LLaDA2.2-flash

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What LLaDA2.2-flash is

LLaDA2.2-flash LLaDA2.2-flash is an agent-oriented diffusion language model in the LLaDA2 series. By introducing Levenshtein Editing (with DELETE and INSERT control tokens) to diffusion language modeling, it represents the LLaDA2 series' first step in agentic applications, including long-context tool use, multi-turn interaction, and robust error correction.For more information, please refer to our technical report. 📊 Benchmarks The following tables compare LLaDA2.2-flash and Ling-2.6-flash in terms of agentic benchmark scores and throughput (TPS). Agentic benchmark scores LLaDA2.2-flash evaluation setup: The SWE-bench series was evaluated using the Claude Code scaffold. Across all benchmarks, we used a 128K context window with temperature=1.0, blocklength=32, threshold=0.5, and editingthreshold=0.0. Each score represents the average of five runs. Throughput (TPS) Ling-2.6-flash evaluation setup: MTP was enabled with 4 draft tokens. More results will be released in the upcoming technical report. 🚀 Highlights + Efficient 128K Diffusion Infrastructure: LLaDA2.2-flash extends the context window to 128K and introduces Block Routing, which bounds MoE expert activation at the diffusion-block level to enable efficient long-context agentic workloads. + Levenshtein Editing: We introduces DELETE and INSERT control tokens, allowing diffusion decoding to edit sequence structure, remove redundant content, and create insertion slots during parallel generation. + Agentic Reinforcement Learning: We propose Levenshtein Editing ELBO-based Block-level Policy Optimization (L-EBPO), which leverages agentic environmental rewards to train levenshtein editing and error correction in multi-turn tool-use scenarios. 📦 Model Variants 🔍 Model Overview LLaDA2.2-flash has the following specifications: + Type: Mixture-of-Experts (MoE) Diffusion Language Model with Levenshtein Editing + Context Length: 128K tokens + Levenshtein Editing Control Tokens: DELETE, INSERT + Total Parameters (Non-Embedding): 100B + Number of Layers: 32 + Attention Heads: 32 + Positional Encoding: Rotary Position Embedding (RoPE) + Vocabulary Size: 157,184 🤗 Hugging Face Transformers Make sure you have transformers and

Summarised from the published model card. Read the full card on the HuggingFace links below.

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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

Call it like any OpenAI endpoint

Once AxForge deploys llada2-2-flash for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (llada2-2-flash 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":"llada2-2-flash","messages":[{"role":"user","content":"Hello"}]}'

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