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from huggingface_hub import InferenceClient |
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import gradio as gr |
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|
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client = InferenceClient( |
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"mistralai/Mistral-7B-Instruct-v0.1" |
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) |
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|
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def format_prompt(message, history): |
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prompt = "<s>" |
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for user_prompt, bot_response in history: |
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prompt += f"[INST] {user_prompt} [/INST]" |
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prompt += f" {bot_response}</s> " |
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prompt += f"[INST] {message} [/INST]" |
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return prompt |
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|
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def generate( |
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prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0, |
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): |
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temperature = float(temperature) |
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if temperature < 1e-2: |
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temperature = 1e-2 |
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top_p = float(top_p) |
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|
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generate_kwargs = dict( |
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temperature=temperature, |
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max_new_tokens=max_new_tokens, |
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top_p=top_p, |
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repetition_penalty=repetition_penalty, |
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do_sample=True, |
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seed=42, |
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) |
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formatted_prompt = format_prompt(prompt, history) |
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stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False) |
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output = "" |
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for response in stream: |
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output += response.token.text |
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yield output |
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return output |
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additional_inputs=[ |
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gr.Slider( |
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label="Temperature", |
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value=0.9, |
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minimum=0.0, |
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maximum=1.0, |
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step=0.05, |
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interactive=True, |
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info="Higher values produce more diverse outputs", |
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), |
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gr.Slider( |
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label="Max new tokens", |
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value=256, |
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minimum=0, |
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maximum=1048, |
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step=64, |
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interactive=True, |
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info="The maximum numbers of new tokens", |
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), |
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gr.Slider( |
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label="Top-p (nucleus sampling)", |
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value=0.90, |
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minimum=0.0, |
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maximum=1, |
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step=0.05, |
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interactive=True, |
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info="Higher values sample more low-probability tokens", |
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), |
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gr.Slider( |
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label="Repetition penalty", |
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value=1.2, |
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minimum=1.0, |
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maximum=2.0, |
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step=0.05, |
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interactive=True, |
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info="Penalize repeated tokens", |
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) |
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] |
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css = """ |
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#mkd { |
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height: 200px; |
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overflow: auto; |
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border: 1px solid #ccc; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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|
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gr.ChatInterface( |
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generate, |
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additional_inputs=additional_inputs, |
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examples = [ |
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["🎸 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Everclear songs, including when they were top ten. 🎤"], |
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["🎵 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Taylor Swift songs, including when they were top ten. 🎶"], |
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["🎙️ Show full verse, chorus, intro, and outro chords and lyrics for top 3 Adele songs, including when they were top ten. 🎧"], |
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["🎼 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Bruno Mars songs, including when they were top ten. 🎷"], |
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["🎹 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Lady Gaga songs, including when they were top ten. 🎺"], |
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["🎻 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Ed Sheeran songs, including when they were top ten. 🥁"], |
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["🎤 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Drake songs, including when they were top ten. 🎶"], |
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["🎧 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Rihanna songs, including when they were top ten. 🎵"], |
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["🎷 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Justin Bieber songs, including when they were top ten. 🎼"], |
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["🎶 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Beyoncé songs, including when they were top ten. 🎙️"], |
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["🎺 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Katy Perry songs, including when they were top ten. 🎹"], |
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["🥁 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Eminem songs, including when they were top ten. 🎻"], |
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["🎤 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Ariana Grande songs, including when they were top ten. 🎧"], |
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["🎶 Show full verse, chorus, intro, and outro chords and lyrics for top 3 Billie Eilish songs, including when they were top ten. 🎵"] |
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] |
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) |
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gr.HTML("""<h2>🤖 Mistral Chat - Gradio 🤖</h2> |
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In this demo, you can chat with <a href='https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1'>Mistral-7B-Instruct</a> model. 💬 |
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Learn more about the model <a href='https://huggingface.co/docs/transformers/main/model_doc/mistral'>here</a>. 📚 |
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<h2>🛠 Model Features 🛠</h2> |
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<ul> |
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<li>🪟 Sliding Window Attention with 128K tokens span</li> |
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<li>🚀 GQA for faster inference</li> |
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<li>📝 Byte-fallback BPE tokenizer</li> |
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</ul> |
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<h3>📜 License 📜 Released under Apache 2.0 License</h3> |
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<h3>📦 Usage 📦</h3> |
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<ul> |
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<li>📚 Available on Huggingface Hub</li> |
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<li>🐍 Python code snippets for easy setup</li> |
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<li>📈 Expected speedups with Flash Attention 2</li> |
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</ul> |
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""") |
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|
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markdown=""" |
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| Feature | Description | Byline | |
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|---------|-------------|--------| |
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| 🪟 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. | |
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| 🚀 GQA for faster inference | Graph Query Attention allows faster computation during inference. | Speeds up the model inference time without sacrificing too much on accuracy. | |
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| 📝 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. | |
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| 📜 License | Released under Apache 2.0 License | Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. | |
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| 📦 Usage | | | |
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| 📚 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. | |
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| 🐍 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. | |
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| 📈 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. | |
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# 🛠 Model Features and More 🛠 |
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## Features |
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- 🪟 Sliding Window Attention with 128K tokens span |
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- **Byline**: Increases model's understanding of context, resulting in more coherent and contextually relevant outputs. |
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- 🚀 GQA for faster inference |
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- **Byline**: Speeds up the model inference time without sacrificing too much on accuracy. |
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- 📝 Byte-fallback BPE tokenizer |
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- **Byline**: Allows the tokenizer to handle a wider variety of input text while keeping token size manageable. |
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- 📜 License: Released under Apache 2.0 License |
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- **Byline**: Gives you a permissive free software license, allowing you freedom to use, modify, and distribute the code. |
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## Usage 📦 |
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- 📚 Available on Huggingface Hub |
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- **Byline**: Makes it easier to integrate the model into various projects. |
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- 🐍 Python code snippets for easy setup |
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- **Byline**: Facilitates rapid development and deployment, especially useful for prototyping. |
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- 📈 Expected speedups with Flash Attention 2 |
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- **Byline**: Keep an eye out for this update to benefit from performance gains. |
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""" |
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gr.Markdown(markdown) |
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|
|
|
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def SpeechSynthesis(result): |
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documentHTML5=''' |
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<!DOCTYPE html> |
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<html> |
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<head> |
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<title>Read It Aloud</title> |
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<script type="text/javascript"> |
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function readAloud() { |
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const text = document.getElementById("textArea").value; |
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const speech = new SpeechSynthesisUtterance(text); |
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window.speechSynthesis.speak(speech); |
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} |
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</script> |
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</head> |
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<body> |
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<h1>🔊 Read It Aloud</h1> |
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<textarea id="textArea" rows="10" cols="80"> |
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''' |
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documentHTML5 = documentHTML5 + result |
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documentHTML5 = documentHTML5 + ''' |
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</textarea> |
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<br> |
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<button onclick="readAloud()">🔊 Read Aloud</button> |
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</body> |
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</html> |
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''' |
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gr.HTML(documentHTML5) |
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SpeechSynthesis(markdown) |
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|
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|
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demo.queue().launch(debug=True) |
