Upload 2 files

causallm-to-hub.py

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+# Usage: python upload.py --dir <dir> --hub-name <hub_name>
+
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+import argparse
+
+
+def get_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--path", type=str)
+    parser.add_argument("--hub-name", type=str)
+    return parser.parse_args()
+
+
+def main():
+    args = get_args()
+    print(f"Args: {args}")
+    
+    print(f"Loading tokenizer from path: {args.path}")
+    tokenizer = AutoTokenizer.from_pretrained(args.path)
+    print(f"Pushing the tokenizer to the Hub at {args.hub_name}")
+    tokenizer.push_to_hub(args.hub_name, private=True)
+    
+    print(f"Loading model from path: {args.path}")
+    model = AutoModelForCausalLM.from_pretrained(
+        args.path,
+        return_dict=True,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+    print(f"Pushing the model to the Hub at {args.hub_name}")
+    model.push_to_hub(args.hub_name, private=True)
+    
+    from huggingface_hub import HfApi
+
+    api = HfApi()
+    for file in ["all_results.json", "eval_results.json"]:
+        try:
+            api.upload_file(
+                path_or_fileobj=f"{args.peft}/{file}",
+                path_in_repo=file,
+                repo_id=args.out,
+                repo_type="model",
+            )
+        except Exception as e:
+            print(f"Failed to upload {file}: {e}")
+
+
+if __name__ == "__main__":
+    main()

dpo-qlora-4bit.py

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+import torch
+from datasets import load_dataset
+from peft import LoraConfig, get_peft_model
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from trl import DPOTrainer
+
+
+if __name__ == "__main__":
+	model_name = "..."
+	dataset = load_dataset(...)
+	
+	tokenizer = AutoTokenizer.from_pretrained(model_name)
+	tokenizer.pad_token = tokenizer.eos_token
+
+	model = AutoModelForCausalLM.from_pretrained(
+		model_name,
+		low_cpu_mem_usage=True,
+		torch_dtype=torch.bfloat16,
+		load_in_4bit=True,
+		use_flash_attention_2=True,
+		bnb_4bit_compute_dtype=torch.bfloat16,
+		bnb_4bit_quant_type="nf4",
+	)
+	model.resize_token_embeddings(len(tokenizer))
+	model.config.pad_token_id = tokenizer.pad_token_id
+	model.config.use_cache = False
+
+	ref_model = AutoModelForCausalLM.from_pretrained(
+		model_name,
+		low_cpu_mem_usage=True,
+		torch_dtype=torch.bfloat16,
+		load_in_4bit=True,
+		use_flash_attention_2=True,
+		bnb_4bit_compute_dtype=torch.bfloat16,
+	).eval()
+
+	peft_config = LoraConfig(
+		lora_alpha=128,
+		lora_dropout=0.05,
+		r=64,
+		bias="none",
+		task_type="CAUSAL_LM",
+		target_modules=[
+			"q_proj",
+			"k_proj",
+			"v_proj",
+		],
+	)
+	model = get_peft_model(model, peft_config)
+
+	training_args = DPOConfig(
+		num_train_epochs=3,
+		learning_rate=5e-07,
+		per_device_train_batch_size=1,
+		do_eval=True,
+		per_device_eval_batch_size=1,
+		adam_epsilon=1e-08,
+		lr_scheduler_type="linear",
+		warmup_ratio=0.1,
+		seed=42,
+		logging_steps=100,
+		save_steps=500,
+		save_strategy="steps",
+		output_dir="./output-dir",
+		gradient_checkpointing=True,
+		bf16=True,
+		remove_unused_columns=False,
+	)
+
+	dpo_trainer = DPOTrainer(
+		model,
+		ref_model,
+		args=training_args,
+		beta=training_args.beta,
+		train_dataset=dataset["train"],
+		eval_dataset=dataset["test"],
+		tokenizer=tokenizer,
+		max_length=training_args.max_length,
+		max_prompt_length=training_args.max_prompt_length,
+		peft_config=peft_config,
+	)
+	dpo_trainer.train()
+	dpo_trainer.save_model()