Instructions to use caikybaldo999/webcoder-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caikybaldo999/webcoder-pro with Transformers:

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

pipe = pipeline("text-generation", model="caikybaldo999/webcoder-pro")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caikybaldo999/webcoder-pro")
model = AutoModelForCausalLM.from_pretrained("caikybaldo999/webcoder-pro", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use caikybaldo999/webcoder-pro with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caikybaldo999/webcoder-pro"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caikybaldo999/webcoder-pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/caikybaldo999/webcoder-pro

SGLang

How to use caikybaldo999/webcoder-pro 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 "caikybaldo999/webcoder-pro" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caikybaldo999/webcoder-pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "caikybaldo999/webcoder-pro" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "caikybaldo999/webcoder-pro",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use caikybaldo999/webcoder-pro with Docker Model Runner:
```
docker model run hf.co/caikybaldo999/webcoder-pro
```

webcoder-pro / README.md

caikybaldo999

Upload WebCoder-100M — step 8000

7070f0a verified 1 day ago

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	---
	library_name: transformers
	pipeline_tag: text-generation
	language:
	- code
	- pt
	- en
	tags:
	- html
	- css
	- javascript
	- code-generation
	- from-scratch
	datasets:
	- bigcode/the-stack-smol-xl
	license: other
	---

	# WebCoder-100M

	WebCoder-100M é um pequeno modelo decoder-only treinado do zero para geração e continuação de HTML, CSS e JavaScript.

	## Arquitetura

	- Parâmetros: 100,000,512 (100.000512M)
	- Layers: 11
	- Hidden size: 768
	- Attention heads: 12
	- Contexto: 1024 tokens
	- Vocabulário: 27,664
	- Precisão de treino: BF16

	## Dataset

	Treinado a partir dos subconjuntos HTML, CSS e JavaScript de `bigcode/the-stack-smol-xl`, com limpeza, deduplicação e filtro opcional por metadados de licença.

	O código-fonte original do dataset pode possuir licenças variadas. Consulte o dataset e a origem de cada amostra antes de usar os pesos em cenários que exijam revisão jurídica específica.

	## Formato de prompt

	```text
	<\|bos\|><\|prompt\|>
	Crie uma landing page responsiva.
	<\|html\|>
	```

	Também existem os tokens `<\|css\|>` e `<\|javascript\|>`.

	## Métricas desta execução

	- Step final: 8000
	- Eval loss: 1.2921
	- Perplexidade: 3.6405
	- Tempo de treino: 47.43 min

	## Limitações

	Este é um modelo de aproximadamente 100M parâmetros e treinamento curto. Pode produzir HTML/CSS/JS inválido, inseguro, incompleto ou repetitivo. Revise o código antes de executar ou publicar.

	## Uso com Transformers

	```python
	from transformers import AutoTokenizer, AutoModelForCausalLM

	tok = AutoTokenizer.from_pretrained("caikybaldo999/webcoder-pro")
	model = AutoModelForCausalLM.from_pretrained("caikybaldo999/webcoder-pro")
	```