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| from huggingface_hub import InferenceClient | |
| import gradio as gr | |
| client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.1") | |
| def format_prompt(message, history): | |
| prompt = "<s>" | |
| for user_prompt, bot_response in history: | |
| prompt += f"[INST] {user_prompt} [/INST]" | |
| prompt += f" {bot_response}</s> " | |
| prompt += f"[INST] {message} [/INST]" | |
| return prompt | |
| def generate(prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0,): | |
| temperature = float(temperature) | |
| if temperature < 1e-2: | |
| temperature = 1e-2 | |
| top_p = float(top_p) | |
| generate_kwargs = dict( | |
| temperature=temperature, | |
| max_new_tokens=max_new_tokens, | |
| top_p=top_p, | |
| repetition_penalty=repetition_penalty, | |
| do_sample=True, | |
| seed=42, | |
| ) | |
| formatted_prompt = format_prompt(prompt, history) | |
| stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) | |
| output = "" | |
| for response in stream: | |
| output += response.token.text | |
| yield output | |
| return output | |
| additional_inputs=[ | |
| gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05, interactive=True, info="Higher values produce more diverse outputs",), | |
| gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=1048, step=64, interactive=True, info="The maximum numbers of new tokens",), | |
| gr.Slider(label="Top-p (nucleus sampling)",value=0.90,minimum=0.0,maximum=1,step=0.05,interactive=True,info="Higher values sample more low-probability tokens",), | |
| gr.Slider(label="Repetition penalty",value=1.2,minimum=1.0,maximum=2.0,step=0.05,interactive=True,info="Penalize repeated tokens",) | |
| ] | |
| css = """#mkd {height: 200px; overflow: auto; border: 1px solid #ccc;}""" | |
| with gr.Blocks(css=css) as demo: | |
| gr.ChatInterface( | |
| generate, | |
| additional_inputs=additional_inputs, | |
| examples = [ | |
| ["🐍 Write a Python Streamlit program that shows a thumbs up and thumbs down button for scoring an evaluation. When the user clicks, maintain a saved text file that tracks and shows the number of clicks with a refresh and sorts responses by the number of clicks."], | |
| ["🐍 Write a Python Gradio program that shows a thumbs up and thumbs down button for scoring an evaluation. When the user clicks, maintain a saved text file that tracks and shows the number of clicks with a refresh and sorts responses by the number of clicks."], | |
| ["📊 Write a Python Streamlit program that creates a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."], | |
| ["📊 Write a Python Gradio program that creates a Pandas DataFrame and display it using Streamlit. Use emojis to indicate the status of each row (e.g., ✅ for good, ❌ for bad)."], | |
| ["🗂 Using Streamlit, create a simple interface where users can upload a CSV file and filter the data based on selected columns."], | |
| ["🗂 Using Gradio, create a simple interface where users can upload a CSV file and filter the data based on selected columns."], | |
| ["😃 Implement emoji reactions in a Streamlit app. When a user clicks on an emoji, record the click count in a Pandas DataFrame and display the DataFrame."], | |
| ["😃 Implement emoji reactions in a Gradio app. When a user clicks on an emoji, record the click count in a Pandas DataFrame and display the DataFrame."], | |
| ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Streamlit app."], | |
| ["🔗 Create a program that fetches a dataset from Huggingface Hub and shows basic statistics about it using Pandas in a Gradio app."], | |
| ["🤖 Use Streamlit to create a user interface for a text summarizer model from Huggingface Hub."], | |
| ["🤖 Use Gradio to create a user interface for a text summarizer model from Huggingface Hub."], | |
| ["📈 Create a Streamlit app to visualize time series data. Use Pandas to manipulate the data and plot it using Streamlit’s native plotting options."], | |
| ["📈 Create a Gradio app to visualize time series data. Use Pandas to manipulate the data and plot it using Streamlit’s native plotting options."], | |
| ["🎙 Implement a voice-activated feature in a Streamlit interface. Use a pre-trained model from Huggingface Hub for speech recognition."], | |
| ["🎙 Implement a voice-activated feature in a Gradio interface. Use a pre-trained model from Huggingface Hub for speech recognition."], | |
| ["🔍 Create a search function in a Streamlit app that filters through a Pandas DataFrame and displays the results."], | |
| ["🔍 Create a search function in a Gradio app that filters through a Pandas DataFrame and displays the results."], | |
