Instructions to use FredyRivera-dev/LLaDA-100M-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FredyRivera-dev/LLaDA-100M-Test with Transformers:

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

pipe = pipeline("text-generation", model="FredyRivera-dev/LLaDA-100M-Test", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("FredyRivera-dev/LLaDA-100M-Test", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FredyRivera-dev/LLaDA-100M-Test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FredyRivera-dev/LLaDA-100M-Test"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FredyRivera-dev/LLaDA-100M-Test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FredyRivera-dev/LLaDA-100M-Test

SGLang

How to use FredyRivera-dev/LLaDA-100M-Test 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 "FredyRivera-dev/LLaDA-100M-Test" \
    --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": "FredyRivera-dev/LLaDA-100M-Test",
		"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 "FredyRivera-dev/LLaDA-100M-Test" \
        --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": "FredyRivera-dev/LLaDA-100M-Test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FredyRivera-dev/LLaDA-100M-Test with Docker Model Runner:
```
docker model run hf.co/FredyRivera-dev/LLaDA-100M-Test
```

LLaDA-100M-Test / model.py

Commit History

New checkpoint trained in 8,000 steps and 65,536,000 tokens

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Fr commited on Aug 5, 2025

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Update model.py

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Fr commited on Jul 17, 2025

Upload model.py

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Commit History

New checkpoint trained in 8,000 steps and 65,536,000 tokens 2cb0949 verified

Delete model.py c3d2ce9 verified

Update model.py 3b5f344 verified

Upload model.py 3ef8190 verified

New checkpoint trained in 8,000 steps and 65,536,000 tokens

2cb0949
verified

Delete model.py

c3d2ce9
verified

Update model.py

3b5f344
verified

Upload model.py

3ef8190
verified