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README.md
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---
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library_name: peft
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---
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: bfloat16
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### Framework versions
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- PEFT 0.4.0.dev0
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---
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library_name: peft
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---
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language: en
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thumbnail:
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tags:
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- peft
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- text-generation
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- chatbot
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- ecommerce
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- fine-tuned
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pipeline_tag: text-generation
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license: apache-2.0
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datasets:
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- kaggle
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metrics:
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---
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# Falcon 7B LLM Fine Tune Model
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## Model description
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This model is a fine-tuned version of the `tiiuae/falcon-7b` model using the QLoRa library and the PEFT library. It was fine-tuned on the [Ecommerce-FAQ-Chatbot-Dataset](https://kaggle.com/datasets/saadmakhdoom/ecommerce-faq-chatbot-dataset) from Kaggle.
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## Intended uses & limitations
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#### How to use
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
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import torch
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model_id = "hipnologo/Falcon-7B-FineTune-Chatbot"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# generate text
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input_prompt = "Hello, Bot!"
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input_ids = tokenizer.encode(input_prompt, return_tensors='pt')
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output = model.generate(input_ids)
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output_text = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
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## Training procedure
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The model was fine-tuned on the [Ecommerce-FAQ-Chatbot-Dataset](https://kaggle.com/datasets/saadmakhdoom/ecommerce-faq-chatbot-dataset) using the `bitsandbytes` quantization config:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: bfloat16
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### Framework versions
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- PEFT 0.4.0.dev0
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## Evaluation results
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The model was trained for 80 steps, with the training loss decreasing from 0.184 to nearly 0. The final training loss was 0.03094411873175886.
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- Trainable params: 2359296
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- All params: 3611104128
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- Trainable%: 0.06533447711203746
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## License
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This model is licensed under Apache 2.0. Please see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) for more information.
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