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--- |
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license: cc-by-nc-2.0 |
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datasets: |
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- cosimoiaia/Loquace-102k |
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language: |
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- it |
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pipeline_tag: conversational |
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tags: |
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- alpaca |
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- llama |
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- llm |
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- finetune |
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- Italian |
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- qlora |
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--- |
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Model Card for Loquace-7B |
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# 🇮🇹 Loquace-7B 🇮🇹 |
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An exclusively Italian speaking, instruction finetuned, Large Language model. 🇮🇹 |
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The Loquace Italian LLM models are created as a proof-of-concept to evaluate on how language tuning can be achieved using QLoRa by instruct-tunings foundational LLMs |
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using dataset of a specific language. |
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The QLoRa (https://github.com/artidoro/qlora) method of fine-tuning significantly lower the resources requirements compared to any other methods available, |
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this allow to easily execute the process on significanly larger dataset while still using consumers GPUs and still achieve high accuracy. |
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## Model Description |
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Loquace-7B is the first 7B italian Large Language Model trained using QLoRa on a large dataset of 102k question/answer pairs |
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exclusively in Italian and that uses Falcon-7B model as base, the most accurate model of it's class. |
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The related code can be found at: |
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https://github.com/cosimoiaia/Loquace |
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Loquace-7B is part of the big Loquace family: |
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https://huggingface.co/cosimoiaia/Loquace-70m - Based on pythia-70m |
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https://huggingface.co/cosimoiaia/Loquace-410m - Based on pythia-410m |
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https://huggingface.co/cosimoiaia/Loquace-7B - Based on Falcon-7B |
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https://huggingface.co/cosimoiaia/Loquace-12B - Based on pythia-12B |
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https://huggingface.co/cosimoiaia/Loquace-20B - Based on gpt-neox-20B |
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## Usage |
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```python |
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from transformers import ( |
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AutoTokenizer, |
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AutoModelForCausalLM, |
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BitsAndBytesConfig |
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) |
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tokenizer = AutoTokenizer.from_pretrained("cosimoiaia/Loquace-7B", padding_side="right", use_fast=True) |
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model = AutoModelForCausalLM.from_pretrained( |
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"cosimoiaia/Loquace-7B", |
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load_in_8bit=True, |
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device_map="auto", |
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quantization_config=BitsAndBytesConfig( |
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load_in_4bit=True, |
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llm_int8_has_fp16_weight=False |
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) |
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) |
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``` |
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## Training |
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Loquace-7B was trained on a conversational dataset comprising 102k question/answer pairs in Italian language. |
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The training data was constructed by putting together translations from the original alpaca Dataset and other sources like the OpenAssistant dataset. |
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The model was trained for only 3000 iterations and took 16 hours on a single RTX 3090, kindly provided by Genesis Cloud. (https://gnsiscld.co/26qhlf) |
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## Limitations |
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- Loquace-7B may not handle complex or nuanced queries well and may struggle with ambiguous or poorly formatted inputs. |
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- The model may generate responses that are factually incorrect or nonsensical. It should be used with caution, and outputs should be carefully verified. |
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- The training data primarily consists of conversational examples and may not generalize well to other types of tasks or domains. |
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## Dependencies |
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- PyTorch |
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- Transformers library by Hugging Face |
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- Bitsandbites |
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- QLoRa |
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