Text Generation
Transformers
Safetensors
llada2_moe
dllm
diffusion
llm
text_generation
conversational
custom_code
Instructions to use inclusionAI/LLaDA2.0-mini-preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use inclusionAI/LLaDA2.0-mini-preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.0-mini-preview", 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/LLaDA2.0-mini-preview", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use inclusionAI/LLaDA2.0-mini-preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "inclusionAI/LLaDA2.0-mini-preview" # 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/LLaDA2.0-mini-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/inclusionAI/LLaDA2.0-mini-preview
- SGLang
How to use inclusionAI/LLaDA2.0-mini-preview 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/LLaDA2.0-mini-preview" \ --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/LLaDA2.0-mini-preview", "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/LLaDA2.0-mini-preview" \ --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/LLaDA2.0-mini-preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use inclusionAI/LLaDA2.0-mini-preview with Docker Model Runner:
docker model run hf.co/inclusionAI/LLaDA2.0-mini-preview
Add support for greedy decoding (#5)
Browse files- Add support for greedy decoding (3d62ef09768490af8a8c7867405942fcc79749b8)
Co-authored-by: Aditya Tomar <adityastomar@users.noreply.huggingface.co>
- modeling_llada2_moe.py +8 -0
modeling_llada2_moe.py
CHANGED
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@@ -1431,6 +1431,14 @@ class LLaDA2MoeModelLM(LLaDA2MoePreTrainedModel, GenerationMixin):
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orig_shape = logits.shape[:-1]
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vocab_size = logits.shape[-1]
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logits = logits.reshape(-1, vocab_size)
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if temperature > 0 and temperature != 1.0:
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logits = logits / temperature
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logits = self._top_k_logits(logits, top_k)
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orig_shape = logits.shape[:-1]
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vocab_size = logits.shape[-1]
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logits = logits.reshape(-1, vocab_size)
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# Greedy mode: temperature = 0, no top-k/p
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if temperature == 0.0 and (top_k in (None, 0)) and (top_p is None or top_p >= 1.0):
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probs = F.softmax(logits, dim=-1)
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token = logits.argmax(dim=-1, keepdim=True)
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token_prob = probs.gather(-1, token)
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return token.view(*orig_shape), token_prob.view(*orig_shape)
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if temperature > 0 and temperature != 1.0:
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logits = logits / temperature
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logits = self._top_k_logits(logits, top_k)
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