Instructions to use caikybaldo999/webcoder-100m-html-css-js with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use caikybaldo999/webcoder-100m-html-css-js with Transformers:

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

pipe = pipeline("text-generation", model="caikybaldo999/webcoder-100m-html-css-js")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("caikybaldo999/webcoder-100m-html-css-js")
model = AutoModelForCausalLM.from_pretrained("caikybaldo999/webcoder-100m-html-css-js", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use caikybaldo999/webcoder-100m-html-css-js with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "caikybaldo999/webcoder-100m-html-css-js"
# 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-100m-html-css-js",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/caikybaldo999/webcoder-100m-html-css-js

SGLang

How to use caikybaldo999/webcoder-100m-html-css-js 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-100m-html-css-js" \
    --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-100m-html-css-js",
		"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-100m-html-css-js" \
        --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-100m-html-css-js",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use caikybaldo999/webcoder-100m-html-css-js with Docker Model Runner:
```
docker model run hf.co/caikybaldo999/webcoder-100m-html-css-js
```

WebCoder-100M

A small decoder-only model specialized in HTML, CSS and JavaScript.

Architecture

Parameters: 99,894,528
Layers: 11
Hidden size: 768
Attention heads: 12
Vocabulary: 28,672
Max context: 2,048
Training sequence length: 1,024

Training

The model was initialized from scratch.

Causal pre-training on HTML/CSS/JavaScript.
Instruction fine-tuning on web-development instruction/code pairs.

Token accounting

Total processed: 582,209,140
Pre-training: 568,246,272
SFT processed: 13,962,868
SFT supervised response tokens: 9,124,446
Global cap: 2,000,000,000

Data

Pre-training: bigcode/the-stack-smol-xl, HTML/JavaScript/CSS subsets.

Instruction tuning: iamtarun/code_instructions_120k_alpaca, filtered for web-development examples.

Review upstream dataset cards and source licenses before commercial use.

Prompt format

<|system|>
You are WebCoder...<|end|>
<|user|>
Create a responsive landing page...<|end|>
<|assistant|>
...

Limitations

This is a roughly 100M-parameter model trained from scratch. Its quality depends strongly on how many tokens were actually processed. Generated code can contain bugs or security issues and should be reviewed.

Downloads last month: -

Safetensors

Model size

99.9M params

Tensor type

F32

caikybaldo999
/

webcoder-100m-html-css-js