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--- |
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license: apache-2.0 |
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pipeline_tag: text-generation |
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--- |
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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. |
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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. |
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Below is the training configuarations for the finetuning process: |
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``` |
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LoraConfig: |
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r=16, |
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lora_alpha=16, |
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target_modules=["q_proj", "v_proj","k_proj","o_proj","gate_proj","up_proj","down_proj"], |
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lora_dropout=0.05, |
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bias="none", |
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task_type="CAUSAL_LM" |
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``` |
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``` |
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TrainingArguments: |
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per_device_train_batch_size=12, |
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gradient_accumulation_steps=1, |
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optim='paged_adamw_8bit', |
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learning_rate=5e-06 , |
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fp16=True, |
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logging_steps=10, |
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num_train_epochs = 1, |
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output_dir=zephyr_lora_output, |
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remove_unused_columns=False, |
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``` |
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## Inferencing: |
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``` |
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def process_data_sample(example): |
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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" |
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return processed_example |
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inp_str = process_data_sample("Tell me about KUET.") |
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inputs = tokenizer(inp_str, return_tensors="pt") |
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generation_config = GenerationConfig( |
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do_sample=True, |
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top_k=1, |
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temperature=0.1, |
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max_new_tokens=256, |
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pad_token_id=tokenizer.eos_token_id |
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) |
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outputs = model.generate(**inputs, generation_config=generation_config) |
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print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
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``` |
