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 / README.md

FredyRivera-dev

Update README.md

bae861d verified 15 days ago

preview code

Raw

History Blame Contribute Delete

1.02 kB

	---
	datasets:
	- Fredtt3/LLaDA-Sample-10BT
	- Fredtt3/LLaDA-Sample-ES
	language:
	- en
	- es
	pipeline_tag: text-generation
	library_name: transformers
	---


	# 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.

	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

	Example of the results it gives:

	![image/png](lladaout.PNG)

	For those who want to train and get the correct format to be able to load it with `transformers`, everything needed is in [`pre_trainv2.py`](https://github.com/FredyRivera-dev/LLaDA-from-scratch/blob/main/pre_trainv2.py) of the project repo