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import os
import torch
from flask import Flask, render_template, request, Response, stream_with_context
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
# Load model & tokenizer once
base_model_name = "unsloth/llama-3.2-3b-bnb-4bit"
adapter_model_name = "aismaanly/ai_synthetic"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
bnb_4bit_compute_dtype=torch.bfloat16
)
print("Loading base model...")
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=bnb_config,
device_map="auto",
)
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
print("Loading adapter...")
model = PeftModel.from_pretrained(model, adapter_model_name)
model = model.merge_and_unload()
print("Model loaded!")
app = Flask(__name__)
@app.route("/")
def index():
return render_template("index.html")
@app.route("/generate", methods=["POST"])
def generate():
data = request.get_json()
prompt = data.get("prompt")
input_ids = tokenizer(prompt, return_tensors="pt").to(model.device)
def generate_stream():
generated = input_ids["input_ids"]
past_key_values = None
max_tokens = 100
for _ in range(max_tokens):
outputs = model(
input_ids=generated[:, -1:],
past_key_values=past_key_values,
use_cache=True,
)
logits = outputs.logits[:, -1, :]
next_token_id = torch.argmax(logits, dim=-1, keepdim=True)
generated = torch.cat([generated, next_token_id], dim=-1)
past_key_values = outputs.past_key_values
token_text = tokenizer.decode(next_token_id[0])
yield token_text
if token_text.strip() in ["", "\n", "\r\n", "<|endoftext|>"]:
break
return Response(stream_with_context(generate_stream()), content_type='text/plain')
if __name__ == "__main__":
port = int(os.environ.get("PORT", 7860))
app.run(host="0.0.0.0", port=port, threaded=True) |