| import gradio as gr |
| import spaces |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
|
|
| model_name = "rubenroy/Zurich-7B-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 helpul assistant named Zurich, a 7 billion parameter Large Language model, you were 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 was also made by Ruben Roy."}, |
| {"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, #2563eb 0%, #1d4ed8 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, #1d4ed8 0%, #1e40af 100%); |
| box-shadow: 0 4px 6px rgba(0,0,0,0.2); |
| } |
| .model-section { |
| flex: 1; |
| max-width: 450px; |
| 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: #60a5fa; |
| text-decoration: none; |
| transition: color 0.2s ease; |
| } |
| .info-link:hover { |
| color: #93c5fd; |
| 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, #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);"> |
| <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, #60a5fa 0%, #93c5fd 100%); -webkit-background-clip: text; -webkit-text-fill-color: transparent;">Zurich</h1> |
| <div style="width: 2px; height: 2.5rem; background: linear-gradient(180deg, #3b82f6 0%, #60a5fa 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/Qwen/Qwen2.5-7B-Instruct" class="info-link">Qwen 2.5 7B Instruct</a> | Model: <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-5m" class="info-link">Zurich-7B-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; flex-wrap: wrap;"> |
| <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="fas fa-microchip"></i> |
| 1.5B Models |
| </h2> |
| <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-5m" class="model-btn">Zurich 1.5B GCv2 5m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-1m" class="model-btn">Zurich 1.5B GCv2 1m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-500k" class="model-btn">Zurich 1.5B GCv2 500k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-100k" class="model-btn">Zurich 1.5B GCv2 100k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-50k" class="model-btn">Zurich 1.5B GCv2 50k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-1.5B-GCv2-10k" class="model-btn">Zurich 1.5B GCv2 10k</a> |
| </div> |
| </div> |
| <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="fas fa-brain"></i> |
| 7B Models |
| </h2> |
| <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-5m" class="model-btn">Zurich 7B GCv2 5m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-1m" class="model-btn">Zurich 7B GCv2 1m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-500k" class="model-btn">Zurich 7B GCv2 500k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-100k" class="model-btn">Zurich 7B GCv2 100k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-50k" class="model-btn">Zurich 7B GCv2 50k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-7B-GCv2-10k" class="model-btn">Zurich 7B GCv2 10k</a> |
| </div> |
| </div> |
| <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="fas fa-rocket"></i> |
| 14B Models |
| </h2> |
| <div style="display: grid; grid-template-columns: repeat(2, 1fr); gap: 0.75rem;"> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-5m" class="model-btn">Zurich 14B GCv2 5m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-1m" class="model-btn">Zurich 14B GCv2 1m</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-500k" class="model-btn">Zurich 14B GCv2 500k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-100k" class="model-btn">Zurich 14B GCv2 100k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-50k" class="model-btn">Zurich 14B GCv2 50k</a> |
| <a href="https://huggingface.co/rubenroy/Zurich-14B-GCv2-10k" class="model-btn">Zurich 14B GCv2 10k</a> |
| </div> |
| </div> |
| </div> |
| </div> |
| """ |
|
|
| examples = [ |
| ["Explain quantum computing in simple terms"], |
| ["Write a short story about a time traveler"], |
| ["Explain the process of photosynthesis"], |
| ["Tell me an interesting fact about Palm trees"] |
| ] |
|
|
| 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) |
