Text Generation
Transformers
Safetensors
llada2_moe
dllm
diffusion
llm
text_generation
conversational
custom_code
Instructions to use inclusionAI/LLaDA2.2-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.2-flash with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.2-flash", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("inclusionAI/LLaDA2.2-flash", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use inclusionAI/LLaDA2.2-flash with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/LLaDA2.2-flash" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "inclusionAI/LLaDA2.2-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/LLaDA2.2-flash
- SGLang
How to use inclusionAI/LLaDA2.2-flash 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 "inclusionAI/LLaDA2.2-flash" \ --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": "inclusionAI/LLaDA2.2-flash", "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 "inclusionAI/LLaDA2.2-flash" \ --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": "inclusionAI/LLaDA2.2-flash", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/LLaDA2.2-flash with Docker Model Runner:
docker model run hf.co/inclusionAI/LLaDA2.2-flash
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| "added_tokens_decoder": { | |
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| "clean_up_tokenization_spaces": false, | |
| "cls_token": "[CLS]", | |
| "eos_token": "<|endoftext|>", | |
| "mask_token": "<|mask|>", | |
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| "gmask_token": "[gMASK]", | |
| "merges_file": null, | |
| "model_max_length": 262144, | |
| "pad_token": "<|endoftext|>", | |
| "tokenizer_class": "CustomFastTokenizer", | |
| "auto_map": { | |
| "AutoTokenizer": [ | |
| "tokenization_llada2.CustomFastTokenizer", | |
| null | |
| ] | |
| }, | |
| "trust_remote_code": true, | |
| "vocab_file": null | |
| } |