Deploy complete Dataset Card

README.md

@@ -0,0 +1,102 @@
+---
+license: apache-2.0
+tags:
+- flow-matching
+- continuous-latents
+- math-reasoning
+- qwen2.5
+- block-diffusion
+- non-autoregressive
+size_categories:
+- 10K<n<100K
+task_categories:
+- text-generation
+language:
+- en
+---
+
+# 📦 BlockDiffuse Precomputed Latents & Reasoning Datasets
+
+[![License: Apache 2.0](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
+[![Base Model](https://img.shields.io/badge/Base%20LLM-Qwen2.5--0.5B--Instruct-green.svg)](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
+[![HuggingFace Model](https://img.shields.io/badge/HF%20Model-tahamajs%2FBlockDiffuse-yellow.svg)](https://huggingface.co/tahamajs/BlockDiffuse)
+[![HuggingFace Space](https://img.shields.io/badge/HF%20Space-BlockDiffuse--Blog-blueviolet.svg)](https://huggingface.co/spaces/tahamajs/BlockDiffuse-Blog)
+
+This repository contains the complete pre-extracted dataset files used to train **BlockDiffuse** Diffusion Transformers to generate **100-token blocks in continuous latent space** using **Rectified Flow Matching**.
+
+---
+
+## 🔬 Dataset Overview & Extraction Pipeline
+
+Standard language models operate over discrete token vocabularies ($V \approx 151{,}936$). To bypass sequential autoregressive decoding, BlockDiffuse maps prompts and target answer sequences into continuous latent vectors:
+
+```
+Discrete Prompt Tokens (L_p) ──► Qwen2.5-0.5B (Layer 12) ──► Prompt Latents c [L_p x 896]
+Discrete Target Tokens (100) ──► Qwen2.5-0.5B (Layer 12) ──► Target Latents z_1 [100 x 896]
+```
+
+These precomputed continuous tensors allow training the Diffusion Transformer directly on latent trajectory matching without re-computing LLM forward passes on every iteration, accelerating training throughput by **> 12x**.
+
+---
+
+## 📁 File Manifest & Specifications
+
+| File Name | File Size | Description | Shape / Keys |
+| :--- | :--- | :--- | :--- |
+| `reasoning_tokenized_qwen.pt` | **12.5 MB** | Pre-tokenized GSM8K & Math reasoning conversations formatted with the Qwen2.5 ChatML template (`<\|im_start\|>...<\|im_end\|>`). | `input_ids`, `attention_mask`, `labels` |
+| `precomputed_reasoning_latents_qwen.pt` | **69.1 MB** | Validation subset of continuous target latents ($z_1$) and prompt conditionings ($c$) extracted from Layer 12 of Qwen2.5. | `{"prompt_latents": [N, L_p, 896], "target_latents": [N, 100, 896]}` |
+| `precomputed_real_qwen.pt` | **1,045.9 MB** | Intermediate-scale latent training dataset containing 1,000 multi-turn mathematical reasoning trajectories. | `{"prompt_latents", "target_latents", "target_tokens"}` |
+| `precomputed_real_qwen_full.pt` | **2,360.3 MB** | Complete production training dataset covering extensive multi-step reasoning problems. | `{"prompt_latents", "target_latents", "target_tokens"}` |
+
+---
+
+## 🚀 How to Load and Use
+
+### 1. Direct Python Loading via `torch.load`
+```python
+import torch
+
+# Load tokenized sequences
+tokenized_data = torch.load("reasoning_tokenized_qwen.pt", map_location="cpu")
+print("Tokenized sample count:", len(tokenized_data["input_ids"]))
+
+# Load precomputed continuous latents
+latents_data = torch.load("precomputed_reasoning_latents_qwen.pt", map_location="cpu")
+print("Prompt latents shape:", latents_data["prompt_latents"][0].shape)  # [L_p, 896]
+print("Target latents shape:", latents_data["target_latents"][0].shape)  # [100, 896]
+```
+
+### 2. Training BlockDiffuse DiT with this Dataset
+```bash
+# Clone official codebase
+git clone https://github.com/Hooshaai/BlockDiffuse.git
+cd BlockDiffuse
+
+# Train with the precomputed full dataset
+python train.py \
+    --config_train configs/gpu_full_capacity_improved.yaml \
+    --config_dit configs/gpu_full_capacity_improved.yaml \
+    --data_path ./data/precomputed_real_qwen_full.pt \
+    --max_steps 20000 \
+    --output_dir ./checkpoints_improved
+```
+
+---
+
+## 📐 Latent Space Normalization & Properties
+- **Dimensionality**: $d_{\text{model}} = 896$ per token position.
+- **Layer Origin**: Extracted after RMSNorm from Transformer Block 12 of `Qwen2.5-0.5B-Instruct`.
+- **Target Block Length**: Exactly 100 contiguous tokens. Shorter sequences are padded to 100 with EOS token latents; longer reasoning traces are chunked with rolling context propagation.
+
+---
+
+## 📜 Citation
+```bibtex
+@article{blockdiffuse2026,
+  title={BlockDiffuse: Fully Parallel Latent Space Reasoning Generation with Diffusion Transformers},
+  author={Hooshaai Research},
+  journal={GitHub / HuggingFace Technical Report},
+  year={2026},
+  url={https://github.com/Hooshaai/BlockDiffuse}
+}
+```