
Model Card for Loquace-70m

# 🇮🇹 Loquace-70m 🇮🇹 

An exclusively Italian speaking, instruction finetuned, Large Language model. 🇮🇹

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 
using dataset of a specific language.


The QLoRa (https://github.com/artidoro/qlora) method of fine-tuning significantly lower the resources requirements compared to any other methods available, 
this allow to easily execute the process on significanly larger dataset while still using consumers GPUs and still achieve high accuracy.


## Model Description

Loquace-70m is the smallest model of the Loquace family. It was trained using QLoRa on a large dataset of 102k question/answer pairs
exclusively in Italian.

The related code can be found at: https://github.com/cosimoiaia/Loquace

Loquace-70m is part of the big Loquace family:

https://huggingface.co/cosimoiaia/Loquace-70m   -   Based on pythia-70m
https://huggingface.co/cosimoiaia/Loquace-410m  -   Based on pythia-410m
https://huggingface.co/cosimoiaia/Loquace-7B    -   Based on Falcon-7B.
https://huggingface.co/cosimoiaia/Loquace-12B   -   Based on pythia-12B
https://huggingface.co/cosimoiaia/Loquace-20B   -   Based on gpt-neox-20B



## Usage


```python
from transformers import (
    AutoTokenizer,
    AutoModelForCausalLM,
    BitsAndBytesConfig
)

tokenizer = AutoTokenizer.from_pretrained("cosimoiaia/Loquace-70m", padding_side="right", use_fast=True)
model = AutoModelForCausalLM.from_pretrained(
    "cosimoiaia/Loquace-70m",
    load_in_8bit=True,
    device_map="auto",
    quantization_config=BitsAndBytesConfig(
      load_in_4bit=True,
      llm_int8_has_fp16_weight=False
    )
)
```


## Training

Loquace-70m was trained on a conversational dataset comprising 102k question/answer pairs in Italian language. 
The training data was constructed by putting together translations from the original alpaca Dataset and other sources like the OpenAssistant dataset.
The model was trained for only 10000 iterations and took 6 hours on a single RTX 3090, kindly provided by Genesis Cloud. (https://gnsiscld.co/26qhlf)

## Limitations

- Loquace-70m may not handle complex or nuanced queries well and may struggle with ambiguous or poorly formatted inputs.
- The model may generate responses that are factually incorrect or nonsensical. It should be used with caution, and outputs should be carefully verified.
- The training data primarily consists of conversational examples and may not generalize well to other types of tasks or domains.

## Dependencies

- PyTorch
- Transformers library by Hugging Face
- Bitsandbites
- QLoRa 

{"language": ["it"], "license": "cc-by-nc-2.0", "tags": ["alpaca", "llama", "llm", "finetune", "Italian", "qlora"], "datasets": ["cosimoiaia/Loquace-102k"], "pipeline_tag": "conversational"}
transformers pytorch gpt_neox text-generation alpaca llama llm finetune Italian qlora conversational it dataset:cosimoiaia/Loquace-102k license:cc-by-nc-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
TAGS
#transformers #pytorch #gpt_neox #text-generation #alpaca #llama #llm #finetune #Italian #qlora #conversational #it #dataset-cosimoiaia/Loquace-102k #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 


Model Card for Loquace-70m

# 🇮🇹 Loquace-70m 🇮🇹 

An exclusively Italian speaking, instruction finetuned, Large Language model. 🇮🇹

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 
using dataset of a specific language.


The QLoRa (URL method of fine-tuning significantly lower the resources requirements compared to any other methods available, 
this allow to easily execute the process on significanly larger dataset while still using consumers GPUs and still achieve high accuracy.


## Model Description

Loquace-70m is the smallest model of the Loquace family. It was trained using QLoRa on a large dataset of 102k question/answer pairs
exclusively in Italian.

