FLARE-2B

Model overview

FLARE-2B is a 2.39B-parameter text-generation checkpoint produced by applying FLARE's autoregressive-to-diffusion conversion recipe to Qwen3.5-2B. A single set of weights supports AR-Trust verified decoding, Diffusion-Trust parallel denoising, and ordinary causal generation through the FLARE runtime.

Property	Value
Base model	Qwen3.5-2B
Parameters	2,390,384,448
Architecture	Hybrid Gated DeltaNet and softmax attention
Input / output	Text / text
Weight format	BF16 safetensors
Context configuration	262,144 positions
Vocabulary size	248,320
License	License

The native mask ID is 248077. It belongs to the padded model vocabulary and is intentionally not exposed as a tokenizer token.

Quick Start Guide

FLARE inference uses the SGLang and FlashInfer implementations bundled with the FLARE repository. The checkpoint uses a standard Hugging Face layout and is downloaded automatically by the serving runtime.

1. Install the runtime

git clone https://github.com/yuchen-zhu-zyc/FLARE.git
cd FLARE/eval

FLARE_CACHE_ROOT=/persistent/flare-cache \
  bash scripts/setup_eval_env.sh

The one-shot installer creates the evaluation environment and places reusable Hugging Face, FlashInfer, Triton, and extension caches below FLARE_CACHE_ROOT.

2. Start a server

Start one decoding mode. Each command below serves the same checkpoint and uses port 30000 unless PORT is overridden.

# AR-Trust: noisy-stream drafting with clean-stream verification.
scripts/serve.sh self-spec yzhu738/FLARE-2B

# Diffusion-Trust: parallel block denoising.
scripts/serve.sh diffusion yzhu738/FLARE-2B

# Causal reference: ordinary left-to-right decoding.
scripts/serve.sh causal yzhu738/FLARE-2B

To run more than one server, assign a different PORT and GPU to each process, for example PORT=30001 CUDA_VISIBLE_DEVICES=1 ....

3. Query the OpenAI-compatible API

curl http://localhost:30000/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "yzhu738/FLARE-2B",
    "messages": [{"role": "user", "content": "Solve: 17 × 23."}],
    "temperature": 1.0,
    "top_p": 0.95,
    "top_k": 20,
    "max_tokens": 512,
    "chat_template_kwargs": {"enable_thinking": false}
  }'

The installed environment also includes the OpenAI Python client:

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="yzhu738/FLARE-2B",
    messages=[{"role": "user", "content": "Solve: 17 × 23."}],
    temperature=1.0,
    top_p=0.95,
    max_tokens=512,
    extra_body={
        "top_k": 20,
        "chat_template_kwargs": {"enable_thinking": False},
    },
)
print(response.choices[0].message.content)

Decoding modes

Mode	Launcher	Released configuration	Description
AR-Trust	scripts/serve.sh self-spec	flare_self_spec_b7_g4.yaml	The noisy stream proposes multiple draft tokens and clean-stream logits verify them left-to-right.
Diffusion-Trust	scripts/serve.sh diffusion	flare_shift_b3_g1.yaml	A masked active block is denoised in parallel and committed after refinement.
Causal	scripts/serve.sh causal	Native causal decoding	Standard left-to-right generation from the clean stream.

AR-Trust uses speculation horizon N=4 and the Exact-Truncated verification policy. Diffusion-Trust uses runtime block_size: 3, which implements logical block size B=4 under FLARE's shifted decoding rule. See the evaluation guide for the full configuration and sampling interface.

The API example controls request-level sampling. The released AR-Trust proposal and verification law uses temperature 1.0, top-k 50, and top-p 0.95; Diffusion-Trust uses threshold 0.95, temperature 1.0, top-k 20, and top-p 0.95 in its algorithm configuration.

Benchmark results

The FLARE, SDAR, and Qwen3.5 values come from Table 2 of the FLARE paper; the LLaDA-mini values come from Table 1. FLARE's AR-Trust and Diffusion-Trust columns use the same checkpoint. Bold marks the best open-source diffusion LLM result in each row; * denotes a result identified in the paper as potentially under-reported. — means the source did not report that result.

Benchmark	SDAR 1.7B	LLaDA-2.0 mini	LLaDA-2.1 mini	FLARE-2B AR-Trust	FLARE-2B Diffusion-Trust	Qwen3.5-2B AR
Knowledge & Instruction Following
ARC-Challenge	85.4	93.56	—	85.07	85.84	92.15
MMLU	62.9	80.53	—	67.60	64.14	73.59
MMLU-Pro	37.0	63.22	63.42	53.57	53.63	59.53
GPQA-Diamond	29.8	47.98	48.36	37.37	35.35	62.12
IFEval	43.4	80.78	81.33	68.95	62.66	79.48
Math
GSM8K	80.1	94.24	—	84.46	82.79	77.63*
MATH-500	63.2	—	—	84.40	82.20	72.20*
AIME-24	10.0	—	—	31.11	31.11	8.89*
AIME-25	2.1	36.67	36.67	26.67	26.67	12.22*
Code
HumanEval	61.6	86.59	—	64.02	50.61*	48.17
MBPP	61.1	81.50	—	68.09	55.25*	53.31
LiveCodeBench v6	5.7	31.50	28.85	15.43	9.71*	17.71

Qwen3.5-2B is the autoregressive source checkpoint and is shown as a capability-retention reference; it is not included when selecting the bolded diffusion-LLM result.

Reproduce evaluation

With a server running on port 30000, launch the complete evaluation suite from the repository's eval directory:

PORTS=30000 \
TASKS="gsm8k math500 ifeval humaneval mbpp arc_c gpqa aime2024 aime2025 mmlu mmlu_pro lcb" \
  scripts/evaluate.sh

The evaluator writes generations, scores, executed commands, and a run manifest below FLARE_CACHE_ROOT. See the evaluation README for task-specific requirements and full-evaluation settings.

Citation

@article{zhu2026flare,
  title   = {FLARE: Diffusion for Hybrid Language Model},
  author  = {Yuchen Zhu and Jing Shi and Chongjian Ge and Hao Tan and
             Yiran Xu and Wanrong Zhu and Jason Kuen and Koustava Goswami and
             Rajiv Jain and Yongxin Chen and Molei Tao and Jiuxiang Gu},
  journal = {arXiv preprint arXiv:2606.01774},
  year    = {2026}
}

Acknowledgements

FLARE-2B is derived from Qwen3.5-2B. The serving stack builds on SGLang and FlashInfer.