Delete alpacatrain.py

alpacatrain.py

@@ -1,59 +0,0 @@
-from datasets import load_dataset
-from transformers import RwkvForCausalLM, GPTNeoXTokenizerFast, Trainer, TrainingArguments,DataCollatorForLanguageModeling
-
-MICRO_BATCH_SIZE = 8
-BATCH_SIZE = 128
-GRADIENT_ACCUMULATION_STEPS = BATCH_SIZE // MICRO_BATCH_SIZE
-EPOCHS = 100
-LEARNING_RATE = 2e-5
-CUTOFF_LEN = 256
-
-model = RwkvForCausalLM.from_pretrained("rwkv-430M-pile").to("cuda")
-tokenizer = GPTNeoXTokenizerFast.from_pretrained("rwkv-430M-pile", add_special_tokens=True)
-# model = RwkvForCausalLM.from_pretrained("rwkv-7b-pile")
-# tokenizer = GPTNeoXTokenizerFast.from_pretrained("rwkv-7b-pile", add_special_tokens=True)
-tokenizer.pad_token = tokenizer.eos_token
-tokenizer.pad_token_id = tokenizer.eos_token_id
-
-data = load_dataset("json", data_files="test.json")
-
-def generate_prompt(data_point):
-    
-    return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
-
-### Instruction:
-{data_point["instruction"]}
-
-### Response:
-{data_point["output"]}"""
-
-
-data = data.shuffle().map(
-    lambda data_point: tokenizer(
-        generate_prompt(data_point),
-        truncation=True,
-        max_length=CUTOFF_LEN,
-        padding="max_length",
-    )
-)
-
-trainer = Trainer(
-    model=model,
-    train_dataset=data["train"],
-    args=TrainingArguments(
-        per_device_train_batch_size=MICRO_BATCH_SIZE,
-        gradient_accumulation_steps=GRADIENT_ACCUMULATION_STEPS,
-        warmup_steps=100,
-        num_train_epochs=EPOCHS,
-        learning_rate=LEARNING_RATE,
-        fp16=True,
-        logging_steps=1,
-        output_dir="rwkv-alpaca",
-        save_total_limit=3,
-    ),
-    data_collator=DataCollatorForLanguageModeling(tokenizer, mlm=False),
-)
-model.config.use_cache = False
-trainer.train(resume_from_checkpoint=False)
-
-model.save_pretrained("rwkv-alpaca")