Instructions to use Fredtt3/LLaDA-100M-Test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fredtt3/LLaDA-100M-Test with Transformers:

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

pipe = pipeline("text-generation", model="Fredtt3/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("Fredtt3/LLaDA-100M-Test", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Fredtt3/LLaDA-100M-Test with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fredtt3/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": "Fredtt3/LLaDA-100M-Test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fredtt3/LLaDA-100M-Test

SGLang

How to use Fredtt3/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 "Fredtt3/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": "Fredtt3/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 "Fredtt3/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": "Fredtt3/LLaDA-100M-Test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Fredtt3/LLaDA-100M-Test with Docker Model Runner:
```
docker model run hf.co/Fredtt3/LLaDA-100M-Test
```

Fredtt3 commited on Sep 30, 2025

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@@ -10,9 +10,6 @@ library_name: transformers
 ---
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/63cb46191b705cc951e88e6c/2LZWEcQcoLIT4Fm1SHMCB.png)
 # New checkpoint trained on an NVIDIA H100 for 8,000 steps and 65,536,000 tokens
 It is not yet a competent model because it does not meet the minimum training requirement of 20-30 tokens per parameter. However, it can give us a better idea of how a better-trained model would perform.

 ---
 # New checkpoint trained on an NVIDIA H100 for 8,000 steps and 65,536,000 tokens
 It is not yet a competent model because it does not meet the minimum training requirement of 20-30 tokens per parameter. However, it can give us a better idea of how a better-trained model would perform.