Instructions to use chatpbclabs/chatpbc-v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use chatpbclabs/chatpbc-v4 with Transformers:

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

pipe = pipeline("text-generation", model="chatpbclabs/chatpbc-v4")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("chatpbclabs/chatpbc-v4")
model = AutoModelForCausalLM.from_pretrained("chatpbclabs/chatpbc-v4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use chatpbclabs/chatpbc-v4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "chatpbclabs/chatpbc-v4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chatpbclabs/chatpbc-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/chatpbclabs/chatpbc-v4

SGLang

How to use chatpbclabs/chatpbc-v4 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-v4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chatpbclabs/chatpbc-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-v4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "chatpbclabs/chatpbc-v4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use chatpbclabs/chatpbc-v4 with Docker Model Runner:
```
docker model run hf.co/chatpbclabs/chatpbc-v4
```

ChatPBC V4 — Advanced Reasoning

ChatPBC V4 is a fine-tuned causal language model specifically trained for advanced reasoning, multi-step problem solving, and deep business consulting conversations.

Model Description

Model type: Causal Language Model
Language: English
Fine-tuned by: ChatPBC Labs
Architecture: Transformer (decoder-only)

Intended Use

ChatPBC V4 is designed for:

Advanced multi-step reasoning and logical analysis
Deep-dive business consulting and strategy sessions
Complex problem decomposition and solution generation
Executive-level advisory conversations

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "chatpbc1/chatpbc-v4"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

inputs = tokenizer("Analyze the competitive landscape for a SaaS startup entering the CRM market:", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=300)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

This model was fine-tuned on a curated dataset of advanced business reasoning tasks, consulting case studies, and executive decision-making scenarios.

Limitations

Outputs are generative and should be reviewed by domain experts.
Not intended for financial or legal advice.

Downloads last month: 2

Safetensors

Model size

7B params

Tensor type

F16