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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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import spaces |
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title = """# 🙋🏻♂️Welcome to 🌟Tonic's ☯️🧑💻Yi-Coder-9B-Chat Demo!""" |
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description = """Yi-Coder-9B-Chat is a 9B parameter model fine-tuned for coding tasks. This demo showcases its ability to generate code based on your prompts. Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters. Excelling in long-context understanding with a maximum context length of 128K tokens. - Supporting 52 major programming languages: |
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```bash |
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'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog' |
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``` |
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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/qdfnvSPcqP) On 🤗Huggingface:[MultiTransformer](https://huggingface.co/MultiTransformer) On 🌐Github: [Tonic-AI](https://github.com/tonic-ai) & contribute to🌟 [Build Tonic](https://git.tonic-ai.com/contribute)🤗Big thanks to Yuvi Sharma and all the folks at huggingface for the community grant 🤗 |
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""" |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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model_path = "01-ai/Yi-Coder-9B-Chat" |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval() |
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@spaces.GPU(duration=130) |
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def generate_code(system_prompt, user_prompt, max_length): |
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messages = [ |
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{"role": "system", "content": system_prompt}, |
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{"role": "user", "content": user_prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=max_length, |
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eos_token_id=tokenizer.eos_token_id |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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return response |
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def gradio_interface(): |
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with gr.Blocks() as interface: |
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gr.Markdown(title) |
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gr.Markdown(description) |
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system_prompt_input = gr.Textbox( |
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label="☯️Yinstruction:", |
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value="You are a helpful coding assistant. Provide clear and concise code examples.", |
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lines=2 |
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) |
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user_prompt_input = gr.Code( |
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label="🤔Coding Question", |
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value="Write a quick sort algorithm in Python.", |
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language="python", |
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lines=15 |
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) |
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code_output = gr.Code(label="☯️Yi-Coder-7B", language='python', lines=20, interactive=True) |
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max_length_slider = gr.Slider(minimum=1, maximum=1800, value=650, label="Max Token Length") |
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generate_button = gr.Button("Generate Code") |
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generate_button.click( |
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generate_code, |
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inputs=[system_prompt_input, user_prompt_input, max_length_slider], |
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outputs=code_output |
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) |
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return interface |
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if __name__ == "__main__": |
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interface = gradio_interface() |
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interface.queue() |
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interface.launch() |
