Instructions to use GSAI-ML/LLaDA-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use GSAI-ML/LLaDA-8B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="GSAI-ML/LLaDA-8B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("GSAI-ML/LLaDA-8B-Instruct", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use GSAI-ML/LLaDA-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GSAI-ML/LLaDA-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GSAI-ML/LLaDA-8B-Instruct

SGLang

How to use GSAI-ML/LLaDA-8B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "GSAI-ML/LLaDA-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "GSAI-ML/LLaDA-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GSAI-ML/LLaDA-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use GSAI-ML/LLaDA-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/GSAI-ML/LLaDA-8B-Instruct
```

diffuse-cpp: C++ inference engine for LLaDA on CPU (GGUF format, Q4_K_M quantization)

#17

by Carmenest - opened Mar 20

Discussion

Carmenest

Mar 20

Hi @GSAI-ML team,

We've built diffuse-cpp, the first C++ inference engine for LLaDA, using the GGML tensor library (same foundation as llama.cpp).

What it does:

Runs LLaDA-8B-Instruct on CPU only — no GPU required
Supports F16, Q8_0, and Q4_K_M quantization via GGUF format
Includes a SafeTensors → GGUF converter for your model
Entropy-exit adaptive scheduling: reduces steps from 16 to 3–4 on easy prompts

Results (AMD EPYC 12-core, Q4_K_M):

9–11 tok/s on factual prompts with entropy-exit
7.4× thread scaling (near-linear up to physical core count)
Outperforms llama.cpp (8.51 tok/s with Llama-3-8B) on easy prompts

Pre-quantized models available:
https://huggingface.co/diffuse-cpp/LLaDA-8B-Instruct-GGUF

Engine source:
https://github.com/iafiscal1212/diffuse-cpp

We've also launched a Kaggle hackathon to benchmark across diverse hardware:
https://www.kaggle.com/competitions/cpu-inference-challenge-diffusion-vs-autoregressive-on-your-hardware

The key finding is that diffusion models have a computational advantage on CPUs due to the memory-compute regime inversion. We'd love feedback from the LLaDA team on potential optimizations.

Paper with full methodology: https://doi.org/10.5281/zenodo.19128920

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