Model reference · open weights
LLaDA2.0-Uni is an open-weight language model from inclusionAI, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
[📑 Technical Report ]   [🌐 Github ]  [🤗 FP8 Version ] Model Capabilities LLaDA2.0-Uni is a unified diffusion Large Language Model (dLLM) based on Mixture-of-Experts (MoE) that seamlessly integrates multimodal understanding and generation within a single model. It supports: - 🖼️ Text-to-Image Generation — high-fidelity image synthesis with optional thinking/reasoning. - 🔍 Image Understanding — visual question answering, image captioning, document understanding, etc. - ✏️ Image Editing — instruction-based editing with single or multi-reference support. - 🎨 Interleaved Generation and Reasoning — provide preliminary support for interleaved generation and unlock advanced interleaved reasoning. - ⚡ Sprint Acceleration — KV cache reuse and adaptive unmasking for faster inference. Model Architecture - Unified dLLM-MoE Backbone: Unifies multimodal understanding and generation into a simple Mask Token Prediction paradigm. - Discrete Semantic Tokenizer: Utilizes SigLIP-VQ to convert visual inputs into discrete semantic tokens, significantly enhancing multimodal understanding. - Efficient Diffusion Decoder: Pairs discrete tokens with a specialized diffusion decoder for high-fidelity generation, enabling rapid 8-step inference via distillation. Evaluation Results Quick Start Note: Full installation instructions and CLI scripts are available in the GitHub repository. ⚙️ Installation 1. Create a conda environment 2. Install PyTorch (CUDA 12.4) 3. Install Flash Attention 2 (required for efficient inference) 4. Install remaining dependencies 🌟 Text-to-Image Generation [!Note] 💡 Faster decoding — Use the decoder-turbo (distilled decoder) for ~10× faster image decoding (8 steps instead of 50) with minimal quality loss: python image = decodevqtokens( result["tokenids"], result["h"], result["w"], modelpath, "cuda", numsteps=8, decodemode="decoder-turbo", ) 🌟 Text-to-Image Generation with Thinking 🌟 Image Understanding 🌟 Image Editing 🌟 SPRINT Acceleration SPRINT accelerates inference by combining KV cache reuse, adaptive unmasking, and threshold-based batch acceptance: - KV Cache Reuse & Pruning: The prefix KV cache is computed once during warmup steps, then optionally pru
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | inclusionAI |
|---|---|
| Type | Language models |
| Parameters (lead) | 16.3B |
| Context | 8k tokens |
| Variants | 2 |
| Runs with | transformers |
| Based on | inclusionAI/LLaDA2.0-mini |
| Released | 2026-04-22 |
| Popularity | 5k downloads / month |
| Likes | 250 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys llada2-0-uni for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llada2-0-uni below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"llada2-0-uni","messages":[{"role":"user","content":"Hello"}]}'
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