Feat: Swap to GenerationConfig

scripts/finetune.py

@@ -12,6 +12,7 @@ from typing import Any, Dict, List, Optional, Union
 import fire
 import torch
 import yaml
+from transformers import GenerationConfig
 
 from axolotl.utils.data import load_prepare_datasets
 from axolotl.utils.dict import DictDefault
@@ -73,26 +74,33 @@ def do_inference(cfg, model, tokenizer, prompter="AlpacaPrompter"):
         instruction = get_multi_line_input()
         if not instruction:
             return
-        prompt: str = next(prompter_module().build_prompt(instruction=instruction))
+        prompt: str = next(
+            prompter_module().build_prompt(instruction=instruction.strip("\n"))
+        )
         batch = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
 
         model.eval()
         with torch.no_grad():
-            # gc = GenerationConfig()  # TODO swap out and use this
-            generated = model.generate(
-                inputs=batch["input_ids"].to(cfg.device),
-                do_sample=True,
-                use_cache=True,
+            generation_config = GenerationConfig(
                 repetition_penalty=1.1,
                 max_new_tokens=100,
                 temperature=0.9,
                 top_p=0.95,
                 top_k=40,
+                bos_token_id=tokenizer.bos_token_id,
+                eos_token_id=tokenizer.eos_token_id,
+                pad_token_id=tokenizer.pad_token_id,
+                do_sample=True,
+                use_cache=True,
                 return_dict_in_generate=True,
                 output_attentions=False,
                 output_hidden_states=False,
                 output_scores=False,
             )
+            generated = model.generate(
+                inputs=batch["input_ids"].to(cfg.device),
+                generation_config=generation_config,
+            )
         print(tokenizer.decode(generated["sequences"].cpu().tolist()[0]))