KUETLLM is a zephyr7b-beta finetune, using a dataset with prompts and answers about Khulna University of Engineering and Technology. It was loaded in 8 bit quantization using bitsandbytes. LORA was used to finetune an adapter, which was leter merged with the base unquantized model.
datasets:
- University information (collected from website, https://kuet.ac.bd/)
Below are the training configurations for the fine-tuning process:
LoraConfig:
r=16,
lora_alpha=16,
target_modules=["q_proj", "v_proj","k_proj","o_proj","gate_proj","up_proj","down_proj"],
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM"
TrainingArguments:
per_device_train_batch_size=12,
gradient_accumulation_steps=1,
optim='paged_adamw_8bit',
learning_rate=5e-06 ,
fp16=True,
logging_steps=10,
num_train_epochs = 1,
output_dir=zephyr_lora_output,
remove_unused_columns=False,
Inferencing:
def process_data_sample(example):
processed_example = "<|system|>\nYou are a KUET authority managed chatbot, help users by answering their queries about KUET.\n<|user|>\n" + example + "\n<|assistant|>\n"
return processed_example
inp_str = process_data_sample("Tell me about KUET.")
inputs = tokenizer(inp_str, return_tensors="pt")
generation_config = GenerationConfig(
do_sample=True,
top_k=1,
temperature=0.1,
max_new_tokens=256,
pad_token_id=tokenizer.eos_token_id
)
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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