metadata

license: apache-2.0
datasets:
  - cosimoiaia/Loquace-102k
language:
  - it
tags:
  - Italian
  - Qlora
  - finetuning
  - Text Generation
pipeline_tag: text-generation

Model Card for Loquace-Wizard-13B

🇮🇹 Loquace-Wizard-13B v0.1 🇮🇹

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

Loquace-Wizard-14B's peculiar features:

The First 13B Specifically finetuned in Italian.
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 using QLoRa and WizardLM-13B-Instruct as base.
Training took only 8 hours on a 3090, costing a little more than 2 euro! On Genesis Cloud GPU.
It is Truly Open Source: 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.

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 )

Fine-tuning Instructions:

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

Inference:

from transformers import LlamaForCausalLM, AutoTokenizer


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

model_name = "."

model = LlamaForCausalLM.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())

Model Author:

Cosimo Iaia [email protected]