toaster61 commited on
Commit
43a82b2
1 Parent(s): a098627

add fix (maybe) + adding new languages for translator

Browse files
Files changed (2) hide show
  1. Dockerfile +1 -3
  2. gradio_app.py +18 -6
Dockerfile CHANGED
@@ -19,10 +19,8 @@ RUN mkdir translator
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  RUN chmod -R 777 translator
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  # Installing wget and downloading model.
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- #RUN apt install wget -y
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- #RUN wget -q -O model.bin https://huggingface.co/TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGUF/resolve/main/wizardlm-1.0-uncensored-llama2-13b.Q5_K_M.gguf
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- #RUN ls
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  ADD https://huggingface.co/TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGUF/resolve/main/wizardlm-1.0-uncensored-llama2-13b.Q5_K_M.gguf /app/model.bin
 
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  # You can use other models! Or u can comment this two RUNs and include in Space/repo/Docker image own model with name "model.bin".
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  # Updating pip and installing everything from requirements
 
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  RUN chmod -R 777 translator
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  # Installing wget and downloading model.
 
 
 
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  ADD https://huggingface.co/TheBloke/WizardLM-1.0-Uncensored-Llama2-13B-GGUF/resolve/main/wizardlm-1.0-uncensored-llama2-13b.Q5_K_M.gguf /app/model.bin
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+ RUN chmod -R 777 /app/model.bin
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  # You can use other models! Or u can comment this two RUNs and include in Space/repo/Docker image own model with name "model.bin".
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  # Updating pip and installing everything from requirements
gradio_app.py CHANGED
@@ -23,8 +23,7 @@ print("! INITING DONE !")
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  # Preparing things to work
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  translator_tokenizer.src_lang = "en"
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  title = "llama.cpp API"
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- desc = '''<style>a:visited{color:black;}</style>
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- <h1>Hello, world!</h1>
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  This is showcase how to make own server with Llama2 model.<br>
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  I'm using here 7b model just for example. Also here's only CPU power.<br>
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  But you can use GPU power as well!<br>
@@ -37,6 +36,21 @@ Or you can once follow steps in Dockerfile and try it on your machine, not in Do
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  <br>''' + f"Memory used: {psutil.virtual_memory()[2]}<br>" + '''
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  <script>document.write("<b>URL of space:</b> "+window.location.href);</script>'''
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  # Loading prompt
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  with open('system.prompt', 'r', encoding='utf-8') as f:
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  prompt = f.read()
@@ -54,9 +68,7 @@ def generate_answer(request: str, max_tokens: int = 256, language: str = "en", c
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  try:
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  output = llm(userPrompt, max_tokens=maxTokens, stop=["User:"], echo=False)
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  text = output["choices"][0]["text"]
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- # i allowed only certain languages (its not discrimination, its just other popular language on my opinion!!!):
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- # russian (ru), ukranian (uk), chinese (zh)
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- if language in ["ru", "uk", "zh"]:
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  encoded_input = translator_tokenizer(text, return_tensors="pt")
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  generated_tokens = translator_model.generate(
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  **encoded_input, forced_bos_token_id=translator_tokenizer.get_lang_id(language)
@@ -76,7 +88,7 @@ demo = gr.Interface(
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  inputs=[
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  gr.components.Textbox(label="Input"),
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  gr.components.Number(value=256),
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- gr.components.Dropdown(label="Target Language", value="en", choices=["en", "ru", "uk", "zh"]),
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  gr.components.Textbox(label="Custom system prompt"),
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  ],
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  outputs=["text"],
 
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  # Preparing things to work
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  translator_tokenizer.src_lang = "en"
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  title = "llama.cpp API"
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+ desc = '''<h1>Hello, world!</h1>
 
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  This is showcase how to make own server with Llama2 model.<br>
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  I'm using here 7b model just for example. Also here's only CPU power.<br>
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  But you can use GPU power as well!<br>
 
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  <br>''' + f"Memory used: {psutil.virtual_memory()[2]}<br>" + '''
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  <script>document.write("<b>URL of space:</b> "+window.location.href);</script>'''
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+ '''
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+ # Defining languages for translator (i just chose popular on my opinion languages!!!)
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+ ru - Russian
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+ uk - Ukranian
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+ zh - Chinese
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+ de - German
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+ fr - French
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+ hi - Hindi
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+ it - Italian
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+ ja - Japanese
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+ es - Spanish
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+ ar - Arabic
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+ '''
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+ languages = ["ru", "uk", "zh", "de", "fr", "hi", "it", "ja", "es", "ar"]
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+
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  # Loading prompt
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  with open('system.prompt', 'r', encoding='utf-8') as f:
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  prompt = f.read()
 
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  try:
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  output = llm(userPrompt, max_tokens=maxTokens, stop=["User:"], echo=False)
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  text = output["choices"][0]["text"]
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+ if language in languages:
 
 
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  encoded_input = translator_tokenizer(text, return_tensors="pt")
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  generated_tokens = translator_model.generate(
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  **encoded_input, forced_bos_token_id=translator_tokenizer.get_lang_id(language)
 
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  inputs=[
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  gr.components.Textbox(label="Input"),
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  gr.components.Number(value=256),
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+ gr.components.Dropdown(label="Target Language", value="en", choices=["en"]+languages),
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  gr.components.Textbox(label="Custom system prompt"),
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  ],
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  outputs=["text"],