文,例如 title、abstract、introduction、正文关键片段、figure/table captions、claims、reviews 或具体任务所需的 paper context。
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### 具体示例
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输入:
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```text
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你是顶会论文实验论证规划专家。给定论文设定、方法组件和 claims,请用中文设计完整的实验论证方案。
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论文标题: Adaptive Evidence Routing for Retrieval-Augmented Scientific QA
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摘要: 论文提出一个 router,在 dense retrieval、citation-graph retrieval 和 hybrid path 之间选择证据路径,用于 scientific QA。作者声称 router 提升 factuality 和 citation precision。
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引言: 不同科学问题需要不同证据类型:定义问题需要 canonical source,对比问题需要 related-work neighborhood,机制问题需要 method section。
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方法组件: evidence router, citation-graph retriever, reranker trained on paper-review pairs.
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Claims:
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1. router 是 factuality 提升的主要来源。
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2. citation graph 提升 citation precision。
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3. hybrid path 对 mechanistic questions 尤其有用。
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```
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ParaDesigner-SFT 期望输出片段:
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```text
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这篇论文的实验论证不能只做 full system vs. base RAG。你真正要证明的是三个组件分别在什么地方起作用,所以实验应该分成三层。
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第一层是贡献隔离:保留 citation graph 和 reranker,只移除 router,观察 factuality 是否明显下降。否则不能说提升主要来自 router,只能说来自整个 evidence stack。
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+
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第二层是任务分解:把问题按 definition / comparison / mechanism 三类拆开。router 的价值应该体现在不同问题类型选择不同 evidence path,而不是只在平均分上变好。
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+
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第三层是机制解释:报告 router 的选择分布和错误案例。如果 mechanistic questions 经常被路由到 hybrid path,并且这些样本的引用更完整,才说明 router 学到了有意义的证据策略。
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```
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## 局限
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- 模型针对 ParadoxGPT 窄域专家工作流优化,不是通用聊天模型。
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