Text Generation
Transformers
Safetensors
English
llama
business
consulting
chatpbc
sharp-business-ideas
text-generation-inference
Instructions to use chatpbclabs/chatpbc-v33 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use chatpbclabs/chatpbc-v33 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="chatpbclabs/chatpbc-v33")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("chatpbclabs/chatpbc-v33") model = AutoModelForCausalLM.from_pretrained("chatpbclabs/chatpbc-v33", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use chatpbclabs/chatpbc-v33 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "chatpbclabs/chatpbc-v33" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "chatpbclabs/chatpbc-v33", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/chatpbclabs/chatpbc-v33
- SGLang
How to use chatpbclabs/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 "chatpbclabs/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": "chatpbclabs/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 "chatpbclabs/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": "chatpbclabs/chatpbc-v33", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use chatpbclabs/chatpbc-v33 with Docker Model Runner:
docker model run hf.co/chatpbclabs/chatpbc-v33
ChatPBC V33 โ Sharp Business Ideas
ChatPBC V33 is a fine-tuned causal language model specifically trained to generate sharp, actionable business ideas, market analysis, and strategic consulting insights.
Model Description
- Model type: Causal Language Model
- Language: English
- License: Apache 2.0
- Fine-tuned by: ChatPBC Labs
- Architecture: Transformer (decoder-only)
Intended Use
ChatPBC V33 is designed for:
- Generating sharp, concise business ideas on demand
- Providing market entry strategies and competitive analysis
- Assisting entrepreneurs and consultants with ideation
- Business plan drafting and strategic brainstorming
How to Use
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "chatpbc1/chatpbc-v33"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("Give me a sharp business idea for the fitness industry:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Training Details
This model was fine-tuned on a curated dataset of business consulting conversations, startup pitch decks, and strategic planning documents.
Limitations
- Outputs are generative and should be reviewed by a human expert before implementation.
- Not intended for financial or legal advice.
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