Text Generation
Transformers
Safetensors
code
Portuguese
English
gpt2
html
css
javascript
code-generation
from-scratch
text-generation-inference
Instructions to use caikybaldo999/webcoder-pro with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "model_name": "webcoder-100m", | |
| "output_dir": "/content/webcoder-100m", | |
| "tokenizer_dir": "/content/webcoder-tokenizer", | |
| "seed": 42, | |
| "dataset_id": "bigcode/the-stack-smol-xl", | |
| "languages": [ | |
| "html", | |
| "css", | |
| "javascript" | |
| ], | |
| "max_samples_per_language": 10000, | |
| "min_chars": 120, | |
| "max_chars": 20000, | |
| "license_filter": true, | |
| "vocab_size": 27664, | |
| "context_length": 1024, | |
| "n_embd": 768, | |
| "n_layer": 11, | |
| "n_head": 12, | |
| "train_batch_size": 8, | |
| "eval_batch_size": 8, | |
| "gradient_accumulation_steps": 4, | |
| "learning_rate": 0.0004, | |
| "weight_decay": 0.1, | |
| "warmup_ratio": 0.03, | |
| "max_steps": 8000, | |
| "time_limit_minutes": 55, | |
| "logging_steps": 20, | |
| "eval_steps": 250, | |
| "save_steps": 500, | |
| "save_total_limit": 3, | |
| "hf_repo_id": "caikybaldo999/webcoder-pro", | |
| "hf_private": false | |
| } |