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# Importing libraries | |
from transformers import M2M100Tokenizer, M2M100ForConditionalGeneration | |
from llama_cpp import Llama | |
import gradio as gr | |
import psutil | |
# Initing things | |
llm = Llama(model_path="./model.bin") # LLaMa model | |
llama_model_name = "TheBloke/Llama-2-13B-chat-GGUF" | |
translator_tokenizer = M2M100Tokenizer.from_pretrained( # tokenizer for translator | |
"facebook/m2m100_1.2B", cache_dir="translator/" | |
) | |
translator_model = M2M100ForConditionalGeneration.from_pretrained( # translator model | |
"facebook/m2m100_1.2B", cache_dir="translator/" | |
) | |
print("! SETTING MODEL IN EVALUATION MODE !") | |
translator_model.eval() | |
print("! DONE !") | |
# Preparing things to work | |
translator_tokenizer.src_lang = "en" | |
title = "llama.cpp API" | |
desc = '''<style>a:visited{color:black;}</style> | |
<h1>Hello, world!</h1> | |
This is showcase how to make own server with Llama2 model.<br> | |
I'm using here 7b model just for example. Also here's only CPU power.<br> | |
But you can use GPU power as well!<br> | |
<h1>How to GPU?</h1> | |
Change <code>`CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS`</code> in Dockerfile on <code>`CMAKE_ARGS="-DLLAMA_CUBLAS=on"`</code>. Also you can try <code>`DLLAMA_CLBLAST`</code>, <code>`DLLAMA_METAL`</code> or <code>`DLLAMA_METAL`</code>.<br> | |
Powered by <a href="https://github.com/abetlen/llama-cpp-python">llama-cpp-python</a>, <a href="https://quart.palletsprojects.com/">Quart</a> and <a href="https://www.uvicorn.org/">Uvicorn</a>.<br> | |
<h1>How to test it on own machine?</h1> | |
You can install Docker, build image and run it. I made <code>`run-docker.sh`</code> for ya. To stop container run <code>`docker ps`</code>, find name of container and run <code>`docker stop _dockerContainerName_`</code><br> | |
Or you can once follow steps in Dockerfile and try it on your machine, not in Docker.<br> | |
<br>''' + f"Memory used: {psutil.virtual_memory()[2]}<br>" + ''' | |
<script>document.write("<b>URL of space:</b> "+window.location.href);</script>''' | |
# Loading prompt | |
with open('system.prompt', 'r', encoding='utf-8') as f: | |
prompt = f.read() | |
def generate_answer(request: str, max_tokens: int = 256, language: str = "en", custom_prompt: str = None): | |
try: | |
maxTokens = max_tokens if 16 <= max_tokens <= 256 else 64 | |
if isinstance(custom_prompt, str): | |
userPrompt = custom_prompt + "\n\nUser: " + request + "\nAssistant: " | |
else: | |
userPrompt = prompt + "\n\nUser: " + request + "\nAssistant: " | |
except: | |
return "Not enough data! Check that you passed all needed data." | |
try: | |
output = llm(userPrompt, max_tokens=maxTokens, stop=["User:", "\n"], echo=False) | |
text = output["choices"][0]["text"] | |
# i allowed only certain languages (its not discrimination, its just other popular language on my opinion!!!): | |
# russian (ru), ukranian (uk), chinese (zh) | |
if language in ["ru", "uk", "zh"]: | |
encoded_input = translator_tokenizer(output, return_tensors="pt") | |
generated_tokens = translator_model.generate( | |
**encoded_input, forced_bos_token_id=translator_tokenizer.get_lang_id(language) | |
) | |
translated_text = translator_tokenizer.batch_decode( | |
generated_tokens, skip_special_tokens=True | |
)[0] | |
return translated_text | |
return text | |
except Exception as e: | |
print(e) | |
return "Oops! Internal server error. Check the logs of space/instance." | |
print("! LOAD GRADIO INTERFACE !") | |
demo = gr.Interface( | |
fn=generate_answer, | |
inputs=[ | |
gr.components.Textbox(label="Input"), | |
gr.components.Number(value=256), | |
gr.components.Dropdown(label="Target Language", value="en", choices=["en", "ru", "uk", "zh"]), | |
gr.components.Textbox(label="Custom system prompt"), | |
], | |
outputs=["text"], | |
title=title, | |
description=desc | |
).queue() | |
if __name__ == "__main__": | |
print("! LAUNCHING GRADIO !") | |
demo.launch() |