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-PIG / README.md

qfruytier

Model card: link the blog post

dd26130 verified 11 days ago

preview code

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---
license: mit
base_model: GSAI-ML/LLaDA-8B-Instruct
base_model_relation: adapter
pipeline_tag: text-generation
library_name: transformers
inference: false
language:
- en
tags:
- diffusion-language-model
- masked-diffusion
- parallel-decoding
- llada
- pig
---
# LLaDA-8B-Instruct with PIG

[LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/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](https://arxiv.org/abs/2610.17375). **Code:** https://github.com/Quentin-Fruytier/PIG-DLM-release
**Blog:** [Which tokens can a diffusion language model commit together?](https://quentin-fruytier.github.io/blog/pig-parallel-inconsistency-guidance/)
**Companion:** [UT-IFML/Nemotron-Labs-Diffusion-3B-PIG](https://huggingface.co/UT-IFML/Nemotron-Labs-Diffusion-3B-PIG)

## Use

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

```python
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](AGENTS.md).

## License

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

## Citation

```bibtex
@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}
}
```

	---
	license: mit
	base_model: GSAI-ML/LLaDA-8B-Instruct
	base_model_relation: adapter
	pipeline_tag: text-generation
	library_name: transformers
	inference: false
	language:
	- en
	tags:
	- diffusion-language-model
	- masked-diffusion
	- parallel-decoding
	- llada
	- pig
	---

	# LLaDA-8B-Instruct with PIG

	[LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/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](https://arxiv.org/abs/2610.17375). Code: https://github.com/Quentin-Fruytier/PIG-DLM-release
	Blog: [Which tokens can a diffusion language model commit together?](https://quentin-fruytier.github.io/blog/pig-parallel-inconsistency-guidance/)
	Companion: [UT-IFML/Nemotron-Labs-Diffusion-3B-PIG](https://huggingface.co/UT-IFML/Nemotron-Labs-Diffusion-3B-PIG)

	## Use

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

	```python
	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](AGENTS.md).

	## License

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

	## Citation

	```bibtex
	@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}
	}
	```