Text Generation
Transformers
PyTorch
Safetensors
English
gpt2
conversational
text-generation-inference
Instructions to use jesseD/homer-bot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jesseD/homer-bot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jesseD/homer-bot") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("jesseD/homer-bot") model = AutoModelForCausalLM.from_pretrained("jesseD/homer-bot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use jesseD/homer-bot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "jesseD/homer-bot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "jesseD/homer-bot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/jesseD/homer-bot
- SGLang
How to use jesseD/homer-bot 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 "jesseD/homer-bot" \ --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": "jesseD/homer-bot", "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 "jesseD/homer-bot" \ --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": "jesseD/homer-bot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use jesseD/homer-bot with Docker Model Runner:
docker model run hf.co/jesseD/homer-bot
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jesseD/homer-bot")
model = AutoModelForCausalLM.from_pretrained("jesseD/homer-bot")Quick Links
HomerBot: A conversational chatbot imitating Homer Simpson
This model is a fine-tuned DialoGPT (medium version) on Simpsons scripts.
More specifically, we fine-tune DialoGPT-medium for 3 epochs on 10K (character utterance, Homer's response) pairs
For more details, check out our git repo containing all the code.
How to use
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("DingleyMaillotUrgell/homer-bot")
model = AutoModelForCausalLM.from_pretrained("DingleyMaillotUrgell/homer-bot")
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User: ") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids,
max_length=1000,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature = 0.8
)
# print last outpput tokens from bot
print("Homer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="jesseD/homer-bot") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)