README.md

---
license: apache-2.0
datasets:
- cosimoiaia/Loquace-102k
language:
- it
tags:
- Italian
- Qlora
- Mistral
- finetuning
- Text Generation
pipeline_tag: text-generation
---
Model Card for Loquace-7B-Mistral [(Versione in Italiano tradotta da Loquace)](https://huggingface.co/cosimoiaia/Loquace-7B-Mistral/blob/main/Readme-ITA.md)

# 🇮🇹 Loquace-7B-Mistral v0.1 🇮🇹 

Loquace is an Italian speaking, instruction finetuned, Large Language model. 🇮🇹

Loquace-7B-Mistral's peculiar features:

- Is pretty good a following istructions in Italian.
- Responds well to prompt-engineering.
- Works well in a RAG (Retrival Augmented Generation) setup.
- It has been trained on a relatively raw dataset [Loquace-102K](https://huggingface.co/datasets/cosimoiaia/Loquace-102k) using QLoRa and Mistral-7B-Instruct as base.
- Training took only 4 hours on a 3090, costing a little more than <b>1 euro</b>! On [Genesis Cloud](https://gnsiscld.co/26qhlf) GPU.
- It is <b><i>Truly Open Source</i></b>: Model, Dataset and Code to replicate the results are completely released.
- Created in a garage in the south of Italy.

The Loquace Italian LLM models are created with the goal of democratizing AI and LLM in the Italian Landscape. 

<b>No more need for expensive GPU, large funding, Big Corporation or Ivory Tower Institution, just download the code and train on your dataset on your own PC (or a cheap and reliable cloud provider like [Genesis Cloud](https://gnsiscld.co/26qhlf) )</b>

### Fine-tuning Instructions:
The related code can be found at:
https://github.com/cosimoiaia/Loquace

### GGUF Version for CPU Inference:
8bit quantized Version of Loquace can be found [here](https://huggingface.co/cosimoiaia/Loquace-7B-Mistral-GGUF)

Here is an incomplate list of clients and libraries that are known to support GGUF (thanks to [TheBloke](https://huggingface.co/TheBloke) for this list and his awesome work) ):

* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.

#### Previous releases of the Loquace family:

The Loquace family started in the beginning of 2023 to prove it was possible to fine-tune a Large Language model in a different language, you can find the other members of the family here:

- 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


These model, although not producing good results, are proof that cheap finetuning is actually possible in a short amount of time, with a relaively raw dataset and extremely low resources.

## Inference:

```python
from transformers import MistralForCausalLM, AutoTokenizer


def generate_prompt(instruction):    
   prompt = f"""### Instruction: {instruction}
   
### Response:
"""
   return prompt

model_name = "cosimoiaia/Loquace-7B-Mistral"

model = MistralForCausalLM.from_pretrained(
   model_name,
   device_map="auto",
   torch_dtype=torch.bfloat16                
)

model.config.use_cache = True


tokenizer = AutoTokenizer.from_pretrained(model_name, add_eos_token=False)

prompt = generate_prompt("Chi era Dante Alighieri?")
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, do_sample = True, num_beams = 2, top_k=50, top_p= 0.95, max_new_tokens=2046, early_stopping = True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("Response:")[1].strip())
```

## Examples:
```
> Pretendi di essere Dante Alighieri e raccontami la tua vita.
 Sono Dante Alighieri, un poeta e scrittore italiano del XIV secolo. Nacqui a Firenze nel 1265 e ho trascorso gran parte della mia vita viaggiando in Europa e scrivendo poesie. Ho anche scritto la Divina Commedia, una delle opere letterarie più famose di tutti i tempi.

> Quanto fa 7 x 14 - 98 ?
 7 x 14 = 98, quindi la risposta è 0.

> La radice quadrata di 144 ?
 La radice quadrata di 144 è 12.

> Inventa una ricetta pugliese con la polenta.
 La ricetta è chiamata "Polenta al burro di arachidi e formaggio". Per prepararla, inizia bollendo la polenta secondo le istruzioni sulla confezione. Mentre la polenta si cucina, mescola insieme 1/2 tazza di burro di arachidi, 1/4 di tazza di formaggio grattugiato e 1/4 di tazza di parmigiano grattugiato. Una volta che la polenta è cotta, versala su una teglia da forno e distribuire il composto di burro di arachidi e formaggio sopra la polenta. Metti in forno a 350 gradi per 15 minuti o fino a quando la parte superiore è dorata. Servi caldo con un'insalata di pomodoro e basilico fresco.
```

## Limitations

- Loquace-7B 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.

## Model Author:
Cosimo Iaia <[email protected]>