Fine-tune SafeTensors format model

[icy kiln]

the safetensors format is not a big issue. It's effectively a special pytorch .pt or .ckpt format. The primary difference between those classic formats and the safetensor format is that former allowed for unchecked code execution within the binary (which was a security issue). safetensors does not allow for such things, so it is preferred.

If you are using huggingface transformers or diffusers for a task, you should be able to read a safetensors file just as you would with a regular pytorch file.

[modest meadow]

Fine-tuning a large SafeTensors format model (~17GB) on a custom dataset with an A100 is feasible, but there are important considerations to ensure efficient training and to keep the model size within your 20GB limit, I think.

Here’s a simple, high-level guide with tips

1. Steps to Fine-Tune SafeTensors Model
   Load the Pre-Trained Model (SafeTensors Format)
   SafeTensors models are typically used with frameworks like Hugging Face Transformers. You can load the model like this:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "path/to/your/safetensors/model"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)

If the model weights are stored in SafeTensors format, ensure that your Hugging Face library version supports it (version 4.25.0 or later).

2. Prepare the Dataset
   Tokenize your dataset using the same tokenizer as the pre-trained model:

from datasets import load_dataset

dataset = load_dataset("path/to/your/custom_dataset")
def tokenize_function(examples):
return tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
tokenized_dataset = dataset.map(tokenize_function, batched=True)

3. Set Up Efficient Fine-Tuning (LoRA or PEFT)
   Since your goal is to fine-tune the model without exceeding 20GB, you should use parameter-efficient fine-tuning techniques like LoRA (Low-Rank Adaptation) or PEFT (Parameter-Efficient Fine-Tuning). These methods significantly reduce the number of trainable parameters, keeping the final model size small.

Using PEFT with LoRA:

from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
r=16, # Rank (smaller values reduce memory usage)
lora_alpha=32, # Scaling factor
target_modules=["q_proj", "v_proj"], # Apply LoRA to attention layers
lora_dropout=0.1,
bias="none"
)
peft_model = get_peft_model(model, lora_config)