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Model: hyperspaceai/hyperEngine_phi3_128k
Source: Original Platform

sample_finetune.py

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+import torch
+from datasets import load_dataset
+from trl import SFTTrainer
+from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments
+
+"""
+A simple example on using SFTTrainer and Accelerate to finetune Phi-3 models. For
+a more advanced example, please follow HF alignment-handbook/scripts/run_sft.py
+
+1. Install accelerate: 
+    conda install -c conda-forge accelerate
+2. Setup accelerate config:
+    accelerate config
+to simply use all the GPUs available:
+    python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='bf16')"
+check accelerate config:
+    accelerate env
+3. Run the code:
+    accelerate launch sample_finetune.py
+"""
+
+###################
+# Hyper-parameters
+###################
+args = {
+    "bf16": True,
+    "do_eval": False,
+    "learning_rate": 5.0e-06,
+    "log_level": "info",
+    "logging_steps": 20,
+    "logging_strategy": "steps",
+    "lr_scheduler_type": "cosine",
+    "num_train_epochs": 1,
+    "max_steps": -1,
+    "output_dir": "./checkpoint_dir",
+    "overwrite_output_dir": True,
+    "per_device_eval_batch_size": 4,
+    "per_device_train_batch_size": 8,
+    "remove_unused_columns": True,
+    "save_steps": 100,
+    "save_total_limit": 1,
+    "seed": 0,
+    "gradient_checkpointing": True,
+    "gradient_checkpointing_kwargs":{"use_reentrant": False},
+    "gradient_accumulation_steps": 1,
+    "warmup_ratio": 0.2,
+    }
+    
+training_args = TrainingArguments(**args)
+
+
+################
+# Modle Loading
+################
+checkpoint_path = "microsoft/Phi-3-mini-4k-instruct"
+# checkpoint_path = "microsoft/Phi-3-mini-128k-instruct"
+model_kwargs = dict(
+    use_cache=False,
+    trust_remote_code=True,
+    attn_implementation="flash_attention_2",  # loading the model with flash-attenstion support
+    torch_dtype=torch.bfloat16,
+    device_map="cuda",
+)
+model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)
+tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation
+tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)
+tokenizer.padding_side = 'right'
+
+##################
+# Data Processing
+##################
+def apply_chat_template(
+    example,
+    tokenizer,
+):
+    messages = example["messages"]
+    # Add an empty system message if there is none
+    if messages[0]["role"] != "system":
+        messages.insert(0, {"role": "system", "content": ""})
+    example["text"] = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=False)
+    return example
+
+raw_dataset = load_dataset("HuggingFaceH4/ultrachat_200k")
+column_names = list(raw_dataset["train_sft"].features)
+
+processed_dataset = raw_dataset.map(
+    apply_chat_template,
+    fn_kwargs={"tokenizer": tokenizer},
+    num_proc=12,
+    remove_columns=column_names,
+    desc="Applying chat template",
+)
+train_dataset = processed_dataset["train_sft"]
+eval_dataset = processed_dataset["test_sft"]
+
+
+###########
+# Training
+###########
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    max_seq_length=2048,
+    dataset_text_field="text",
+    tokenizer=tokenizer,
+    packing=True
+)
+train_result = trainer.train()
+metrics = train_result.metrics
+trainer.log_metrics("train", metrics)
+trainer.save_metrics("train", metrics)
+trainer.save_state()
+
+#############
+# Evaluation
+#############
+tokenizer.padding_side = 'left'
+metrics = trainer.evaluate()
+metrics["eval_samples"] = len(eval_dataset)
+trainer.log_metrics("eval", metrics)
+trainer.save_metrics("eval", metrics)
+
+############
+# Save model
+############
+trainer.save_model(training_args.output_dir)