text stringlengths 1 93.6k |
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elif quant8:
|
quant_config = BitsAndBytesConfig(
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load_in_8bit=True,
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bnb_8bit_quant_type="qat8",
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bnb_8bit_compute_dtype=getattr(torch, "float32"),
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bnb_8bit_use_double_quant=False
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)
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# Load base model
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model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=quant_config, device_map={"": 0}, trust_remote_code=True)
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else:
|
# Set up quantization config
|
quant_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=getattr(torch, "float16"),
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bnb_4bit_use_double_quant=True,
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)
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# Load base model
|
model = AutoModelForCausalLM.from_pretrained(base_model, quantization_config=quant_config, device_map={"": 0}, trust_remote_code=True)
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model.config.use_cache = False
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model.config.pretraining_tp = 1
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# Load base tokenizer
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tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)
|
# Just like Alpaca, because we allow to add history in the prompts, it makes more sense to do left-padding to have the most informative text at the end.
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# In this case, we need a different pad token than EOS because we actually do _not_ pad end of sentence.
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if padding == 'left':
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tokenizer.pad_token_id = 0
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else:
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tokenizer.pad_token_id = tokenizer.eos_token_id
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tokenizer.padding_side = padding
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# Set up LoRA configuration
|
peft_params = LoraConfig(
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lora_alpha=16,
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lora_dropout=0.1,
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r=64,
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bias="none",
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task_type="CAUSAL_LM",
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target_modules='all-linear',
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)
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# Pass quant and lora to trainer
|
training_params = TrainingArguments(
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num_train_epochs=num_train_epochs,
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per_device_train_batch_size=per_device_train_batch_size,
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gradient_accumulation_steps=1,
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gradient_checkpointing=True,
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learning_rate=5e-5,
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lr_scheduler_type="cosine",
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max_steps=max_steps,
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save_strategy="no",
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logging_steps=1,
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output_dir="./results",
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optim="paged_adamw_32bit",
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warmup_steps=100,
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bf16=True,
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report_to=None,
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remove_unused_columns=False
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)
|
trainer = DPOTrainer(
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model=model,
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ref_model=None,
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args=training_params,
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train_dataset=prompts['train'],
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tokenizer=tokenizer,
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peft_config=peft_params,
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beta=0.1,
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max_prompt_length=max_prompt_length,
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max_length=max_seq_length,
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)
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# Before starting training, free up memory
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torch.cuda.empty_cache()
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# Train the DPO model
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trainer.train()
|
# Check if output location is a valid directory
|
print(f"[---- LLaMa2Lang ----] Writing model and tokenizer out to {tuned_model}")
|
if os.path.isdir(tuned_model):
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trainer.model.save_to_disk(tuned_model)
|
trainer.tokenizer.save_to_disk(tuned_model)
|
else:
|
# Try to push to hub, requires HF_TOKEN environment variable to be set, see https://huggingface.co/docs/huggingface_hub/package_reference/environment_variables#hftoken
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trainer.model.push_to_hub(tuned_model)
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trainer.tokenizer.push_to_hub(tuned_model)
|
if __name__ == "__main__":
|
main()
|
# <FILESEP>
|
from binaryninja import *
|
import angr
|
import re
|
from pprint import pprint
|
# from main import Main
|
from binaryninja.architecture import Architecture, ArchitectureHook
|
from binaryninja.enums import *
|
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