GFusion-10B-A1.8B

GFusion-10B-A1.8B is an experimental instruction-tuned diffusion language model trained by adapting GigaChat3-10B-A1.8B-base to block diffusion generation, followed by context extension, SFT, and confidence tuning.

GFusion uses a block size of 32 tokens and performs decoding with entropy-bounded sampling. In contrast to standard autoregressive generation, the model iteratively refines partially masked token blocks. This allows it to finalize multiple tokens in a single forward pass and provides a controllable trade-off between generation quality and decoding speed.

For architecture details, please refer to the GigaChat3-10B-A1.8B-base model card.

More details about GFusion are available in the Habr article.

Inference

Single-user (concurrency=1) decode throughput comparison using aiperf + sglang.

Benchmarks

Benchmark	GFusion 10B-A1.8B	GFusion + CT 10B-A1.8B	GigaChat3 10B-A1.8B	LLaDA-MoE 7B-A1.4B	LLaDA2.0-mini preview 16B-A1.4B
MMLU	73.38	73.09	71.20	67.18	72.49
MMLU-Pro	58.48	58.04	59.60	44.64	49.22
IFEval	70.38	71.22	66.55	59.33	62.50
GPQA	33.84	32.12	35.02	--	23.74
TruthfulQA	44.84	44.68	45.90	--	56.54
GSM8K	84.48	83.78	85.44	82.41	89.01
MGSM	78.80	79.20	76.80	--	81.44
MATH	68.08	66.86	70.00	58.68	73.50
MBPP+	67.20	65.81	63.60	--	66.67
HumanEval	75.00	71.34	72.56	61.59	80.49
HumanEval+	65.63	63.63	66.46	--	71.95
LCB-Lite	29.10	29.09	31.94	--	29.07
RUBQ	63.49	62.56	65.16	--	16.84
MMLU-RU	67.92	67.74	66.20	--	50.48
IFEval-RU	61.27	64.51	64.19	--	55.75

Quickstart

HF Transformers 🤗

from transformers import AutoTokenizer, AutoModelForCausalLM

device = "auto"
model_path = "ai-sage/GFusion-10B-A1.8B"

model = AutoModelForCausalLM.from_pretrained(
    model_path, device_map=device, trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
    model_path, device_map=device, trust_remote_code=True
)

messages = [
    {"role": "user", "content": "What are the KKT optimality conditions?"}
]
inputs = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    block_size=32,
    gamma=0.70
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

SGLang

GFusion support is available in SGLang PR #29776:

git clone https://github.com/sgl-project/sglang.git
cd sglang

git fetch origin refs/pull/29776/head:gfusion
git switch gfusion

python -m pip install --upgrade pip setuptools wheel
python -m pip install -e "python"

Create an EBSampling config file:

# eb_sampling.yaml
gamma: 0.15

Start the server with entropy-bounded sampling and FA3 attention:

python -m sglang.launch_server \
  --model-path ai-sage/GFusion-10B-A1.8B \
  --dllm-algorithm EBSampling \
  --dllm-algorithm-config eb_sampling.yaml \
  --attention-backend fa3 \
  --host 0.0.0.0 \
  --port 30000 \
  --dtype auto \
  --mem-fraction-static 0.88 \
  --cuda-graph-bs-decode 1

If FA3 is not available in your environment, use the Triton backend instead:

python -m sglang.launch_server \
  --model-path ai-sage/GFusion-10B-A1.8B \
  --dllm-algorithm EBSampling \
  --dllm-algorithm-config eb_sampling.yaml \
  --attention-backend triton \
  --host 0.0.0.0 \
  --port 30000 \
  --dtype auto \
  --mem-fraction-static 0.88

Example request for the instruct model:

curl http://localhost:30000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ai-sage/GFusion-10B-A1.8B",
    "temperature": 0,
    "max_tokens": 512,
    "messages": [
      {
        "role": "user",
        "content": "What are the KKT optimality conditions?"
      }
    ]
  }'