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MekkCyber
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Commit
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21e7b6b
1
Parent(s):
a6b0228
update
Browse files- .gitignore +1 -0
- README.md +1 -1
- app.py +12 -9
.gitignore
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models
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README.md
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title: BitNet.cpp
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emoji: 💻
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colorFrom: blue
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colorTo:
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sdk: docker
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app_file: app.py
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app_port: 7860
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title: BitNet.cpp
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emoji: 💻
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colorFrom: blue
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colorTo: white
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sdk: docker
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app_file: app.py
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app_port: 7860
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app.py
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@@ -36,6 +36,8 @@ def setup_bitnet(model_name):
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def run_inference(model_name, input_text, num_tokens=6):
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try:
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# Call the `run_inference.py` script with the model and input
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start_time = time.time()
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if input_text is None :
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return "Please provide an input text for the model"
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# if oauth_token is None :
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# return "Error : To Compare please login to your HF account and make sure you have access to the used Llama models"
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# Load the model and tokenizer dynamically if needed (commented out for performance)
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if input_text is None :
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return "Please provide an input text for the model", None
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# Encode the input text
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# fastapi_app.add_middleware(SessionMiddleware, secret_key="secret_key") # Use a secure, random secret key
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# # Launch the app
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from fastapi import FastAPI
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app = FastAPI()
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# Add SessionMiddleware for sessions handling
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app.add_middleware(SessionMiddleware, secret_key="secure_secret_key")
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# Mount Gradio app to FastAPI at the root
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app.mount("/", demo)
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def run_inference(model_name, input_text, num_tokens=6):
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try:
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# Call the `run_inference.py` script with the model and input
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model_name = model_name.split("/")[1]
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start_time = time.time()
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if input_text is None :
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return "Please provide an input text for the model"
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# if oauth_token is None :
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# return "Error : To Compare please login to your HF account and make sure you have access to the used Llama models"
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# Load the model and tokenizer dynamically if needed (commented out for performance)
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if model_name=="TinyLlama/TinyLlama-1.1B-Chat-v1.0" :
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tokenizer = AutoTokenizer.from_pretrained('./models/tinyllama')
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model = AutoModelForCausalLM.from_pretrained('./models/tinyllama')
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if input_text is None :
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return "Please provide an input text for the model", None
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# Encode the input text
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# fastapi_app.add_middleware(SessionMiddleware, secret_key="secret_key") # Use a secure, random secret key
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# # Launch the app
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demo.launch()
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# from fastapi import FastAPI
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# app = FastAPI()
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# # Add SessionMiddleware for sessions handling
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# app.add_middleware(SessionMiddleware, secret_key="secure_secret_key")
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# # Mount Gradio app to FastAPI at the root
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# app.mount("/", demo)
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