app.py

import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "rubenroy/Geneva-12B-GCv2-5m"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

@spaces.GPU
def generate(message, chat_history, temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=512, repetition_penalty=1.1):
    messages = [
        {"role": "system", "content": "You are a helpful assistant named Geneva, a 12 billion parameter Large Language Model, fine-tuned and trained by Ruben Roy. You have been trained with the GammaCorpus v2 dataset, a dataset filled with structured and filtered multi-turn conversations. This dataset was also made by Ruben Roy."}, # Attribution for Mistral removed to prevent unneccesary hallucinations.
    ]
    
    for user, assistant in chat_history:
        messages.append({"role": "user", "content": user})
        messages.append({"role": "assistant", "content": assistant})
    
    messages.append({"role": "user", "content": message})
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
    
    generated_ids = model.generate(
        **model_inputs,
        temperature=float(temperature),
        top_p=float(top_p),
        top_k=int(top_k),
        max_new_tokens=int(max_new_tokens),
        repetition_penalty=float(repetition_penalty),
        do_sample=True if float(temperature) > 0 else False
    )
    
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    return response


TITLE_HTML = """
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css">
<style>
    .model-btn {
        background: linear-gradient(135deg, #059669 0%, #047857 100%);
        color: white !important;
        padding: 0.75rem 1rem;
        border-radius: 0.5rem;
        text-decoration: none !important;
        font-weight: 500;
        transition: all 0.2s ease;
        font-size: 0.9rem;
        display: flex;
        align-items: center;
        justify-content: center;
        box-shadow: 0 2px 4px rgba(0,0,0,0.1);
    }
    .model-btn:hover {
        background: linear-gradient(135deg, #047857 0%, #065f46 100%);
        box-shadow: 0 4px 6px rgba(0,0,0,0.2);
    }
    .model-section {
        flex: 1;
        max-width: 800px;
        background: rgba(255, 255, 255, 0.05);
        padding: 1.5rem;
        border-radius: 1rem;
        border: 1px solid rgba(255, 255, 255, 0.1);
        backdrop-filter: blur(10px);
        transition: all 0.3s ease;
    }
    .info-link {
        color: #34d399;
        text-decoration: none;
        transition: color 0.2s ease;
    }
    .info-link:hover {
        color: #6ee7b7;
        text-decoration: underline;
    }
    .info-section {
        margin-top: 0.5rem;
        font-size: 0.9rem;
        color: #94a3b8;
    }
    .settings-section {
        background: rgba(255, 255, 255, 0.05);
        padding: 1.5rem;
        border-radius: 1rem;
        margin: 1.5rem auto;
        border: 1px solid rgba(255, 255, 255, 0.1);
        max-width: 800px;
    }
    .settings-title {
        color: #e2e8f0;
        font-size: 1.25rem;
        font-weight: 600;
        margin-bottom: 1rem;
        display: flex;
        align-items: center;
        gap: 0.7rem;
    }
    .parameter-info {
        color: #94a3b8;
        font-size: 0.8rem;
        margin-top: 0.25rem;
    }
</style>

<div style="background: linear-gradient(135deg, #064e3b 0%, #022c22 100%); padding: 1.5rem; border-radius: 1.5rem; text-align: center; margin: 1rem auto; max-width: 1200px; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);">
    <div style="margin-bottom: 1.5rem;">
        <div style="display: flex; align-items: center; justify-content: center; gap: 1rem;">
            <h1 style="font-size: 2.5rem; font-weight: 800; margin: 0; background: linear-gradient(135deg, #34d399 0%, #6ee7b7 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Geneva</h1>
            <div style="width: 2px; height: 2.5rem; background: linear-gradient(180deg, #059669 0%, #34d399 100%);"></div>
            <p style="font-size: 1.25rem; color: #94a3b8; margin: 0;">GammaCorpus v2-5m</p>
        </div>
        <div class="info-section">
            <span>Fine-tuned from <a href="https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407" class="info-link">Mistral NeMo Instruct 2407</a> | Model: <a href="https://huggingface.co/rubenroy/Geneva-14B-GCv2-5m" class="info-link">Geneva-14B-GCv2-5m</a> | Training Dataset: <a href="https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-5m" class="info-link">GammaCorpus v2 5m</a></span>
        </div>
    </div>

    <div style="display: flex; gap: 1.5rem; justify-content: center;">
        <div class="model-section">
            <h2 style="font-size: 1.25rem; color: #e2e8f0; margin-bottom: 1.4rem; margin-top: 1px; font-weight: 600; display: flex; align-items: center; justify-content: center; gap: 0.7rem;">
                <i class="fa-solid fa-sparkles"></i>
                Geneva Models
            </h2>
            <div style="display: grid; grid-auto-flow: column; gap: 0.75rem; overflow-x: auto; white-space: nowrap;">
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-5m" class="model-btn">Geneva 12B GCv2 5m</a>
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-1m" class="model-btn">Geneva 12B GCv2 1m</a>
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-500k" class="model-btn">Geneva 12B GCv2 500k</a>
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-100k" class="model-btn">Geneva 12B GCv2 100k</a>
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-50k" class="model-btn">Geneva 12B GCv2 50k</a>
                <a href="https://huggingface.co/rubenroy/Geneva-12B-GCv2-10k" class="model-btn">Geneva 12B GCv2 10k</a>
            </div>
        </div>
    </div>
</div>
"""

examples = [
    ["Explain deep learning in simple terms."],
    ["Write a short science fiction story."],
    ["Describe the laws of thermodynamics."],
    ["Write me a simple game in Python."]
]

with gr.Blocks() as demo:
    gr.HTML(TITLE_HTML)
    
    with gr.Accordion("Generation Settings", open=False):
        with gr.Row():
            with gr.Column():
                temperature = gr.Slider(
                    minimum=0.0,
                    maximum=2.0,
                    value=0.7,
                    step=0.1,
                    label="Temperature",
                    info="Higher values make the output more random, lower values make it more deterministic",
                    interactive=True
                )
                top_p = gr.Slider(
                    minimum=0.0,
                    maximum=1.0,
                    value=0.9,
                    step=0.05,
                    label="Top P",
                    info="Controls the cumulative probability threshold for nucleus sampling",
                    interactive=True
                )
                top_k = gr.Slider(
                    minimum=1,
                    maximum=100,
                    value=50,
                    step=1,
                    label="Top K",
                    info="Limits the number of tokens to consider for each generation step",
                    interactive=True
                )
            with gr.Column():
                max_new_tokens = gr.Slider(
                    minimum=1,
                    maximum=2048,
                    value=512,
                    step=1,
                    label="Max New Tokens",
                    info="Maximum number of tokens to generate in the response",
                    interactive=True
                )
                repetition_penalty = gr.Slider(
                    minimum=1.0,
                    maximum=2.0,
                    value=1.1,
                    step=0.1,
                    label="Repetition Penalty",
                    info="Higher values stop the model from repeating the same info",
                    interactive=True
                )
    
    chatbot = gr.ChatInterface(
        fn=generate,
        additional_inputs=[
            temperature,
            top_p,
            top_k,
            max_new_tokens,
            repetition_penalty
        ],
        examples=examples
    )

demo.launch(share=True)