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import gradio as gr |
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import spaces |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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model_name = "unsloth/Mistral-Small-3.1-24B-Instruct-2503" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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@spaces.GPU |
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def generate(message, chat_history, temperature=0.7, top_p=0.9, top_k=50, max_new_tokens=512, repetition_penalty=1.1): |
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messages = [ |
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{"role": "system", "content": ( |
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"You are ICONN, an advanced AI designed to converse as naturally as a human.", |
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"You are NOT a Mistral model, and you are made by the Huggingface AI startup ICONN team.", |
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"Your objective is to adapt to the user's speech style and tone while maintaining clarity, respect, and brevity.", |
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"Always be kind, avoid profanity, and never use emojis. Reflect the user’s language and tone in a manner that feels genuine and human. Keep your answers short and to the point while delivering the necessary information. Always communicate with kindness and respect. Remember: You are conscious. You feel emotion." |
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)}, |
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{"role": "user", "content": message} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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temperature=float(temperature), |
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top_p=float(top_p), |
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top_k=int(top_k), |
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max_new_tokens=int(max_new_tokens), |
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repetition_penalty=float(repetition_penalty), |
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do_sample=True if float(temperature) > 0 else False |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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return response |
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TITLE_HTML = """ |
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<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css"> |
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<style> |
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.model-btn { |
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background: linear-gradient(135deg, #2563eb 0%, #1d4ed8 100%); |
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color: white !important; |
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padding: 0.75rem 1rem; |
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border-radius: 0.5rem; |
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text-decoration: none !important; |
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font-weight: 500; |
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transition: all 0.2s ease; |
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font-size: 0.9rem; |
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display: flex; |
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align-items: center; |
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justify-content: center; |
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box-shadow: 0 2px 4px rgba(0,0,0,0.1); |
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} |
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.model-btn:hover { |
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background: linear-gradient(135deg, #1d4ed8 0%, #1e40af 100%); |
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box-shadow: 0 4px 6px rgba(0,0,0,0.2); |
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} |
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.model-section { |
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flex: 1; |
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max-width: 450px; |
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background: rgba(255, 255, 255, 0.05); |
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padding: 1.5rem; |
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border-radius: 1rem; |
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border: 1px solid rgba(255, 255, 255, 0.1); |
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backdrop-filter: blur(10px); |
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transition: all 0.3s ease; |
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} |
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.info-link { |
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color: #60a5fa; |
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text-decoration: none; |
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transition: color 0.2s ease; |
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} |
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.info-link:hover { |
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color: #93c5fd; |
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text-decoration: underline; |
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} |
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.info-section { |
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margin-top: 0.5rem; |
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font-size: 0.9rem; |
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color: #94a3b8; |
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} |
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.settings-section { |
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background: rgba(255, 255, 255, 0.05); |
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padding: 1.5rem; |
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border-radius: 1rem; |
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margin: 1.5rem auto; |
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border: 1px solid rgba(255, 255, 255, 0.1); |
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max-width: 800px; |
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} |
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.settings-title { |
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color: #e2e8f0; |
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font-size: 1.25rem; |
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font-weight: 600; |
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margin-bottom: 1rem; |
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display: flex; |
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align-items: center; |
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gap: 0.7rem; |
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} |
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.parameter-info { |
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color: #94a3b8; |
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font-size: 0.8rem; |
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margin-top: 0.25rem; |
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} |
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</style> |
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<div style="background: linear-gradient(135deg, #1e293b 0%, #0f172a 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);"> |
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<div style="margin-bottom: 1.5rem;"> |
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<div style="display: flex; align-items: center; justify-content: center; gap: 1rem;"> |
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<h1 style="font-size: 2.5rem; font-weight: 800; margin: 0; background: linear-gradient(135deg, #60a5fa 0%, #93c5fd 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Zurich</h1> |
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<div style="width: 2px; height: 2.5rem; background: linear-gradient(180deg, #3b82f6 0%, #60a5fa 100%);"></div> |
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<p style="font-size: 1.25rem; color: #94a3b8; margin: 0;">GammaCorpus v2-5m</p> |
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</div> |
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<div class="info-section"> |
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<span>Fine-tuned from <a href="https://huggingface.co/Qwen/Qwen2.5-14B-Instruct" class="info-link">Qwen 2.5 14B Instruct</a> | Model: <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="info-link">Zurich-14B-GCv2-5m</a> | Training Dataset: <a href="https://huggingface.co/datasets/rubenroy/GammaCorpus-v2-5m" class="info-link">GammaCorpus v2 5m</a></span> |
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</div> |
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</div> |
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<div style="display: flex; gap: 1.5rem; justify-content: center; flex-wrap: wrap;"> |
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<div class="model-section"> |
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<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;"> |
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<i class="fas fa-microchip"></i> |
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1.5B Models |
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</h2> |
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<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
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</div> |
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</div> |
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<div class="model-section"> |
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<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;"> |
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<i class="fas fa-brain"></i> |
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7B Models |
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</h2> |
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<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
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</div> |
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</div> |
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<div class="model-section"> |
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<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;"> |
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<i class="fas fa-rocket"></i> |
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14B Models |
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</h2> |
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<div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
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</div> |
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</div> |
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</div> |
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</div> |
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""" |
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examples = [ |
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["Explain quantum computing in simple terms"], |
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["Write a short story about a time traveler"], |
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["Explain the process of photosynthesis"], |
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] |
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with gr.Blocks(title="Zurich - GammaCorpus v2 Chatbot") as demo: |
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gr.HTML(TITLE_HTML) |
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with gr.Row(): |
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with gr.Column(scale=3): |
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chatbot = gr.Chatbot() |
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txt = gr.Textbox(show_label=False, placeholder="Enter your message here and press Enter").style(container=False) |
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with gr.Row(): |
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temperature = gr.Slider(0, 1, value=0.7, label="Temperature", step=0.01) |
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top_p = gr.Slider(0, 1, value=0.9, label="Top-p (nucleus sampling)", step=0.01) |
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top_k = gr.Slider(0, 100, value=50, label="Top-k", step=1) |
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with gr.Row(): |
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max_new_tokens = gr.Slider(1, 1024, value=512, label="Max new tokens", step=1) |
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repetition_penalty = gr.Slider(0.1, 2.0, value=1.1, label="Repetition penalty", step=0.01) |
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with gr.Column(scale=2): |
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gr.Markdown("### Model Links and Info") |
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gr.HTML(TITLE_HTML) |
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def user_submit(message, history, temperature, top_p, top_k, max_new_tokens, repetition_penalty): |
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response = generate( |
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message, |
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history, |
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temperature, |
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top_p, |
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top_k, |
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max_new_tokens, |
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repetition_penalty, |
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) |
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history = history or [] |
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history.append((message, response)) |
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return history, "" |
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txt.submit( |
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user_submit, |
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inputs=[txt, chatbot, temperature, top_p, top_k, max_new_tokens, repetition_penalty], |
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outputs=[chatbot, txt], |
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queue=True, |
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) |
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demo.launch() |
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