vllm enabled eval for HF and fine-tuned models; code cleanup and refactoring for text2sql_eval; minimum eval packages for eval requirements; merge peft script to make vllm happy

end-to-end-use-cases/coding/text2sql/fine-tuning/merge_peft.py

@@ -0,0 +1,45 @@
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+peft_model_path = "../fine-tuning/final_test/llama31-8b-text2sql-peft-nonquantized-cot"
+output_dir = (
+    "../fine-tuning/final_test/llama31-8b-text2sql-peft-nonquantized-cot_merged"
+)
+# === Load Base Model and Tokenizer ===
+print("Loading base model and tokenizer...")
+base_model_id = "meta-llama/Llama-3.1-8B-Instruct"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+
+# Configure quantization if needed
+quantization_config = None
+use_quantized = False
+if use_quantized:
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+    )
+
+# Load model
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+    quantization_config=quantization_config,
+)
+base_model.resize_token_embeddings(128257)
+
+# === Load PEFT Adapter and Merge ===
+print("Loading PEFT adapter and merging...")
+# peft_config = PeftConfig.from_pretrained(peft_model_path)
+model = PeftModel.from_pretrained(base_model, peft_model_path)
+model = model.merge_and_unload()  # This merges the adapter weights into the base model
+
+# === Save the Merged Model ===
+print(f"Saving merged model to {output_dir} ...")
+model.save_pretrained(output_dir)
+tokenizer.save_pretrained(output_dir)
+
+print("Done! The merged model is ready for vLLM serving.")