The related code can be found at: URL

Loquace-70m is part of the big Loquace family:

URL   -   Based on pythia-70m
URL  -   Based on pythia-410m
URL    -   Based on Falcon-7B.
URL   -   Based on pythia-12B
URL   -   Based on gpt-neox-20B



## Usage





## Training

Loquace-70m was trained on a conversational dataset comprising 102k question/answer pairs in Italian language. 
The training data was constructed by putting together translations from the original alpaca Dataset and other sources like the OpenAssistant dataset.
The model was trained for only 10000 iterations and took 6 hours on a single RTX 3090, kindly provided by Genesis Cloud. (URL

## Limitations

- Loquace-70m may not handle complex or nuanced queries well and may struggle with ambiguous or poorly formatted inputs.
- The model may generate responses that are factually incorrect or nonsensical. It should be used with caution, and outputs should be carefully verified.
- The training data primarily consists of conversational examples and may not generalize well to other types of tasks or domains.

## Dependencies

- PyTorch
- Transformers library by Hugging Face
- Bitsandbites
- QLoRa 

# 🇮🇹 Loquace-70m 🇮🇹 

An exclusively Italian speaking, instruction finetuned, Large Language model. 🇮🇹

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 
using dataset of a specific language.


The QLoRa (URL method of fine-tuning significantly lower the resources requirements compared to any other methods available, 
this allow to easily execute the process on significanly larger dataset while still using consumers GPUs and still achieve high accuracy. ## Model Description

Loquace-70m is the smallest model of the Loquace family. It was trained using QLoRa on a large dataset of 102k question/answer pairs
exclusively in Italian.

The related code can be found at: URL

Loquace-70m is part of the big Loquace family:

URL   -   Based on pythia-70m
URL  -   Based on pythia-410m
URL    -   Based on Falcon-7B.
URL   -   Based on pythia-12B
URL   -   Based on gpt-neox-20B ## Usage ## Training

Loquace-70m was trained on a conversational dataset comprising 102k question/answer pairs in Italian language. 
The training data was constructed by putting together translations from the original alpaca Dataset and other sources like the OpenAssistant dataset.
The model was trained for only 10000 iterations and took 6 hours on a single RTX 3090, kindly provided by Genesis Cloud. (URL ## Limitations

- Loquace-70m may not handle complex or nuanced queries well and may struggle with ambiguous or poorly formatted inputs.
- The model may generate responses that are factually incorrect or nonsensical. It should be used with caution, and outputs should be carefully verified.
- The training data primarily consists of conversational examples and may not generalize well to other types of tasks or domains. ## Dependencies

- PyTorch
- Transformers library by Hugging Face
- Bitsandbites
- QLoRa
TAGS
#transformers #pytorch #gpt_neox #text-generation #alpaca #llama #llm #finetune #Italian #qlora #conversational #it #dataset-cosimoiaia/Loquace-102k #license-cc-by-nc-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us 
 # 🇮🇹 Loquace-70m 🇮🇹 

An exclusively Italian speaking, instruction finetuned, Large Language model. 🇮🇹

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 
using dataset of a specific language.


The QLoRa (URL method of fine-tuning significantly lower the resources requirements compared to any other methods available, 
this allow to easily execute the process on significanly larger dataset while still using consumers GPUs and still achieve high accuracy. ## Model Description

Loquace-70m is the smallest model of the Loquace family. It was trained using QLoRa on a large dataset of 102k question/answer pairs
exclusively in Italian.

The related code can be found at: URL

Loquace-70m is part of the big Loquace family:

URL   -   Based on pythia-70m
URL  -   Based on pythia-410m
URL    -   Based on Falcon-7B.
URL   -   Based on pythia-12B
URL   -   Based on gpt-neox-20B ## Usage ## Training

Loquace-70m was trained on a conversational dataset comprising 102k question/answer pairs in Italian language. 
The training data was constructed by putting together translations from the original alpaca Dataset and other sources like the OpenAssistant dataset.
The model was trained for only 10000 iterations and took 6 hours on a single RTX 3090, kindly provided by Genesis Cloud. (URL ## Limitations

- Loquace-70m may not handle complex or nuanced queries well and may struggle with ambiguous or poorly formatted inputs.
- The model may generate responses that are factually incorrect or nonsensical. It should be used with caution, and outputs should be carefully verified.
- The training data primarily consists of conversational examples and may not generalize well to other types of tasks or domains. ## Dependencies

- PyTorch
- Transformers library by Hugging Face
- Bitsandbites
- QLoRa