Instructions to use Tesslate/WEBGEN-Devstral-24B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tesslate/WEBGEN-Devstral-24B with Transformers:

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

pipe = pipeline("text-generation", model="Tesslate/WEBGEN-Devstral-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tesslate/WEBGEN-Devstral-24B")
model = AutoModelForCausalLM.from_pretrained("Tesslate/WEBGEN-Devstral-24B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Tesslate/WEBGEN-Devstral-24B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tesslate/WEBGEN-Devstral-24B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tesslate/WEBGEN-Devstral-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tesslate/WEBGEN-Devstral-24B

SGLang

How to use Tesslate/WEBGEN-Devstral-24B 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 "Tesslate/WEBGEN-Devstral-24B" \
    --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": "Tesslate/WEBGEN-Devstral-24B",
		"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 "Tesslate/WEBGEN-Devstral-24B" \
        --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": "Tesslate/WEBGEN-Devstral-24B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tesslate/WEBGEN-Devstral-24B with Docker Model Runner:
```
docker model run hf.co/Tesslate/WEBGEN-Devstral-24B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

WEBGEN DEVSTRAL IMAGES. WEBGEN MODELS MAKE HTML CSS JS TAILWIND ONLY LANDING PAGES. IF YOU NEED REACT, PYTHON, OR OTHER LANGUAGES, CHECK OUT UIGEN-X, UIGENT, UIGEN SERIES. IT WAS TRAINED ON CUSTOM TEMPLATES, FED INTO GPT-OSS-120B ON 13X MI300XS TO CREATE THE DATASET. THEN SFT FINETUNED. APACHE 2.0. TRAINED BY QINGY2004.