How to fix RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
I am trying to finetune a model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ. However, I am getting RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn error.How can I fix this error?Here is the code that is throwing the error:import transformers
from datetime import datetime

project = "Mixtral-alpaca-finance-finetune"
base_model_name = "mixtral"
run_name = base_model_name + "-" + project
output_dir = "./" + run_name

tokenizer.pad_token = tokenizer.eos_token

trainer = transformers.Trainer(
    model=model,
    train_dataset=tokenized_train_dataset,
    eval_dataset=tokenized_val_dataset,
    #dataset_text_field="text",
    #max_seq_length=512,
    args=transformers.TrainingArguments(
        output_dir=output_dir,
        warmup_steps=5,
        per_device_train_batch_size=1,
        gradient_checkpointing=True,
        gradient_accumulation_steps=4,
        max_steps=1000,
        learning_rate=2.5e-5,
        lr_scheduler_type="cosine",
        logging_steps=25,
        fp16=True,
        optim="paged_adamw_8bit",
        logging_dir="./logs",        # Directory for storing logs
        save_strategy="steps",       # Save the model checkpoint every logging step
        save_steps=50,                # Save checkpoints every 50 steps
        evaluation_strategy="steps", # Evaluate the model every logging step
        eval_steps=50,               # Evaluate and save checkpoints every 50 steps
        do_eval=True,                # Perform evaluation at the end of training
        # report_to="wandb",           # Comment this out if you don't want to use weights & baises
        run_name=f"{run_name}-{datetime.now().strftime('%Y-%m-%d-%H-%M')}"          # Name of the W&B run (optional)
),
    data_collator=transformers.DataCollatorForLanguageModeling(tokenizer, mlm=False),

)

model.config.use_cache = False  # silence the warnings. Re-enable for inference!
trainer.train()I tried to upgrade the torch, accelerate auto-gptq but it still did not work.
2024-06-10T07:47:28Z
Hi,One cannot fine-tune quantized models directly, one can only leverage them in a Q-LoRa setting (where you freeze and quantize the base model and train adapter layers on top).TheTheBloke/Mistral-7B-Instruct-v0.1-GPTQmodel leverages the QPTQ quantization method (which is one of the many ways to shrink down the size of a model, also called quantizing a model, in the Transformers library):Quantization.I’d recommend the Q-LoRa blog if you want to know more about it:Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA.The model can only be fine-tuned in half, mixed or full precision. See my notebook regarding that:Transformers-Tutorials/Mistral/Supervised_fine_tuning_(SFT)_of_an_LLM_using_Hugging_Face_tooling.ipynb at master · NielsRogge/Transformers-Tutorials · GitHub