🤖 DeciLM-6B-Instruct: A Fast Instruction-Tuned Model💨
Welcome to DeciLM-6B-Instruct! DeciLM-6B-Instruct is a 6B parameter instruction-tuned language model and released under the Llama license. It's an instruction-tuned model, not a chat-tuned model; you should prompt the model with an instruction that describes a task, and the model will respond appropriately to complete the task.Learn more about the base model DeciLM-6B.
""" if not torch.cuda.is_available(): DESCRIPTION += 'You need a GPU for this example. Try using colab: https://bit.ly/decilm-instruct-nb' if torch.cuda.is_available(): model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map='auto', trust_remote_code=True, use_auth_token=token ) else: model = None tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=token) tokenizer.pad_token = tokenizer.eos_token # Function to construct the prompt using the new system prompt template def get_prompt_with_template(message: str) -> str: return SYSTEM_PROMPT_TEMPLATE.format(instruction=message) # Function to generate the model's response def generate_model_response(message: str) -> str: prompt = get_prompt_with_template(message) inputs = tokenizer(prompt, return_tensors='pt') if torch.cuda.is_available(): inputs = inputs.to('cuda') # Include **generate_kwargs to include the user-defined options output = model.generate(**inputs, max_new_tokens=3000, num_beams=2, no_repeat_ngram_size=4, early_stopping=True, do_sample=True ) return tokenizer.decode(output[0], skip_special_tokens=True) # Function to extract the content after "### Response:" def extract_response_content(full_response: str, ) -> str: response_start_index = full_response.find("### Response:") if response_start_index != -1: return full_response[response_start_index + len("### Response:"):].strip() else: return full_response # The main function that uses the dynamic generate_kwargs def get_response_with_template(message: str) -> str: full_response = generate_model_response(message) return extract_response_content(full_response) with gr.Blocks(css="style.css") as demo: gr.Markdown(DESCRIPTION) gr.DuplicateButton(value='Duplicate Space for private use', elem_id='duplicate-button') with gr.Group(): chatbot = gr.Textbox(label='DeciLM-6B-Instruct Output:') with gr.Row(): textbox = gr.Textbox( container=False, show_label=False, placeholder='Type an instruction...', scale=10, elem_id="textbox" ) submit_button = gr.Button( '💬 Submit', variant='primary', scale=1, min_width=0, elem_id="submit_button" ) # Clear button to clear the chat history clear_button = gr.Button( '🗑️ Clear', variant='secondary', ) clear_button.click( fn=lambda: ('',''), outputs=[textbox, chatbot], queue=False, api_name=False, ) submit_button.click( fn=get_response_with_template, inputs=textbox, outputs= chatbot, queue=False, api_name=False, ) gr.Examples( examples=[ 'Write detailed instructions for making chocolate chip pancakes.', 'Write a 250-word article about your love of pancakes.', 'Explain the plot of Back to the Future in three sentences.', 'How do I make a trap beat?', 'A step-by-step guide to learning Python in one month.', ], inputs=textbox, outputs=chatbot, fn=get_response_with_template, cache_examples=True, elem_id="examples" ) gr.HTML(label="Keep in touch", value="
")
demo.launch()