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import spaces |
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import gradio as gr |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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title = """# 🙋🏻♂️ Welcome to Tonic's Minitron-8B-Base""" |
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description = """ |
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Minitron is a family of small language models (SLMs) obtained by pruning [NVIDIA's](https://huggingface.co/nvidia) Nemotron-4 15B model. We prune model embedding size, attention heads, and MLP intermediate dimension, following which, we perform continued training with distillation to arrive at the final models. |
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### Join us : |
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🌟TeamTonic🌟 is always making cool demos! Join our active builder's 🛠️community 👻 [](https://discord.gg/GWpVpekp) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [BuildTonic](https://github.com/buildtonic/)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 |
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""" |
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model_path = "nvidia/Minitron-8B-Base" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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device='cuda' |
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dtype=torch.bfloat16 |
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model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=dtype, device_map=device) |
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def create_prompt(instruction): |
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PROMPT = '''Below is an instruction that describes a task.\n\nWrite a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:''' |
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return PROMPT.format(instruction=instruction) |
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@spaces.GPU |
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def respond(message, history, system_message, max_tokens, temperature, top_p): |
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prompt = create_prompt(message) |
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.device) |
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output_ids = model.generate(input_ids, max_length=50, num_return_sequences=1) |
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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return output_text |
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demo = gr.ChatInterface( |
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title=gr.Markdown(title), |
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description=gr.Markdown(description), |
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fn=respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are Minitron an AI assistant created by Tonic-AI", label="System message"), |
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), |
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), |
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gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)") |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |
