Delete my.py

my.py

@@ -1,70 +0,0 @@
-import os
-from typing import Optional
-
-import fire
-import torch
-import transformers
-
-from utils.modeling_hack import get_model
-from utils.streaming import generate_stream
-
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-tok_ins = "\n\n### Instruction:\n"
-tok_res = "\n\n### Response:\n"
-prompt_input = tok_ins + "{instruction}" + tok_res
-
-
-def main(
-        model_path: str,
-        max_input_length: int = 512,
-        max_generate_length: int = 1024,
-        model_type: str = 'chat',
-        rope_scaling: Optional[str] = None,
-        rope_factor: float = 8.0,
-        streaming: bool = True  # streaming is always enabled now
-):
-    assert transformers.__version__.startswith('4.34')
-    assert model_type.lower() in ['chat', 'base'], f"model_type must be one of ['chat', 'base'], got {model_type}"
-    assert rope_scaling in [None, 'yarn',
-                            'dynamic'], f"rope_scaling must be one of [None, 'yarn', 'dynamic'], got {rope_scaling}"
-
-    model, tokenizer, generation_config = get_model(model_path=model_path, rope_scaling=rope_scaling,
-                                                    rope_factor=rope_factor)
-    generation_config.max_new_tokens = max_generate_length
-    generation_config.max_length = max_input_length + max_generate_length
-
-    device = torch.cuda.current_device()
-    sess_text = ""
-    while True:
-        raw_text = input("prompt(\"exit\" to end, \"clear\" to clear session) >>> ")
-        if not raw_text:
-            print('prompt should not be empty!')
-            continue
-        if raw_text.strip() == "exit":
-            print('session ended.')
-            break
-        if raw_text.strip() == "clear":
-            print('session cleared.')
-            sess_text = ""
-            continue
-
-        query_text = raw_text.strip()
-        sess_text += tok_ins + query_text
-        if model_type == 'chat':
-            input_text = prompt_input.format_map({'instruction': sess_text.split(tok_ins, 1)[1]})
-        else:
-            input_text = query_text
-        inputs = tokenizer(input_text, return_tensors='pt', truncation=True, max_length=max_input_length)
-        inputs = {k: v.to(device) for k, v in inputs.items()}
-
-        print('=' * 100)
-        for text in generate_stream(model, tokenizer, inputs['input_ids'], inputs['attention_mask'],
-                                    generation_config=generation_config):
-            print(text, end='', flush=True)
-        print('')
-        print("=" * 100)
-
-
-if __name__ == "__main__":
-    fire.Fire(main)