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from flask import Flask, request, jsonify | |
from langchain_community.llms import LlamaCpp | |
import os | |
app = Flask(__name__) | |
n_gpu_layers = 0 | |
n_batch = 1024 | |
llm = LlamaCpp( | |
model_path="Phi-3-mini-4k-instruct-q4.gguf", # path to GGUF file | |
temperature=0.1, | |
n_gpu_layers=n_gpu_layers, | |
n_batch=n_batch, | |
verbose=True, | |
n_ctx=4096 | |
) | |
file_size = os.stat('Phi-3-mini-4k-instruct-q4.gguf') | |
print("model size ====> :", file_size.st_size, "bytes") | |
def get_skills(): | |
cv_body = request.json.get('cv_body') | |
# Simple inference example | |
output = llm( | |
f"<|user|>\n{cv_body}<|end|>\n<|assistant|>Can you list the skills mentioned in the CV?<|end|>", | |
max_tokens=256, # Generate up to 256 tokens | |
stop=["<|end|>"], | |
echo=True, # Whether to echo the prompt | |
) | |
return jsonify({'skills': output}) | |
def health(): | |
return jsonify({'status': 'healthy'}) | |
if __name__ == '__main__': | |
app.run() |