Instructions to use hello12w/persona_chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hello12w/persona_chatbot with Transformers:

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

pipe = pipeline("text-generation", model="hello12w/persona_chatbot")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hello12w/persona_chatbot")
model = AutoModelForCausalLM.from_pretrained("hello12w/persona_chatbot", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hello12w/persona_chatbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hello12w/persona_chatbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hello12w/persona_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/hello12w/persona_chatbot

SGLang

How to use hello12w/persona_chatbot 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 "hello12w/persona_chatbot" \
    --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": "hello12w/persona_chatbot",
		"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 "hello12w/persona_chatbot" \
        --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": "hello12w/persona_chatbot",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use hello12w/persona_chatbot with Docker Model Runner:
```
docker model run hf.co/hello12w/persona_chatbot
```

persona_chatbot / README.md

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metadata

license: mit
language:
  - en
library_name: transformers
datasets:
  - Cynaptics/persona-chat

DialoGPT-Chat-Finetune

This is a fine-tuned version of the DialoGPT model. It has been fine-tuned on persona-based data to generate human-like conversational responses.

Model Description

The model is based on the DialoGPT architecture and has been fine-tuned for conversational tasks, specifically targeting persona-based interactions.

Model Details

Architecture: DialoGPT-medium
Pretraining Data: The original model was pretrained on a large corpus of text data.
Fine-tuning Data: This model was fine-tuned on persona-based conversational data.

Library

Framework: PyTorch
Model: DialoGPT-medium

Example Usage

You can use this model via the Hugging Face transformers library. To use the fine-tuned model for text generation based on a persona, follow these steps:

from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer

# Load the fine-tuned model and tokenizer
model_name = "hello12w/persona_chatbot"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Define the persona and prompt
prompt = prompt = f"""

Person B has the following Persona information.

Persona of Person B: My name is Sarah and I'm a 28 year old software engineer.

Persona of Person B: I love coding and developing new software applications.

Persona of Person B: In my free time, I enjoy reading sci-fi novels and playing board games.

Instruct: Person A and Person B are now having a conversation.

Following the conversation below, write a response that Person B would say based on the above Persona information.

Please carefully consider the flow and context of the conversation below, and use the Person B's Persona information appropriately to generate a response that you think is the most appropriate reply for Person B.

Persona A: Hi Sarah, I heard you're working on a cool project at work. Can you tell me more about it?

Output:

"""
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True)
attention_mask = input_ids.attention_mask
input_ids = input_ids.input_ids

# Inference
with torch.no_grad():
    outputs = model.generate(
        input_ids=input_ids,
        attention_mask=attention_mask,
        max_new_tokens=200,
        do_sample=True,
        top_p=0.95,
        temperature=0.9
    )

# Decode output tokens
decoded_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
output = decoded_outputs[0][len(prompt):]

print(output)