Text Generation
Transformers
PyTorch
Safetensors
English
llama
pretrained
mistral-common
text-generation-inference
Instructions to use chatpbc1/chatpbc-v33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 1,006 Bytes
1ce10f0 6e94b5b 1ce10f0 6e94b5b 1ce10f0 6e94b5b 1ce10f0 6e94b5b 1ce10f0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | import os
def process_file(file_path):
"""
Processes uploaded business documents to extract text for analysis.
"""
_, extension = os.path.splitext(file_path)
extension = extension.lower()
try:
if extension == '.txt':
with open(file_path, 'r', encoding='utf-8') as f:
return f.read()
elif extension == '.md':
with open(file_path, 'r', encoding='utf-8') as f:
return f.read()
else:
return f"Unsupported file type: {extension}. Currently only .txt and .md are supported for direct extraction."
except Exception as e:
return f"Error processing file: {str(e)}"
if __name__ == "__main__":
# Test with a dummy file
test_file = "test_doc.txt"
with open(test_file, "w") as f:
f.write("This is a sample business document for ChatPBC analysis.")
content = process_file(test_file)
print(f"Processed file content: {content}")
os.remove(test_file)
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