| tags: | |
| - autotrain | |
| - text-generation-inference | |
| - text-generation | |
| - peft | |
| library_name: transformers | |
| base_model: microsoft/biogpt | |
| widget: | |
| - messages: | |
| - role: user | |
| content: What is your favorite condiment? | |
| license: other | |
| # Model Trained Using AutoTrain | |
| This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain). | |
| # Usage | |
| ```python | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| model_path = "PATH_TO_THIS_REPO" | |
| tokenizer = AutoTokenizer.from_pretrained(model_path) | |
| model = AutoModelForCausalLM.from_pretrained( | |
| model_path, | |
| device_map="auto", | |
| torch_dtype='auto' | |
| ).eval() | |
| # Prompt content: "hi" | |
| messages = [ | |
| {"role": "user", "content": "hi"} | |
| ] | |
| input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt') | |
| output_ids = model.generate(input_ids.to('cuda')) | |
| response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) | |
| # Model response: "Hello! How can I assist you today?" | |
| print(response) | |
| ``` |