Delete dpo.py

dpo.py

@@ -1,99 +0,0 @@
-import torch
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from urllib.parse import unquote_plus
-import os
-
-from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, Trainer, TrainingArguments, BitsAndBytesConfig, \
-    DataCollatorForLanguageModeling, Trainer, TrainingArguments
-from transformers import BitsAndBytesConfig
-from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training
-
-nf4_config = BitsAndBytesConfig(
-   load_in_4bit=True,
-   bnb_4bit_quant_type="nf4",
-   bnb_4bit_use_double_quant=True,
-   bnb_4bit_compute_dtype=torch.bfloat16
-)
-
-# Carregar o modelo e o tokenizador na GPU
-device = "cuda:0"
-model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
-model = AutoModelForCausalLM.from_pretrained(model_id,quantization_config=nf4_config,device_map="auto",local_files_only=False,trust_remote_code=True)
-tokenizer = AutoTokenizer.from_pretrained(model_id, use_default_system_prompt=False)
-if tokenizer.pad_token is None:
-    tokenizer.pad_token = tokenizer.eos_token
-print(model)
-from transformers import AutoModelForCausalLM
-from datasets import load_dataset
-from trl import *
-
-# jondurbin/truthy-dpo-v0.1
-
-def return_prompt_and_responses(samples) :
-    return {
-        "prompt": [
-            "Question: " + question + "\n\nAnswer: "
-            for question in samples["prompt"]
-        ],
-        "chosen": samples["chosen"],   # rated better than k
-        "rejected": samples["rejected"], # rated worse than j
-    }
-
-dataset = load_dataset(
-    "jondurbin/truthy-dpo-v0.1",
-    split="train",
-    #data_dir="data/rl"
-)
-original_columns = dataset.column_names
-
-dataset.map(
-    return_prompt_and_responses,
-    batched=True,
-    remove_columns=original_columns
-)
-
-
-model = prepare_model_for_kbit_training(model)
-
-peft_config = LoraConfig(
- r=128,
- lora_alpha=16,
- target_modules=["q_proj","k_proj","v_proj","o_proj", "up_proj","gate_proj","down_proj", "lm_head"],
- lora_dropout=0.05,
- bias="none",
- task_type="CAUSAL_LM",
-)
-output_dir = "./odp"
-training_args = TrainingArguments(
- per_device_train_batch_size=1,
- gradient_accumulation_steps=1,
- gradient_checkpointing =True,
- max_grad_norm= 0.3,
- optim='adafactor',
- overwrite_output_dir=True,save_steps=100,
- num_train_epochs=1,
- learning_rate=2e-4,
- bf16=True,
- save_total_limit=3,
- logging_steps=10,
- output_dir=output_dir,
- lr_scheduler_type="cosine",
- warmup_ratio=0.05,
-)
-
-dpo_trainer = DPOTrainer(
- model,
- #model_ref,
- args=training_args,
- peft_config=peft_config,
- beta=0.1,
- train_dataset=dataset,
- tokenizer=tokenizer,
- max_prompt_length=1024,
- max_length=2048,
-)
-
-dpo_trainer.train()
-
-
-