Instructions to use inclusionAI/LLaDA2.0-mini-CAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inclusionAI/LLaDA2.0-mini-CAP with Transformers:

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

pipe = pipeline("text-generation", model="inclusionAI/LLaDA2.0-mini-CAP", 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-CAP", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use inclusionAI/LLaDA2.0-mini-CAP 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-CAP"
# 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-CAP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inclusionAI/LLaDA2.0-mini-CAP

SGLang

How to use inclusionAI/LLaDA2.0-mini-CAP 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-CAP" \
    --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-CAP",
		"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-CAP" \
        --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-CAP",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inclusionAI/LLaDA2.0-mini-CAP with Docker Model Runner:
```
docker model run hf.co/inclusionAI/LLaDA2.0-mini-CAP
```

kashif HF Staff commited on Mar 15

Commit

f4a945b

verified ·

1 Parent(s): ce6a36f

fix: remove deprecated is_torch_fx_available for transformers v5 compat

Browse files

Files changed (1) hide show

modeling_llada2_moe.py +0 -8

modeling_llada2_moe.py CHANGED Viewed

@@ -52,7 +52,6 @@ from transformers.utils import (
     logging,
     replace_return_docstrings,
 )
-from transformers.utils.import_utils import is_torch_fx_available
 from .configuration_llada2_moe import LLaDA2MoeConfig
 from transformers.generation.utils import GenerationMixin
@@ -62,13 +61,6 @@ if is_flash_attn_2_available():
     from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
-# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
-# It means that the function will not be traced through and simply appear as a node in the graph.
-if is_torch_fx_available():
-    if not is_torch_greater_or_equal_than_1_13:
-        import torch.fx
-    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
 logger = logging.get_logger(__name__)

     logging,
     replace_return_docstrings,
 )
 from .configuration_llada2_moe import LLaDA2MoeConfig
 from transformers.generation.utils import GenerationMixin
     from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
 logger = logging.get_logger(__name__)