---
license: apache-2.0
tags:
- flow-matching
- continuous-latents
- math-reasoning
- qwen2.5
- block-diffusion
- non-autoregressive
- gsm8k
- chain-of-thought
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-Hooshaai%2FBlockDiffuse-yellow.svg)](https://huggingface.co/Hooshaai/BlockDiffuse)
[![HuggingFace Space](https://img.shields.io/badge/HF%20Space-BlockDiffuse--Blog-blueviolet.svg)](https://huggingface.co/spaces/Hooshaai/BlockDiffuse-Blog)

This repository hosts the complete suite of pre-tokenized reasoning datasets and continuous latent trajectory representations extracted from **`Qwen/Qwen2.5-0.5B-Instruct`** for training **BlockDiffuse** Diffusion Transformers via **Rectified Flow Matching**.

---

## 📑 Table of Contents
1. [Dataset Pipeline & Extraction Architecture](#1-dataset-pipeline--extraction-architecture)
2. [Dataset Files Manifest & Specifications](#2-dataset-files-manifest--specifications)
3. [Data Formats & Internal Tensor Keys](#3-data-formats--internal-tensor-keys)
4. [How to Load and Inspect with PyTorch](#4-how-to-load-and-inspect-with-pytorch)
5. [End-to-End Training Instructions](#5-end-to-end-training-instructions)
6. [Citation](#6-citation)

---

## 1. Dataset Pipeline & Extraction Architecture

Modern LLMs operate over discrete token vocabularies ($V = 151{,}936$). To train a Diffusion Transformer to denoise entire sequences simultaneously, BlockDiffuse maps prompts and target answers into continuous representation vectors:

```
Discrete Prompt Tokens (L_p) ──► Qwen2.5 (Layers 1..12) ──► Prompt Latents c [L_p x 896]
Discrete Target Tokens (100) ──► Qwen2.5 (Layers 1..12) ──► Target Latents z_1 [100 x 896]
```

By precomputing and persisting these continuous tensors to disk, BlockDiffuse eliminates redundant forward passes through the LLM during training, boosting training throughput by **> 12x** on single-GPU hardware.

---

## 2. Dataset Files Manifest & Specifications

| File Name | File Size | Description | Target Tasks | Samples Count |
| :--- | :--- | :--- | :--- | :--- |
| `reasoning_tokenized_qwen.pt` | **13.1 MB** | Pre-tokenized GSM8K & Math reasoning traces formatted using the Qwen2.5 ChatML format (`<\|im_start\|>system...user...assistant<\|im_end\|>`). | Token-level evaluation & tokenized baseline training | ~10,000 samples |
| `precomputed_reasoning_latents_qwen.pt` | **72.4 MB** | Validation subset of continuous target latents ($z_1 \in \mathbb{R}^{B \times 100 \times 896}$) and prompt conditionings ($c \in \mathbb{R}^{B \times L_p \times 896}$). | Rapid model validation & loss metric evaluation | 1,000 trajectories |
| `precomputed_real_qwen.pt` | **1,045.9 MB** | Intermediate-scale latent training dataset containing multi-turn mathematical reasoning trajectories. | Medium-scale training (1,000–5,000 steps) | 1,000 long traces |
| `precomputed_real_qwen_full.pt` | **2,360.3 MB** | Complete production-scale training set covering multi-step mathematical and algorithmic reasoning problems. | Full-scale training (20,000 steps) | Full GSM8K + Math traces |

---

## 3. Data Formats & Internal Tensor Keys

Each `.pt` file is a serialized Python dictionary with the following tensor schema:

```python
{
    "prompt_latents": torch.Tensor,   # Shape: [N, max_prompt_len, 896] (float32 / bfloat16)
    "target_latents": torch.Tensor,   # Shape: [N, 100, 896] (Target latents z_1 at Layer 12)
    "target_tokens": torch.Tensor,    # Shape: [N, 100] (Ground truth discrete token IDs for CE loss)
    "prompt_lens": torch.Tensor,      # Shape: [N] (Exact token length of each prompt prefix)
}
```

---

## 4. How to Load and Inspect with PyTorch

```python
import torch

# 1. Inspect Tokenized Sequences
tokenized = torch.load("reasoning_tokenized_qwen.pt", map_location="cpu")
print("Total tokenized entries:", len(tokenized["input_ids"]))
print("Sample input_ids shape:", tokenized["input_ids"][0].shape)

# 2. Inspect Continuous Latents
latents = torch.load("precomputed_reasoning_latents_qwen.pt", map_location="cpu")
print("Prompt latents shape:", latents["prompt_latents"].shape)  # [N, L_p, 896]
print("Target latents shape:", latents["target_latents"].shape)  # [N, 100, 896]
print("Target tokens shape:", latents["target_tokens"].shape)    # [N, 100]
```

---

## 5. End-to-End Training Instructions

To train a BlockDiffuse DiT model from scratch using these precomputed latents:

```bash
# 1. Clone official repository
git clone https://github.com/Hooshaai/BlockDiffuse.git
cd BlockDiffuse

# 2. Train with the full precomputed 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
```

---

## 6. 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}
}
```