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, device_map="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
```

Fr commited on Jul 17, 2025

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@@ -14,6 +14,6 @@ pipeline_tag: text-generation
 # DISCLAIMER: Ok, a 310M model has been trained in a test in only 200 steps, on a mini dataset of 40,960 tokens the model is not competent, it is only a test and is compatible with Transformers
-If you want to try how to use it here is a file of how to use it in [test_gen.py](https://github.com/F4k3r22/LLaDA-from-scratch/blob/main/test_gen.py) Or using this Google [Colab](https://colab.research.google.com/drive/1jPIPu9qHEFMkANzUEkeOxUW6hS3DeVwd?usp=sharing) notebook
 For those who want to train and get the correct format to be able to load it with `transformers`, everything needed is in [`pre_train.py`](https://github.com/F4k3r22/LLaDA-from-scratch/blob/main/pre_train.py) of the project repo

 # DISCLAIMER: Ok, a 310M model has been trained in a test in only 200 steps, on a mini dataset of 40,960 tokens the model is not competent, it is only a test and is compatible with Transformers
+If you want to try how to use it here is a file of how to use it in [test_gen.py](https://github.com/F4k3r22/LLaDA-from-scratch/blob/main/test_gen.py) Or using this [Google Colab](https://colab.research.google.com/drive/1jPIPu9qHEFMkANzUEkeOxUW6hS3DeVwd?usp=sharing) notebook
 For those who want to train and get the correct format to be able to load it with `transformers`, everything needed is in [`pre_train.py`](https://github.com/F4k3r22/LLaDA-from-scratch/blob/main/pre_train.py) of the project repo