Text Generation
Transformers
Safetensors
English
pig
feature-extraction
diffusion-language-model
masked-diffusion
parallel-decoding
llada
conversational
custom_code
Instructions to use UT-IFML/LLaDA-8B-Instruct-PIG with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Commit History
2 days ago
-
Combined model: backbone + PIG adapter + tables, transformers 5.x; arXiv 2610.17375 (PIG-DLM-release 085b176)
Verified signature6b9e4ee
3 days ago
-
Single modeling file, tables inside model.safetensors, short card + AGENTS.md (PIG-DLM-release baf2ba5)
Verified signature93cd68a
4 days ago
-
generate(): window is a PIG.EXT knob; passing it with another mode now raises
Verified signature10a11c7 -
Cards: the Nemotron backbone is the instruct model (plain NVIDIA id; base is -3B-Base)
Verified signature9b5295d -
transformers wrapper: AutoModel.from_pretrained + generate(mode=pig.int|pig.ext|confidence); vendored from PIG-DLM-release
Verified signaturea0c86a1 -
PIG adapter, calibration tables and model card (arXiv v1 release)
Verified signaturedcf5ba4 -
initial commit
Verified signature24391bb