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

Libraries

How to use UT-IFML/LLaDA-8B-Instruct-PIG with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("UT-IFML/LLaDA-8B-Instruct-PIG", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UT-IFML/LLaDA-8B-Instruct-PIG with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/UT-IFML/LLaDA-8B-Instruct-PIG

SGLang

How to use UT-IFML/LLaDA-8B-Instruct-PIG 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 "UT-IFML/LLaDA-8B-Instruct-PIG" \
    --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": "UT-IFML/LLaDA-8B-Instruct-PIG",
		"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 "UT-IFML/LLaDA-8B-Instruct-PIG" \
        --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": "UT-IFML/LLaDA-8B-Instruct-PIG",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

LLaDA-8B-Instruct with PIG

LLaDA-8B-Instruct with Parallel Inconsistency Guidance (PIG), which lets the model commit more tokens per forward pass at the same accuracy. This repository is the whole model: the backbone, a 2.2M-parameter PIG adapter and its calibration tables.

Paper: PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models, Fruytier, Xue, Shakkottai, Sanghavi (UT Austin), 2026; arXiv:2610.17375. Code: https://github.com/Quentin-Fruytier/PIG-DLM-release Companion: UT-IFML/Nemotron-Labs-Diffusion-3B-PIG

Use

Needs transformers 5.x and a GPU with 24 GB.

from transformers import AutoModel

model = AutoModel.from_pretrained("UT-IFML/LLaDA-8B-Instruct-PIG", trust_remote_code=True)
out = model.generate("Write a Python function that returns the n-th Fibonacci number.", mode="pig.int", phi=0.1)
print(out.text, out.tpf)          # tpf: tokens per forward pass

argument	what it does	default
`mode`	`"pig.int"`, `"pig.ext"`, or `"confidence"` (the Fast-dLLM baseline)	`"pig.int"`
`phi`	PIG.INT budget; larger is faster, smaller more accurate (paper range 0.015 to 3)	`0.1`
`theta`	PIG.EXT budget; same direction (paper range 0.125 to 3)	`0.5`
`floor`	PIG.EXT: never commit below this confidence	none
`nu`	baseline: commit everything above this confidence	`0.9`
`geometry`, `block`	which positions are candidates: `"block"`, `"sliding"` or `"full"`	`"block"`, `32`

Implementation details, every argument, and how the adapter and tables were fitted: AGENTS.md.

License

MIT. The backbone is LLaDA-8B-Instruct, released by its authors under the MIT License (NOTICE.md).

Citation

@misc{fruytier2026pig,
  title  = {{PIG}: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models},
  author = {Fruytier, Quentin and Xue, Anton and Shakkottai, Sanjay and Sanghavi, Sujay},
  year   = {2026},
  eprint = {2610.17375},
  archivePrefix = {arXiv},
  primaryClass = {cs.CL},
  url    = {https://arxiv.org/abs/2610.17375}
}

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Paper for UT-IFML/LLaDA-8B-Instruct-PIG

PIG: Parallel Inconsistency Guidance for Accelerating Diffusion Language Models

Paper • 2610.17375 • Published 3 days ago