Open noraise opened 6 months ago
I'm having the same problem. Is this the correct way to load the fine-tuned model? Is there no need to merge the lora adapter?
I get the following error after finetuning this model on the R dataset following the example in the README.
Some weights of the model checkpoint at finetune_starcoder2/final_checkpoint were not used when initializing Starcoder2ForCausalLM: ['model.layers.0.self_attn.k_proj.base_layer.bias', 'model.layers.0.self_attn.k_proj.base_layer.weight', 'model.layers.0.self_attn.k_proj.base_layer.weight.absmax', 'model.layers.0.self_attn.k_proj.base_layer.weight.quant_map', 'model.layers.0.self_attn.k_proj.base_layer.weight.quant_state.bitsandbytes__nf4', 'model.layers.0.self_attn.k_proj.lora_A.default.weight', 'model.layers.0.self_attn.k_proj.lora_B.default.weight', 'model.layers.0.self_attn.o_proj.base_layer.bias', 'model.layers.0.self_attn.o_proj.base_layer.weight', 'model.layers.0.self_attn.o_proj.base_layer.weight.absmax', 'model.layers.0.self_attn.o_proj.base_layer.weight.quant_map', 'model.layers.0.self_attn.o_proj.base_layer.weight.quant_state.bitsandbytes__nf4', 'model.layers.0.self_attn.o_proj.lora_A.default.weight', 'model.layers.0.self_attn.o_proj.lora_B.default.weight', 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Some weights of Starcoder2ForCausalLM were not initialized from the model checkpoint at finetune_starcoder2/final_checkpoint and are newly initialized: ['model.layers.0.self_attn.k_proj.bias', 'model.layers.0.self_attn.k_proj.weight', 'model.layers.0.self_attn.o_proj.bias', 'model.layers.0.self_attn.o_proj.weight', 'model.layers.0.self_attn.q_proj.bias', 'model.layers.0.self_attn.q_proj.weight', 'model.layers.0.self_attn.v_proj.bias', 'model.layers.0.self_attn.v_proj.weight', 'model.layers.1.self_attn.k_proj.bias', 'model.layers.1.self_attn.k_proj.weight', 'model.layers.1.self_attn.o_proj.bias', 'model.layers.1.self_attn.o_proj.weight', 'model.layers.1.self_attn.q_proj.bias', 'model.layers.1.self_attn.q_proj.weight', 'model.layers.1.self_attn.v_proj.bias', 'model.layers.1.self_attn.v_proj.weight', 'model.layers.10.self_attn.k_proj.bias', 'model.layers.10.self_attn.k_proj.weight', 'model.layers.10.self_attn.o_proj.bias', 'model.layers.10.self_attn.o_proj.weight', 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'model.layers.8.self_attn.v_proj.bias', 'model.layers.8.self_attn.v_proj.weight', 'model.layers.9.self_attn.k_proj.bias', 'model.layers.9.self_attn.k_proj.weight', 'model.layers.9.self_attn.o_proj.bias', 'model.layers.9.self_attn.o_proj.weight', 'model.layers.9.self_attn.q_proj.bias', 'model.layers.9.self_attn.q_proj.weight', 'model.layers.9.self_attn.v_proj.bias', 'model.layers.9.self_attn.v_proj.weight'] You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference. The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig # to use 4bit use `load_in_4bit=True` instead quantization_config = BitsAndBytesConfig() checkpoint = "bigcode/starcoder2-3b" tokenizer = AutoTokenizer.from_pretrained(checkpoint) model = AutoModelForCausalLM.from_pretrained("finetune_starcoder2/final_checkpoint", quantization_config=quantization_config) inputs = tokenizer.encode("hello_world_function <- function() {", return_tensors="pt").to("cuda") outputs = model.generate(inputs) print(tokenizer.decode(outputs[0]))
The expected output of the fine tuning process should be the peft adapter weights, instead of the whole model. Once the training process is finished, you'll see that under the final_checkpoint there are the safetensors of the model, but if you check the size is similar to the original model, while the adapter should be in the order of MB. I guess something is broken with one of the libraries.
If you check the object type you'll notice the problem:
print("Training...")
trainer.train()
print("Saving the last checkpoint of the model")
print("Original model type:", type(model))
print("Trained model type:", type(trainer.model))
The output for the model type is:
Saving the last checkpoint of the model
Original model type: <class 'transformers.models.starcoder2.modeling_starcoder2.Starcoder2ForCausalLM'>
Trained model type: <class 'peft.peft_model.PeftModelForCausalLM'>
Training Done! 💥
I've temporarily fixed this issue by changing the next line (although I'm not sure if it's totally correct):
model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
to:
trainer.model.save_pretrained(os.path.join(args.output_dir, "final_checkpoint/"))
Then, I can use the merge_peft_adapters.py from StarCoder's repository and do inference.
Hey, nice work! How’s the performance? I am wondering if it’s even worth to do this.
Hey, nice work! How’s the performance? I am wondering if it’s even worth to do this.
In my case is a must, since I'm doing Instruction Fine-tuning and the performance of the fine-tuned model is good as expected.
I get the following error after finetuning this model on the R dataset following the example in the README.
Also, I don't think doing 4-bit quantization as a default for finetuning is a good idea. It should be opt-in with a flag.
I am also wondering why do we use the Stack v1 for finetuning and not the Stack v2?