---
license: other
datasets:
- raicrits/Orca_ITA_200k
language:
- it
pipeline_tag: text-generation
tags:
- LLM
- Italian
- Orca
- Hermes
- LLama2
library_name: transformers
---


# 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](https://huggingface.co/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](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b), a version of [meta-llama/Llama-2-7b](https://huggingface.co/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](https://arxiv.org/abs/2106.09685) approach and a dataset of 120k random pairs of instruction/answer from [raicrits/Orca_ITA_200k](https://huggingface.co/datasets/raicrits/Orca_ITA_200k).

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


- **Developed by:** Stefano Scotta (stefano.scotta@rai.it)
- **Model type:** LLM fine-tuned to follow instructions
- **Language(s) (NLP):** Italian
- **License:** Other
- **Finetuned from model:** [NousResearch/Nous-Hermes-llama-2-7b](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b)

## 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:**

 ``` python
"""### Instruction: {instruction}

### Response:
"""
```

 **Usage:**
Use the code below to get started with the model.
 ``` python
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())
```
``` python
"""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](https://huggingface.co/datasets/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](https://arxiv.org/abs/2106.09685) approach. 


#### Training Hyperparameters

 **Training setting:**
- train epochs=3,

- learning_rate=2e-4,

- mixed precision training: float16


 **LoRA configuration:**
-  r= 8
  
- lora_alpha=16
  
- target_modules=["q_proj","v_proj"]

- lora_dropout=0.05
  
- bias="none"
  
- task_type=TaskType.CAUSAL_LM


## 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](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** 1 NVIDIA A100/40Gb
- **Hours used:** 78
- **Cloud Provider:** Private Infrastructure
- **Carbon Emitted:** 8.42 kg eq. CO2



## Model Card Authors

Stefano Scotta (stefano.scotta@rai.it)

## Model Card Contact

stefano.scotta@rai.it