mistral 7b finetuning code added - Adithya S K

LLM/Mistral-7b/SFT.py

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+# This is a modified version of TRL's `SFTTrainer` example (https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py), 
+# adapted to run with DeepSpeed ZeRO-3 and Mistral-7B-V1.0. The settings below were run on 1 node of 8 x A100 (80GB) GPUs.
+#
+# Usage:
+#   - Install the latest transformers & accelerate versions: `pip install -U transformers accelerate`
+#   - Install deepspeed: `pip install deepspeed==0.9.5`
+#   - Install TRL from main: pip install git+https://github.com/huggingface/trl.git
+#   - Clone the repo: git clone github.com/huggingface/trl.git
+#   - Copy this Gist into trl/examples/scripts
+#   - Run from root of trl repo with: accelerate launch --config_file=examples/accelerate_configs/deepspeed_zero3.yaml --gradient_accumulation_steps 8 examples/scripts/sft_trainer.py  
+from dataclasses import dataclass, field
+from typing import Optional
+
+import torch
+from accelerate import Accelerator
+from datasets import load_dataset
+from peft import LoraConfig
+from tqdm import tqdm
+from transformers import AutoModelForCausalLM, BitsAndBytesConfig, HfArgumentParser, TrainingArguments, AutoTokenizer
+
+from trl import SFTTrainer
+
+
+tqdm.pandas()
+
+
+# Define and parse arguments.
+@dataclass
+class ScriptArguments:
+    """
+    The name of the Casual LM model we wish to fine with SFTTrainer
+    """
+
+    model_name: Optional[str] = field(default="mistralai/Mistral-7B-v0.1", metadata={"help": "the model name"})
+    dataset_name: Optional[str] = field(
+        default="stingning/ultrachat", metadata={"help": "the dataset name"}
+    )
+    dataset_text_field: Optional[str] = field(default="text", metadata={"help": "the text field of the dataset"})
+    log_with: Optional[str] = field(default="wandb", metadata={"help": "use 'wandb' to log with wandb"})
+    learning_rate: Optional[float] = field(default=2.0e-5, metadata={"help": "the learning rate"})
+    batch_size: Optional[int] = field(default=8, metadata={"help": "the batch size"})
+    seq_length: Optional[int] = field(default=1024, metadata={"help": "Input sequence length"})
+    gradient_accumulation_steps: Optional[int] = field(
+        default=8, metadata={"help": "the number of gradient accumulation steps"}
+    )
+    load_in_8bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 8 bits precision"})
+    load_in_4bit: Optional[bool] = field(default=False, metadata={"help": "load the model in 4 bits precision"})
+    use_peft: Optional[bool] = field(default=False, metadata={"help": "Wether to use PEFT or not to train adapters"})
+    trust_remote_code: Optional[bool] = field(default=False, metadata={"help": "Enable `trust_remote_code`"})
+    output_dir: Optional[str] = field(default="output", metadata={"help": "the output directory"})
+    peft_lora_r: Optional[int] = field(default=64, metadata={"help": "the r parameter of the LoRA adapters"})
+    peft_lora_alpha: Optional[int] = field(default=16, metadata={"help": "the alpha parameter of the LoRA adapters"})
+    logging_steps: Optional[int] = field(default=5, metadata={"help": "the number of logging steps"})
+    use_auth_token: Optional[bool] = field(default=True, metadata={"help": "Use HF auth token to access the model"})
+    num_train_epochs: Optional[int] = field(default=3, metadata={"help": "the number of training epochs"})
+    max_steps: Optional[int] = field(default=-1, metadata={"help": "the number of training steps"})
+    save_steps: Optional[int] = field(
+        default=1000, metadata={"help": "Number of updates steps before two checkpoint saves"}
+    )
+    save_total_limit: Optional[int] = field(default=10, metadata={"help": "Limits total number of checkpoints."})
+    push_to_hub: Optional[bool] = field(default=True, metadata={"help": "Push the model to HF Hub"})
+    hub_model_id: Optional[str] = field(default="mistral-7b-finetuned-ultrachat", metadata={"help": "The name of the model on HF Hub"})
+
+
+parser = HfArgumentParser(ScriptArguments)
+script_args = parser.parse_args_into_dataclasses()[0]
+
+# Step 1: Load the dataset
+tokenizer = AutoTokenizer.from_pretrained(script_args.model_name)
+dataset = load_dataset(script_args.dataset_name, split="train[:20000]")
+dataset = dataset.train_test_split(test_size=0.1)
+
+def prepare_dialogue(example):
+    text = ""
+    for idx, msg in enumerate(example["data"]):
+        if idx % 2 == 0:
+            text += f"<|user|>\n{msg}{tokenizer.eos_token}\n"
+        else:
+            text += f"<|assistant|>\n{msg}{tokenizer.eos_token}\n"
+    example["text"] = text
+    return example
+
+dataset = dataset.map(prepare_dialogue, num_proc=4, remove_columns=["id", "data"])
+
+# Step 2: Load the model
+if script_args.load_in_8bit and script_args.load_in_4bit:
+    raise ValueError("You can't load the model in 8 bits and 4 bits at the same time")
+elif script_args.load_in_8bit or script_args.load_in_4bit:
+    quantization_config = BitsAndBytesConfig(
+        load_in_8bit=script_args.load_in_8bit, load_in_4bit=script_args.load_in_4bit
+    )
+    # Copy the model to each device
+    device_map = {"": Accelerator().local_process_index}
+    torch_dtype = torch.bfloat16
+else:
+    device_map = None
+    quantization_config = None
+    torch_dtype = None
+
+model = AutoModelForCausalLM.from_pretrained(
+    script_args.model_name,
+    quantization_config=quantization_config,
+    device_map=device_map,
+    trust_remote_code=script_args.trust_remote_code,
+    torch_dtype=torch_dtype,
+    use_auth_token=script_args.use_auth_token,
+)
+
+
+# Step 3: Define the training arguments
+training_args = TrainingArguments(
+    output_dir=script_args.output_dir,
+    per_device_train_batch_size=script_args.batch_size,
+    gradient_accumulation_steps=script_args.gradient_accumulation_steps,
+    gradient_checkpointing=True,
+    learning_rate=script_args.learning_rate,
+    logging_steps=script_args.logging_steps,
+    num_train_epochs=script_args.num_train_epochs,
+    max_steps=script_args.max_steps,
+    report_to=script_args.log_with,
+    save_steps=script_args.save_steps,
+    save_total_limit=script_args.save_total_limit,
+    push_to_hub=script_args.push_to_hub,
+    hub_model_id=script_args.hub_model_id,
+    bf16=True,
+    lr_scheduler_type="cosine",
+    warmup_ratio=0.1,
+    evaluation_strategy="epoch",
+    logging_first_step=True,
+
+)
+
+# Step 4: Define the LoraConfig
+if script_args.use_peft:
+    peft_config = LoraConfig(
+        r=script_args.peft_lora_r,
+        lora_alpha=script_args.peft_lora_alpha,
+        bias="none",
+        task_type="CAUSAL_LM",
+    )
+else:
+    peft_config = None
+
+
+# Step 5: Define the Trainer
+trainer = SFTTrainer(
+    model=model,
+    args=training_args,
+    max_seq_length=script_args.seq_length,
+    train_dataset=dataset["train"],
+    eval_dataset=dataset["test"],
+    dataset_text_field=script_args.dataset_text_field,
+    peft_config=peft_config,
+    packing=True,
+)
+
+trainer.train()
+
+# Step 6: Save the model
+trainer.save_model(script_args.output_dir)