Instructions to use CoderEFE/DialoGPT-marxbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CoderEFE/DialoGPT-marxbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CoderEFE/DialoGPT-marxbot")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CoderEFE/DialoGPT-marxbot") model = AutoModelForCausalLM.from_pretrained("CoderEFE/DialoGPT-marxbot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use CoderEFE/DialoGPT-marxbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CoderEFE/DialoGPT-marxbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CoderEFE/DialoGPT-marxbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/CoderEFE/DialoGPT-marxbot
- SGLang
How to use CoderEFE/DialoGPT-marxbot 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 "CoderEFE/DialoGPT-marxbot" \ --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": "CoderEFE/DialoGPT-marxbot", "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 "CoderEFE/DialoGPT-marxbot" \ --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": "CoderEFE/DialoGPT-marxbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use CoderEFE/DialoGPT-marxbot with Docker Model Runner:
docker model run hf.co/CoderEFE/DialoGPT-marxbot
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Download README.md from CoderEFE/DialoGPT-marxbot: direct link, hf CLI and curl.
- Browser
- Download file 1.24 kB
-
https://huggingface.co/CoderEFE/DialoGPT-marxbot/resolve/main/README.md
- Command line
-
hf download hf://CoderEFE/DialoGPT-marxbot/README.md
-
curl -L -o README.md https://huggingface.co/CoderEFE/DialoGPT-marxbot/resolve/main/README.md
1.24 kB
| tags: | |
| - conversational | |
| Chat with the model: | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelWithLMHead | |
| tokenizer = AutoTokenizer.from_pretrained("r3dhummingbird/DialoGPT-marxbot") | |
| model = AutoModelWithLMHead.from_pretrained("r3dhummingbird/DialoGPT-marxbot") | |
| # 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=3, | |
| do_sample=True, | |
| top_k=100, | |
| top_p=0.7, | |
| temperature=0.8 | |
| ) | |
| # pretty print last ouput tokens from bot | |
| print("MarxBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True))) | |
| ``` |