--- title: Logics-PPT-Qwen3.6-27B-SFT canonical_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT" md_url: "https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT.md" repository: Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT last_updated: 2026-09-21 license: apache-2.0 model_type: - qwen3_5 architectures: - Qwen3_5ForConditionalGeneration base_model: - Qwen/Qwen3.6-27B base_model_relation: finetune parameters: 27.4B tensor_type: - BF16 library_name: - pytorch - transformer - safetensors frameworks: - pytorch downloads: 164 stars: 0 tags: - presentation-generation - html-generation - slide-generation - code-generation - supervised-fine-tuning - agentic-post-training - multimodal-layout - openclaw --- # Logics-PPT-Qwen3.6-27B-SFT > Logics-PPT-Qwen3.6-27B-SFT - An open-source model by Alibaba-DT on ModelScope. Logics-PPT-Qwen3.6-27B-SFT Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT is a 27.4B-parameter machine learning model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3.6-27B. - **Repository**: Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT - **License**: apache-2.0 - **Parameters**: 27.4B - **Base model**: Qwen/Qwen3.6-27B - **Tags**: presentation-generation, html-generation, slide-generation, code-generation, supervised-fine-tuning, agentic-post-training, multimodal-layout, openclaw - **Downloads**: 164 - **Stars**: 0 - **Last updated**: 2026-09-21 Source: https://www.modelscope.ai/models/Alibaba-DT/Logics-PPT-Qwen3.6-27B-SFT --- # Logics-PPT-Qwen3.6-27B-SFT > Model card for Logics-PPT-Qwen3.6-27B-SFT. The technical report is under review and a public link will be added when available. ## 📰 News - **[2026.09.20]** Released **Logics-PPT-Qwen3.6-27B-SFT** under the Apache-2.0 license. - This model is the supervised fine-tuning (SFT) checkpoint for HTML-based presentation generation in our presentation-intelligence training pipeline. - The technical report is currently under review. A citation link will be added after public release. ## 🔎 Overview **Logics-PPT-Qwen3.6-27B-SFT** is a 27B-parameter model for automatic presentation generation. It is built on top of **Qwen3.6-27B** and trained for **single-page HTML slide generation** within the **OpenClaw** agent framework. Our work targets **end-to-end presentation generation** rather than next-slide completion. The model is trained to generate one self-contained HTML file per slide, which is then rendered into final visual outputs. We adopt **Single-HTML** as the generation paradigm because it is more robust and easier to optimize than multi-page HTML and Python-based alternatives in our study. The training pipeline behind this model contains three stages: 1. **Cross-validated data synthesis** for presentation-generation tasks and instance-level rubrics. 2. **Guided supervised fine-tuning (SFT)** with mixed-teacher trajectory distillation. 3. **Reinforcement learning (RL)** with verifiable visual rewards. This release corresponds to **Stage 2**, i.e., the final **SFT checkpoint before RL**. ### What makes this SFT model different? The model is trained with a supervision pipeline designed specifically for long-horizon PPT generation: - **Cross-validated synthetic data** with explicit control over difficulty and validity. - **Mixed-teacher supervision** combining: - **Qwen-3.8-Max** for broad coding and agentic reasoning capability. - **GLM-5.2** for presentation-specific structuring priors. - **Guided teacher enhancement** with progressively stronger prompts. - **Lightweight outcome-driven filtering** to remove low-quality trajectories such as rendering failures, layout overflow, and content overlap. This model is intended to improve both: - **content quality** of generated presentations, and - **basic visual organization** before reward-based aesthetic refinement. ## 🧠 Training Recipe ### Stage 1: Cross-validated data synthesis We synthesize PPT-generation training instances using a five-stage pipeline: - source profiling - task construction - rubric generation - principle audit - rubric tightening ### Stage 2: Guided SFT with mixed teachers For SFT, we distill presentation-generation trajectories from: - **Qwen-3.8-Max** - **GLM-5.2** We use guided prompts with increasing strength: - **Base** - **Pro** - **Max** and combine teacher corpora under different mixing strategies. The released 27B SFT model corresponds to the selected filtered mixed-teacher checkpoint used to initialize RL in the paper. Selected setting for this release: - **Target backbone:** Qwen-3.6-27B - **Training regime:** mixed + filter - **Selected teacher composition:** `qwen + glm_pro + glm_max (filter)` ## 📊 Experimental Results ### Main SFT results Reported overall benchmark scores from the paper: | Model | PresentBench | GDPVal (PPT subset) | | --- | ---: | ---: | | Qwen3.6-27B (base / no train) | 70.45 | 74.49 | | **Logics-PPT-Qwen3.6-27B-SFT** | **77.57** | **79.36** | Compared with the base model, this SFT checkpoint improves: - **PresentBench:** **70.45 → 77.57** (**+7.12**) - **GDPVal (PPT subset):** **74.49 → 79.36** (**+4.87**) The paper also reports that another mixed-teacher setting reaches the highest PresentBench score of **77.90**, while this released checkpoint is the one selected for subsequent RL because it preserves stronger external generalization. ### Aesthetic metric snapshot The paper also reports rule-based aesthetic scores for the selected SFT checkpoint: | Metric group | PresentBench | GDPVal (PPT subset) | | --- | ---: | ---: | | Aspect ratio | 1.00 | 0.70 | | Whitespace | 0.88 | 0.94 | | Element collision avoidance | 0.74 | 0.57 | | Visual balance | 0.57 | 0.44 | | Font readability | 0.95 | 0.70 | | **Total** | **4.14** | **3.35** | ## 🎯 Intended Use This model is intended for **research use** in: - automatic presentation generation - HTML-based slide generation - agentic document-to-deck generation - long-horizon multimodal/code generation - supervised post-training for presentation intelligence Typical usage involves a system that: - reads task instructions and reference materials, - plans a slide deck, - generates one HTML file per slide, - renders the result in a browser-based pipeline. ## ⚠️ Limitations - This is an **SFT** model, not the final aesthetically refined RL checkpoint. - It improves presentation generation quality substantially, but some post-render visual defects may remain, such as: - excessive whitespace, - visual imbalance, - element overlap. - The model is optimized for **single-page HTML slide generation**, not native `.pptx` authoring. - Reliable deployment still requires the surrounding agent stack, rendering loop, and task prompt format. - This release is intended for research and evaluation, not as a guaranteed production presentation system. ## 🚀 Quickstart ### Transformers: text-generation smoke test ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Logics-MLLM/Logics-PPT-Qwen3.6-27B-SFT" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, dtype=torch.bfloat16, device_map="auto", ) model.eval() messages = [{ "role": "user", "content": ( "Create a 6-slide academic presentation as separate single-slide HTML files. " "The deck should summarize a research topic, maintain a consistent 16:9 layout, " "and keep each slide visually balanced and readable." ), }] inputs = tokenizer.apply_chat_template( messages, tokenize=True, add_generation_prompt=True, return_dict=True, return_tensors="pt", ).to(model.device) with torch.inference_mode(): output = model.generate( **inputs, max_new_tokens=2048, do_sample=True, temperature=1.0, top_p=0.95, top_k=20, ) completion = output[0, inputs["input_ids"].shape[-1]:] print(tokenizer.decode(completion, skip_special_tokens=True)) ``` This is only a minimal text-generation smoke test. Actual presentation generation in our setup additionally uses: - the OpenClaw-style agent framework, - task instruction files and reference materials, - browser rendering ## 📚 Citation The technical report is currently under review. Citation information will be added after public release. ## 🙏 Acknowledgements We thank the Qwen, GLM, OpenClaw, PresentBench, and GDPVal teams and the broader research communities working on agentic generation, presentation intelligence, and reinforcement learning for LLMs. ## License The model weights are released under the Apache License 2.0.