| ["🤗 Write a Python script that uploads a model to Huggingface Hub and then uses it in a Streamlit app."], | |
| ["👏 Create a Gradio interface for a clapping hands emoji (👏) counter. When a user inputs a text, the interface should return the number of clapping hands emojis in the text."], | |
| ["📜 Use Pandas to read an Excel sheet in a Streamlit app. Allow the user to select which sheet they want to view."], | |
| ["🔒 Implement a login screen in a Streamlit app using Python. Secure the login by hashing the password."], | |
| ["🤩 Create a Gradio interface that uses a model from Huggingface Hub to generate creative text based on a user’s input. Add sliders for controlling temperature and other hyperparameters."] | |
| ] | |
| ) | |
| gr.HTML("""<h2>🤖 Mistral Chat - Gradio 🤖</h2> | |
| In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬 | |
| Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚 | |
| <h2>🛠 Model Features 🛠</h2> | |
| <ul> | |
| <li>🪟 Sliding Window Attention with 128K tokens span</li> | |
| <li>🚀 GQA for faster inference</li> | |
| <li>📝 Byte-fallback BPE tokenizer</li> | |
| </ul> | |
| <h3>📜 License 📜 Released under Apache 2.0 License</h3> | |
| <h3>📦 Usage 📦</h3> | |
| <ul> | |
| <li>📚 Available on Huggingface Hub</li> | |
| <li>🐍 Python code snippets for easy setup</li> | |
| <li>📈 Expected speedups with Flash Attention 2</li> | |
| </ul> | |
| """) | |
| markdown=""" | |
| | Feature | Description | Byline | | |
| |---------|-------------|--------| | |
| | 🪟 Sliding Window Attention with 128K tokens span | Enables the model to have a larger context for each token. | Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. | | |
| | 🚀 GQA for faster inference | Graph Query Attention allows faster computation during inference. | Speeds up the model inference time without sacrificing too much on accuracy. | | |
| | 📝 Byte-fallback BPE tokenizer | Uses Byte Pair Encoding but can fall back to byte-level encoding. | Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. | | |
| | 📜 License | Released under Apache 2.0 License | Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. | | |
| | 📦 Usage | | | | |
| | 📚 Available on Huggingface Hub | The model can be easily downloaded and set up from Huggingface. | Makes it easier to integrate the model into various projects. | | |
| | 🐍 Python code snippets for easy setup | Provides Python code snippets for quick and easy model setup. | Facilitates rapid development and deployment, especially useful for prototyping. | | |
| | 📈 Expected speedups with Flash Attention 2 | Upcoming update expected to bring speed improvements. | Keep an eye out for this update to benefit from performance gains. | | |
| # 🛠 Model Features and More 🛠 | |
| ## Features | |
| - 🪟 Sliding Window Attention with 128K tokens span | |
| - **Byline**: Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. | |
| - 🚀 GQA for faster inference | |
| - **Byline**: Speeds up the model inference time without sacrificing too much on accuracy. | |
| - 📝 Byte-fallback BPE tokenizer | |
| - **Byline**: Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. | |
| - 📜 License: Released under Apache 2.0 License | |
| - **Byline**: Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. | |
| ## Usage 📦 | |
| - 📚 Available on Huggingface Hub | |
| - **Byline**: Makes it easier to integrate the model into various projects. | |
| - 🐍 Python code snippets for easy setup | |
| - **Byline**: Facilitates rapid development and deployment, especially useful for prototyping. | |
| - 📈 Expected speedups with Flash Attention 2 | |
| - **Byline**: Keep an eye out for this update to benefit from performance gains. | |
| """ | |
| gr.Markdown(markdown) | |
| def SpeechSynthesis(result): | |
| documentHTML5=''' | |
| <!DOCTYPE html> | |
| <html> | |
| <head> | |
| <title>Read It Aloud</title> | |
| <script type="text/javascript"> | |
| function readAloud() { | |
| const text = document.getElementById("textArea").value; | |
| const speech = new SpeechSynthesisUtterance(text); | |
| window.speechSynthesis.speak(speech); | |
| } | |
| </script> | |
| </head> | |
| <body> | |
| <h1>🔊 Read It Aloud</h1> | |
| <textarea id="textArea" rows="10" cols="80"> | |
| ''' | |
| documentHTML5 = documentHTML5 + result | |
| documentHTML5 = documentHTML5 + ''' | |
| </textarea> | |
| <br> | |
| <button onclick="readAloud()">🔊 Read Aloud</button> | |
| </body> | |
| </html> | |
| ''' | |
| gr.HTML(documentHTML5) | |
| SpeechSynthesis(markdown) | |
| demo.queue().launch(debug=True) |