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import gradio as gr |
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import spaces |
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from huggingface_hub import InferenceClient |
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import os |
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""" |
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For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference |
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""" |
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client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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bnb_config = BitsAndBytesConfig(load_in_8bit=True) |
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token=os.getenv('token') |
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print('token = ',token) |
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model_id = "CohereForAI/c4ai-command-r-plus" |
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tokenizer = AutoTokenizer.from_pretrained(model_id, token= token) |
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model = AutoModelForCausalLM.from_pretrained(model_id, token= token, quantization_config=bnb_config) |
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messages = [{"role": "user", "content": "Hello, how are you?"}] |
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input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") |
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gen_tokens = model.generate( |
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input_ids, |
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max_new_tokens=100, |
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do_sample=True, |
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temperature=0.3, |
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) |
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gen_text = tokenizer.decode(gen_tokens[0]) |
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print(gen_text) |
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@spaces.GPU(duration=120) |
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def respond( |
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message, |
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history: list[tuple[str, str]], |
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system_message, |
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max_tokens, |
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temperature, |
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top_p, |
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): |
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messages = [{"role": "system", "content": system_message}] |
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messages = [{"role": "user", "content": "Hello, how are you?"}] |
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input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt") |
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gen_tokens = model.generate( |
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input_ids, |
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max_new_tokens=100, |
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do_sample=True, |
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temperature=0.3, |
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) |
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gen_text = tokenizer.decode(gen_tokens[0]) |
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print(gen_text) |
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yield gen_text |
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""" |
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface |
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""" |
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demo = gr.ChatInterface( |
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respond, |
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additional_inputs=[ |
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gr.Textbox(value="You are a friendly Chatbot.", 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( |
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minimum=0.1, |
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maximum=1.0, |
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value=0.95, |
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step=0.05, |
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label="Top-p (nucleus sampling)", |
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), |
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], |
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) |
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if __name__ == "__main__": |
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demo.launch() |