Instructions to use deepparag/DumBot-Beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deepparag/DumBot-Beta with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deepparag/DumBot-Beta") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot-Beta") model = AutoModelForCausalLM.from_pretrained("deepparag/DumBot-Beta") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deepparag/DumBot-Beta with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deepparag/DumBot-Beta" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deepparag/DumBot-Beta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/deepparag/DumBot-Beta
- SGLang
How to use deepparag/DumBot-Beta 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 "deepparag/DumBot-Beta" \ --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": "deepparag/DumBot-Beta", "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 "deepparag/DumBot-Beta" \ --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": "deepparag/DumBot-Beta", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use deepparag/DumBot-Beta with Docker Model Runner:
docker model run hf.co/deepparag/DumBot-Beta
| thumbnail: https://cdn.discordapp.com/app-icons/870239976690970625/c02cae78ae105f07969cfd8f8ea3d0a0.png | |
| tags: | |
| - conversational | |
| license: mit | |
| An generative AI made using [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small). | |
| Trained on: | |
| https://www.kaggle.com/Cornell-University/movie-dialog-corpus | |
| https://www.kaggle.com/jef1056/discord-data | |
| Important: | |
| The AI can be a bit weird at times as it is still undergoing training! | |
| At times it send stuff using :<random_wierd_words>: as they are discord emotes. | |
| It also send random @RandomName as it is trying to ping people. | |
| This works well on discord but on the web not so much but it is easy enough to remove such stuff using [re.sub](https://docs.python.org/3/library/re.html#re.sub) | |
| Issues: | |
| The AI like with all conversation AI lacks a character, it changes its name way too often. This can be solved using an AIML chatbot to give it a stable character! | |
| [Live Demo](https://dumbot-331213.uc.r.appspot.com/) | |
| Example: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| tokenizer = AutoTokenizer.from_pretrained("deepparag/DumBot") | |
| model = AutoModelWithLMHead.from_pretrained("deepparag/DumBot") | |
| # Let's chat for 4 lines | |
| for step in range(4): | |
| # 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') | |
| # print(new_user_input_ids) | |
| # 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=200, | |
| pad_token_id=tokenizer.eos_token_id, | |
| no_repeat_ngram_size=4, | |
| do_sample=True, | |
| top_k=100, | |
| top_p=0.7, | |
| temperature=0.8 | |
| ) | |
| # pretty print last ouput tokens from bot | |
| print("DumBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) | |
| ``` |