Create app.py

app.py

@@ -0,0 +1,107 @@
+from flask import Flask, request, jsonify
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+import torch
+
+app = Flask(__name__)
+
+MODEL_NAME = "IlyaGusev/saiga2_70b_lora"
+DEFAULT_MESSAGE_TEMPLATE = "<s>{role}\n{content}</s>\n"
+DEFAULT_SYSTEM_PROMPT = "Ты — Сайга, русскоязычный автоматический ассистент. Ты разговариваешь с людьми и помогаешь им."
+
+class Conversation:
+    def __init__(
+        self,
+        message_template=DEFAULT_MESSAGE_TEMPLATE,
+        system_prompt=DEFAULT_SYSTEM_PROMPT,
+        start_token_id=1,
+        bot_token_id=9225
+    ):
+        self.message_template = message_template
+        self.start_token_id = start_token_id
+        self.bot_token_id = bot_token_id
+        self.messages = [{
+            "role": "system",
+            "content": system_prompt
+        }]
+
+    def get_start_token_id(self):
+        return self.start_token_id
+
+    def get_bot_token_id(self):
+        return self.bot_token_id
+
+    def add_user_message(self, message):
+        self.messages.append({
+            "role": "user",
+            "content": message
+        })
+
+    def add_bot_message(self, message):
+        self.messages.append({
+            "role": "bot",
+            "content": message
+        })
+
+    def get_prompt(self, tokenizer):
+        final_text = ""
+        for message in self.messages:
+            message_text = self.message_template.format(**message)
+            final_text += message_text
+        final_text += tokenizer.decode([self.start_token_id, self.bot_token_id])
+        return final_text.strip()
+
+def generate(model, tokenizer, prompt, generation_config):
+    data = tokenizer(prompt, return_tensors="pt")
+    data = {k: v.to(model.device) for k, v in data.items()}
+    output_ids = model.generate(
+        **data,
+        generation_config=generation_config
+    )[0]
+    output_ids = output_ids[len(data["input_ids"][0]):]
+    output = tokenizer.decode(output_ids, skip_special_tokens=True)
+    return output.strip()
+
+config = PeftConfig.from_pretrained(MODEL_NAME)
+
+# Use GPU if available, else fall back to CPU
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    load_in_8bit=True,
+    torch_dtype=torch.float16,
+    device_map=device
+)
+model = PeftModel.from_pretrained(
+    model,
+    MODEL_NAME,
+    torch_dtype=torch.float16
+)
+model.eval()
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, use_fast=False)
+generation_config = GenerationConfig.from_pretrained(MODEL_NAME)
+
+@app.route('/run_inference', methods=['POST'])
+def run_inference():
+    try:
+        data = request.json
+        inputs = data.get('inputs', [])
+
+        conversation = Conversation()
+        outputs = []
+
+        for inp in inputs:
+            conversation.add_user_message(inp)
+            prompt = conversation.get_prompt(tokenizer)
+            output = generate(model, tokenizer, prompt, generation_config)
+            outputs.append({'input': inp, 'output': output})
+
+        return jsonify(outputs)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+if __name__ == '__main__':
+    app.run(port=7860)