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--- |
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library_name: transformers |
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tags: |
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- unsloth |
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--- |
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# Model Card for Model ID |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Requirements |
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```python |
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!pip install gradio |
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!pip install -U xformers --index-url https://download.pytorch.org/whl/cu121 |
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!pip install "unsloth[kaggle-new] @ git+https://github.com/unslothai/unsloth.git" |
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import os |
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os.environ["WANDB_DISABLED"] = "true" |
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``` |
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## Gradio App |
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```python |
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import gradio as gr |
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from transformers import AutoTokenizer |
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from peft import AutoPeftModelForCausalLM |
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import torch |
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import anthropic |
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# Assuming the model and tokenizer for Mistral are correctly set up as per your provided code. |
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# Let's also assume you have a way to call the Anthropic model, perhaps via an API or another library. |
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load_in_4bit = True |
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model = AutoPeftModelForCausalLM.from_pretrained( |
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"DisgustingOzil/Mistral_summarizer", |
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load_in_4bit=load_in_4bit, |
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torch_dtype=torch.float16, |
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).to("cuda") |
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tokenizer = AutoTokenizer.from_pretrained("DisgustingOzil/Mistral_summarizer") |
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def summarize_with_mistral(text): |
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summary_prompt = f"""Below is a text that needs to be summarized. Based on the input, write a good summary which summarize all main points. |
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### Text: |
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{text} |
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### Summary: |
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""" # The summary part is left empty for generation |
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inputs = tokenizer([summary_prompt], return_tensors="pt").to("cuda") |
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outputs = model.generate(**inputs, max_new_tokens=150, use_cache=True) |
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summary = tokenizer.batch_decode(outputs, skip_special_tokens=True) |
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summary_start_index = summary[0].find("### Summary:") |
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summary_text = summary[0][summary_start_index:].replace("### Summary:", "").strip() |
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return summary_text |
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summary_1="" |
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def summarize_with_anthropic(text): |
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API_KEY="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" |
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client = anthropic.Anthropic( |
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# defaults to os.environ.get("ANTHROPIC_API_KEY") |
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api_key=API_KEY, |
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) |
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message = client.messages.create( |
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model="claude-3-haiku-20240307", |
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max_tokens=3214, |
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temperature=0, |
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system="Create Good summary explaining all key points in detail, easy and understandable way", |
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messages=[ |
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{ |
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"role": "user", |
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"content": [ |
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{ |
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"type": "text", |
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"text": text |
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} |
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] |
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} |
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] |
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) |
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# Placeholder function to represent summarization with an Anthropic model. |
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# This should be replaced with actual API calls or function calls to the Anthropic model. |
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# summary_1=message.content[0] |
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summary=message.content[0] |
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return summary.text |
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def summarize_text(text, model_choice): |
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if model_choice == "Mistral 7b": |
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return summarize_with_mistral(text) |
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elif model_choice == "Claude-3-Haiku": |
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return summarize_with_anthropic(text) |
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else: |
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return "Invalid model choice." |
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# Define the Gradio interface with a dropdown for model selection |
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iface = gr.Interface( |
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fn=summarize_text, |
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inputs=[gr.Textbox(lines=10, label="Input Text"), gr.Dropdown(choices=["Mistral 7b", "Claude-3-Haiku"], label="Model Choice")], |
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outputs=gr.Textbox(label="Summary"), |
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title="Text Summarization", |
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description="Enter text to summarize based on Maxwell's equations and related concepts. Select a model for summarization." |
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) |
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# Launch the app |
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if __name__ == "__main__": |
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iface.launch(debug=True) |
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``` |
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