Spaces:
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on
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Running
on
Zero
| 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) | |
| 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) |