README.md

---
license: apache-2.0
pipeline_tag: text-generation
---
KUETLLM is a [zephyr7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-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](https://github.com/TimDettmers/bitsandbytes). [LORA](https://huggingface.co/docs/diffusers/main/en/training/lora) was used to finetune an adapter, which was leter merged with the base unquantized model.

Below is the training configuarations for the finetuning 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))
```