tahamajs/BlockDiffuse-Data

📦 BlockDiffuse Precomputed Latents & Reasoning Datasets

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.

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🔬 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.

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📁 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"}

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🚀 How to Load and Use

1. Direct Python Loading via torch.load

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

# 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

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📐 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.

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📜 Citation

@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}
}