Create README.md

README.md

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+---
+license: apache-2.0
+---
+
+this is a demo how fine tune phi-2 model.
+
+```
+import torch
+import datasets
+from transformers import TrainingArguments, AutoConfig, AutoTokenizer, AutoModelForCausalLM
+import trl
+from transformers import BitsAndBytesConfig
+
+train_dataset = datasets.load_dataset('HuggingFaceTB/cosmopedia-20k', split='train')
+
+args = TrainingArguments(
+    output_dir="./test-sft",
+    max_steps=20000,
+    per_device_train_batch_size=1,
+    optim="adafactor", report_to="none",
+)
+
+model_id = "microsoft/phi-2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+nf4_config = BitsAndBytesConfig(
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=nf4_config,device_map="auto")
+print(model)
+
+from peft import LoraConfig
+
+peft_config = LoraConfig(
+    lora_alpha=16,
+    lora_dropout=0.1,
+    r=64, target_modules=["q_proj", "v_proj", "k_proj", "dense", "lm_head", "fc1", "fc2"],
+    bias="none", 
+    task_type="CAUSAL_LM",
+)
+model.add_adapter(peft_config)
+
+trainer = trl.SFTTrainer(
+    model=model, 
+    args=args,
+    train_dataset=train_dataset,
+    dataset_text_field='text',
+    max_seq_length=1024
+)
+
+trainer.train()
+
+trainer.model.save_pretrained("sft", dtype=torch.bfloat16)
+trainer.tokenizer.save_pretrained("sft")
+
+```