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| import gradio as gr | |
| from llm_rs import AutoModel, SessionConfig, GenerationConfig, Precision, KnownModels | |
| repo_name = "TheBloke/WizardCoder-15B-1.0-GGML" | |
| file_name = "WizardCoder-15B-1.0.ggmlv3.q5_1.bin" | |
| examples = [ | |
| "Write a travel blog about a 3-day trip to Thailand.", | |
| "Tell me a short story about a robot that has a nice day.", | |
| "Compose a tweet to congratulate rustformers on the launch of their HuggingFace Space.", | |
| "Explain how a candle works to a 6-year-old in a few sentences.", | |
| "What are some of the most common misconceptions about birds?", | |
| "Explain why the Rust programming language is so popular.", | |
| ] | |
| session_config = SessionConfig(threads=2,batch_size=2) | |
| model = AutoModel.from_pretrained(repo_name, model_file=file_name, model_type=KnownModels.Gpt2, session_config=session_config,verbose=True) | |
| def process_stream(instruction, temperature, top_p, top_k, max_new_tokens, seed): | |
| prompt=f"""Below is an instruction that describes a task. Write a response that appropriately completes the request. | |
| ### Instruction: | |
| {instruction} | |
| ### Response: | |
| Answer:""" | |
| generation_config = GenerationConfig(seed=seed,temperature=temperature,top_p=top_p,top_k=top_k,max_new_tokens=max_new_tokens) | |
| response = "" | |
| streamer = model.stream(prompt=prompt,generation_config=generation_config) | |
| for new_text in streamer: | |
| response += new_text | |
| yield response | |
| with gr.Blocks( | |
| theme=gr.themes.Soft(), | |
| css=".disclaimer {font-variant-caps: all-small-caps;}", | |
| ) as demo: | |
| gr.Markdown( | |
| """<h1><center>MPT-7B-Instruct on CPU in Rust 🦀</center></h1> | |
| This demo uses the [rustformers/llm](https://github.com/rustformers/llm) library via [llm-rs](https://github.com/LLukas22/llm-rs-python) to execute [MPT-7B-Instruct](https://huggingface.co/mosaicml/mpt-7b-instruct) on 2 CPU cores. | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| instruction = gr.Textbox( | |
| placeholder="Enter your question or instruction here", | |
| label="Question/Instruction", | |
| elem_id="q-input", | |
| ) | |
| with gr.Accordion("Advanced Options:", open=False): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| temperature = gr.Slider( | |
| label="Temperature", | |
| value=0.8, | |
| minimum=0.1, | |
| maximum=1.0, | |
| step=0.1, | |
| interactive=True, | |
| info="Higher values produce more diverse outputs", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| top_p = gr.Slider( | |
| label="Top-p (nucleus sampling)", | |
| value=0.95, | |
| minimum=0.0, | |
| maximum=1.0, | |
| step=0.01, | |
| interactive=True, | |
| info=( | |
| "Sample from the smallest possible set of tokens whose cumulative probability " | |
| "exceeds top_p. Set to 1 to disable and sample from all tokens." | |
| ), | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| top_k = gr.Slider( | |
| label="Top-k", | |
| value=40, | |
| minimum=5, | |
| maximum=80, | |
| step=1, | |
| interactive=True, | |
| info="Sample from a shortlist of top-k tokens — 0 to disable and sample from all tokens.", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| max_new_tokens = gr.Slider( | |
| label="Maximum new tokens", | |
| value=256, | |
| minimum=0, | |
| maximum=1024, | |
| step=5, | |
| interactive=True, | |
| info="The maximum number of new tokens to generate", | |
| ) | |
| with gr.Column(): | |
| with gr.Row(): | |
| seed = gr.Number( | |
| label="Seed", | |
| value=42, | |
| interactive=True, | |
| info="The seed to use for the generation", | |
| precision=0 | |
| ) | |
| with gr.Row(): | |
| submit = gr.Button("Submit") | |
| with gr.Row(): | |
| with gr.Box(): | |
| gr.Markdown("**MPT-7B-Instruct**") | |
| output_7b = gr.Markdown() | |
| with gr.Row(): | |
| gr.Examples( | |
| examples=examples, | |
| inputs=[instruction], | |
| cache_examples=False, | |
| fn=process_stream, | |
| outputs=output_7b, | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| "Disclaimer: MPT-7B can produce factually incorrect output, and should not be relied on to produce " | |
| "factually accurate information. MPT-7B was trained on various public datasets; while great efforts " | |
| "have been taken to clean the pretraining data, it is possible that this model could generate lewd, " | |
| "biased, or otherwise offensive outputs.", | |
| elem_classes=["disclaimer"], | |
| ) | |
| with gr.Row(): | |
| gr.Markdown( | |
| "[Privacy policy](https://gist.github.com/samhavens/c29c68cdcd420a9aa0202d0839876dac)", | |
| elem_classes=["disclaimer"], | |
| ) | |
| submit.click( | |
| process_stream, | |
| inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed], | |
| outputs=output_7b, | |
| ) | |
| instruction.submit( | |
| process_stream, | |
| inputs=[instruction, temperature, top_p, top_k, max_new_tokens,seed], | |
| outputs=output_7b, | |
| ) | |
| demo.queue(max_size=4, concurrency_count=1).launch(debug=True) |