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import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import spaces
title = """# 🙋🏻♂️Welcome to 🌟Tonic's ☯️🧑💻Yi-Coder-9B-Chat Demo!"""
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:
```bash
'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'
```
### Join us :
🌟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 🤗
"""
# Define the device and model path
device = "cuda" if torch.cuda.is_available() else "cpu"
model_path = "01-ai/Yi-Coder-9B-Chat"
# Load the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
@spaces.GPU(duration=130)
def generate_code(system_prompt, user_prompt, max_length):
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=max_length,
eos_token_id=tokenizer.eos_token_id
)
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
def gradio_interface():
with gr.Blocks() as interface:
gr.Markdown(title)
gr.Markdown(description)
system_prompt_input = gr.Textbox(
label="☯️Yinstruction:",
value="You are a helpful coding assistant. Provide clear and concise code examples.",
lines=2
)
user_prompt_input = gr.Code(
label="🤔Coding Question",
value="Write a quick sort algorithm in Python.",
language="python",
lines=15
)
code_output = gr.Code(label="☯️Yi-Coder-7B", language='python', lines=20, interactive=True)
max_length_slider = gr.Slider(minimum=1, maximum=1800, value=650, label="Max Token Length")
generate_button = gr.Button("Generate Code")
generate_button.click(
generate_code,
inputs=[system_prompt_input, user_prompt_input, max_length_slider],
outputs=code_output
)
return interface
if __name__ == "__main__":
interface = gradio_interface()
interface.queue()
interface.launch() |