LLM Italian Orca Hermes LLama2

Model Card for Model raicrits/Hermes7b_ITA

<!-- Provide a quick summary of what the model is/does. --> An open-source LLaMa2 language model of 7b parameters fine-tuned (using as base model NousResearch/Nous-Hermes-llama-2-7b) to follow instructions in italian.

Model Description

This model is a LLM of 7b parameters based on NousResearch/Nous-Hermes-llama-2-7b, a version of meta-llama/Llama-2-7b fine-tuned to follow instructions. The model was further fine-tuned in order to follow instructions in italian, using LoRA approach and a dataset of 120k random pairs of instruction/answer from raicrits/Orca_ITA_200k.

This repository contains the model weights merged with the LoRA adapters obtained in the fine-tuning procedure.

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> The model can be used as is to respond to simple instructions in Italian or can be further fine-tuned to perform specific tasks.

Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. --> As any other LLM it is possible that the model generates content which does not correspond to the reality as well as wrong, biased, offensive and inappropriate answers.

How to Get Started with the Model

Prompt template:

"""### Instruction: {instruction}

### Response:
"""

Usage: Use the code below to get started with the model.

import os
import torch
import sys
from transformers import LlamaForCausalLM, AutoTokenizer


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

model_name = "raicrits/Hermes7b_ITA"

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_test("Cosa puoi dirmi sul dio Hermes?")
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=256, early_stopping = True)
print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("Response:")[1].strip())

"""Hermes è un dio dell'antica Grecia. Era il dio del commercio, della comunicazione e del trasporto. Era anche il dio della mente e della intelligenza. Era noto per il suo eloquente linguaggio e la sua capacità di spostarsi velocemente. Era considerato il messaggero degli dèi e spesso veniva raffigurato con un cappello di pelle di capra e sandali."""

Training Details

Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> The model was fine-tuned on 120k random records of raicrits/Orca_ITA_200k.

Training Procedure

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> The fine-tuning procedure was done using LoRA approach.

Training Hyperparameters

Training setting:

LoRA configuration:

Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model Card Authors

Stefano Scotta (stefano.scotta@rai.it)

Model Card Contact

stefano.scotta@rai.it