LLaDA2.0-flash-preview

LLaDA2.0-flash-preview is a diffusion language model featuring a 100BA6B Mixture-of-Experts (MoE) architecture. As an enhanced, instruction-tuned iteration of the LLaDA2.0 series, it is optimized for practical applications.

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Benchmark	Ling-flash-2.0	LLaDA2.0-mini-preview	LLaDA2.0-flash-preview
Average	79.93	66.59	77.03
Knowledge
MMLU	87.98	72.49	83.15
MMLU-PRO	76.84	49.22	66.16
CMMLU	86.59	67.53	79.64
C-EVAL	88.03	66.54	79.28
Reasoning
squad2.0	81.32	85.61	90.61
drop	88.32	79.49	88.17
korbench	68.96	37.26	53.28
Coding
CruxEval-O	82.75	61.88	74.50
mbpp	85.01	77.75	86.65
MultiPL-E	65.76	62.43	72.38
humaneval	85.98	80.49	88.41
Bigcodebench-Full	40.70	30.44	40.44
Math
GSM8K	95.45	89.01	95.75
math	96.1	73.50	83.52
Agent & Alignment
BFCL_Live	67.57	74.11	74.86
IFEval-strict -prompt	81.52	62.50	75.60

🚀 Performance Highlights

• Leading MoE Architecture: The open-source Mixture-of-Experts (MoE) diffusion large language model, pre-trained from scratch on approximately 20 trillion tokens.
• Efficient Inference: With 100 billion total parameters, only 6.1 billion are activated during inference. LLaDA2.0-flash-preview significantly reduces computational costs while outperforming open-source dense models of similar scale.
• Impressive Performance on Code & Complex Reasoning: Excels in tasks such as code generation and advanced mathematical reasoning, demonstrating strong reasoning capabilities.
• Tool Use: Supports tool calling and achieves excellent performance in complex agent-based tasks.
• Open & Extensible: Fully open-source with commitment to transparency. We plan to release a leading inference framework in the future and continue investing in cutting-edge areas like diffusion LLMs (dLLM) to drive disruptive innovation.

🗺️ What's Next

• Supercharged Reasoning with LLaDA 2.0: LLaDA 2.0 series will be fine-tuned with Reinforcement Learning, unlocking a new level of sophisticated reasoning and problem-solving abilities.
• Tools for Innovators: we will release a detailed tutorial and our complete post-training framework. Whether you want to master the current model or build your own customized versions, you'll have the tools you need. Stay tuned

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📦 Model Variants

Model ID	Description	Hugging Face Link
inclusionAI/LLaDA2.0-mini-preview	Instruction-tuned model, ready for downstream applications.	🤗 Model Card
inclusionAI/LLaDA2.0-flash-preview	Instruction-tuned model, ready for downstream applications.	🤗 Model Card

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🔍 Model Overview

LLaDA2.0-flash-preview has the following specifications:

• Type: Mixture-of-Experts (MoE) Diffusion Language Model
• Total Parameters (Non-Embedding): 100B
• Number of Layers: 32
• Attention Heads: 32
• Context Length: 4,096 tokens
• Position Embedding: Rotary (RoPE)
• Vocabulary Size: 157,184

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🤗 Hugging Face Transformers

Make sure you have transformers and its dependencies installed:

import torch
import torch.nn.functional as F
from transformers import AutoModelForCausalLM
from transformers import AutoTokenizer

model_path = "/path/to/LLaDA2.0-mini-preview"
device = "auto"
model = AutoModelForCausalLM.from_pretrained(
    model_path, trust_remote_code=True, device_map=device
)
model = model.to(torch.bfloat16)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)

prompt = "Why does Camus think that Sisyphus is happy?"
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    tokenize=True,
    return_tensors="pt",
)
generated_tokens = model.generate(
    inputs=input_ids,
    eos_early_stop=True,
    gen_length=512,
    block_length=32,
    steps=32,
    temperature=0.0,
)
generated_answer = tokenizer.decode(
    generated_tokens[0],
    skip_special_tokens=True,
)
print(generated_answer)

Best Practices

To achieve optimal performance, we recommend the following settings:

1. Sampling Parameters: We suggest using Temperature=0.0, block_length=32, and steps=32. Using a higher temperature value may occasionally result in language mixing and a slight decrease in model performance.

2. Adequate Output Length: We recommend using an output length of 2048 tokens for most queries. For benchmarking on problems require more output length, such as those found in math and programming competitions, we suggest setting the max output length to 4096 tokens.

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🌐 License

This project is licensed under the terms of the Apache License 2.0.

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🤝 Contact & Collaboration

For questions, collaborations, or feedback, please reach out via Hugging Face or open an issue in the repository.

👉 Join us in advancing open, efficient, and intelligent language models!