Instructions to use chatpbc1/chatpbc-v33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chatpbc1/chatpbc-v33 with Transformers:

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

pipe = pipeline("text-generation", model="chatpbc1/chatpbc-v33")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chatpbc1/chatpbc-v33")
model = AutoModelForCausalLM.from_pretrained("chatpbc1/chatpbc-v33", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chatpbc1/chatpbc-v33 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Install mistral-common:
pip install --upgrade mistral-common
# Start the vLLM server:
vllm serve "chatpbc1/chatpbc-v33" --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chatpbc1/chatpbc-v33",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/chatpbc1/chatpbc-v33

SGLang

How to use chatpbc1/chatpbc-v33 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 "chatpbc1/chatpbc-v33" \
    --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": "chatpbc1/chatpbc-v33",
		"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 "chatpbc1/chatpbc-v33" \
        --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": "chatpbc1/chatpbc-v33",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use chatpbc1/chatpbc-v33 with Docker Model Runner:
```
docker model run hf.co/chatpbc1/chatpbc-v33
```

chatpbc-v33 / chatpbc_webscraper.py

chatpbc1

Upload chatpbc_webscraper.py with huggingface_hub

c83f32b verified 6 days ago

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	import requests
	from bs4 import BeautifulSoup

	def scrape_url(url):
	"""
	Scrapes the content of a given URL for business analysis.
	"""
	try:
	headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}
	response = requests.get(url, headers=headers, timeout=10)
	response.raise_for_status()

	soup = BeautifulSoup(response.text, 'html.parser')

	# Remove script and style elements
	for script in soup(["script", "style"]):
	script.decompose()

	# Get text
	text = soup.get_text()

	# Break into lines and remove leading and trailing whitespace
	lines = (line.strip() for line in text.splitlines())
	# Break multi-headlines into a line each
	chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
	# Drop blank lines
	text = '\n'.join(chunk for chunk in chunks if chunk)

	return text[:5000] # Return first 5000 characters for context
	except Exception as e:
	return f"Error scraping URL: {str(e)}"

	if __name__ == "__main__":
	url = "https://www.mik-tse.com"
	content = scrape_url(url)
	print(f"Scraped content from {url}:\n{content[:200]}...")