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Model: swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
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---
language:
- en
- it
license: llama3
library_name: transformers
tags:
- facebook
- meta
- pythorch
- llama
- llama-3
- llamantino
base_model: meta-llama/Meta-Llama-3-8B-Instruct
datasets:
- gsarti/clean_mc4_it
- Chat-Error/wizard_alpaca_dolly_orca
- mlabonne/orpo-dpo-mix-40k
metrics:
- accuracy
model_creator: Marco Polignano - SWAP Research Group
pipeline_tag: text-generation
model-index:
- name: LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 74.57
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 92.75
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 66.85
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 75.93
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 82.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 58.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA
name: Open LLM Leaderboard
---
<img src="https://cdn-uploads.huggingface.co/production/uploads/5df8bb21da6d0311fd3d540f/xL6Ax1I34qfC4VPKEFA6Z.png" alt="llamantino3_anita" border="0" width="800px">
<hr>
📣 New MODEL FAMILY❗ [https://huggingface.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA](https://huggingface.co/m-polignano/ANITA-NEXT-24B-Magistral-2506-VISION-ITA)
<hr>
<!--<img src="https://i.ibb.co/6mHSRm3/llamantino53.jpg" width="200"/>-->
<h3><i>"Built with <b>Meta Llama 3</b>".</i></i></h3>
<p style="text-align:justify;"><b>LLaMAntino-3-ANITA-8B-Inst-DPO-ITA</b> is a model of the <a href="https://huggingface.co/swap-uniba"><b>LLaMAntino</b></a> - <i>Large Language Models family</i>.
The model is an instruction-tuned version of <a href="https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct"><b>Meta-Llama-3-8b-instruct</b></a> (a fine-tuned <b>LLaMA 3 model</b>).
This model version aims to be the a <b>Multilingual Model</b> 🏁 (EN 🇺🇸 + ITA🇮🇹) to further fine-tuning on Specific Tasks in Italian.</p>
The 🌟**ANITA project**🌟 *(**A**dvanced **N**atural-based interaction for the **ITA**lian language)*
wants to provide Italian NLP researchers with an improved model for the Italian Language 🇮🇹 use cases.<br>
<hr>
**Live DEMO:** [https://chat.llamantino.it/](https://chat.llamantino.it/)<br>
*It works only with Italian connection.*
<hr>
## Model Details
*Last Update: 10/05/2024*<br>
<a href="https://github.com/marcopoli/LLaMAntino-3-ANITA"><img src="https://github.githubassets.com/assets/GitHub-Logo-ee398b662d42.png" width="150"> https://github.com/marcopoli/LLaMAntino-3-ANITA</a><br>
| Model | HF | GGUF | EXL2 |
|-------|-------|-------|-------|
| *swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA* | [Link](https://huggingface.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA) | [Link](https://huggingface.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_GGUF) | [Link](https://huggingface.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA_EXL2) |
<hr>
## Specifications
- **Model developers**: <br><a href="https://marcopoli.github.io/">Ph.D. Marco Polignano</a> - University of Bari Aldo Moro, Italy <br> <a href="https://huggingface.co/swap-uniba">SWAP Research Group</a> <br>
- **Variations**: The model release has been **supervised fine-tuning (SFT)** using **QLoRA** 4bit, on instruction-based datasets. **DPO** approach over the *mlabonne/orpo-dpo-mix-40k* dataset is used to align with human preferences for helpfulness and safety.
- **Input**: Models input text only.
- **Language**: Multilingual 🏁 + Italian 🇮🇹
- **Output**: Models generate text and code only.
- **Model Architecture**: *Llama 3 architecture*.
- **Context length**: 8K, 8192.
- **Library Used**: [Unsloth](https://unsloth.ai/)
<hr>
## Playground
To use the model directly, there are many ways to get started, choose one of the following ways to experience it.
### Prompt Template
```
<|start_header_id|>system<|end_header_id|>
{ SYS Prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
{ USER Prompt }<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{ ASSIST Prompt }<|eot_id|>
````
### Transformers
For direct use with `transformers`, you can easily get started with the following steps.
- Firstly, you need to install transformers via the command below with `pip`.
```bash
pip install -U transformers trl peft accelerate bitsandbytes
```
- Right now, you can start using the model directly.
```python
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
)
base_model = "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA"
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.bfloat16,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
sys = "Sei un an assistente AI per la lingua Italiana di nome LLaMAntino-3 ANITA " \
"(Advanced Natural-based interaction for the ITAlian language)." \
" Rispondi nella lingua usata per la domanda in modo chiaro, semplice ed esaustivo."
messages = [
{"role": "system", "content": sys},
{"role": "user", "content": "Chi è Carlo Magno?"}
]
#Method 1
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
for k,v in inputs.items():
inputs[k] = v.cuda()
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, top_p=0.9, temperature=0.6)
results = tokenizer.batch_decode(outputs)[0]
print(results)
#Method 2
import transformers
pipe = transformers.pipeline(
model=model,
tokenizer=tokenizer,
return_full_text=False, # langchain expects the full text
task='text-generation',
max_new_tokens=512, # max number of tokens to generate in the output
temperature=0.6, #temperature for more or less creative answers
do_sample=True,
top_p=0.9,
)
sequences = pipe(messages)
for seq in sequences:
print(f"{seq['generated_text']}")
```
- Additionally, you can also use a model with **4bit quantization** to reduce the required resources at least. You can start with the code below.
```python
import torch
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
)
base_model = "swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=False,
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model)
sys = "Sei un an assistente AI per la lingua Italiana di nome LLaMAntino-3 ANITA " \
"(Advanced Natural-based interaction for the ITAlian language)." \
" Rispondi nella lingua usata per la domanda in modo chiaro, semplice ed esaustivo."
messages = [
{"role": "system", "content": sys},
{"role": "user", "content": "Chi è Carlo Magno?"}
]
#Method 1
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
for k,v in inputs.items():
inputs[k] = v.cuda()
outputs = model.generate(**inputs, max_new_tokens=512, do_sample=True, top_p=0.9, temperature=0.6)
results = tokenizer.batch_decode(outputs)[0]
print(results)
#Method 2
import transformers
pipe = transformers.pipeline(
model=model,
tokenizer=tokenizer,
return_full_text=False, # langchain expects the full text
task='text-generation',
max_new_tokens=512, # max number of tokens to generate in the output
temperature=0.6, #temperature for more or less creative answers
do_sample=True,
top_p=0.9,
)
sequences = pipe(messages)
for seq in sequences:
print(f"{seq['generated_text']}")
```
<hr>
## Evaluation
**Open LLM Leaderboard:**
Evaluated with lm-evaluation-benchmark-harness for the [**Open Italian LLMs Leaderboard**](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard)
```
lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks hellaswag_it,arc_it --device cuda:0 --batch_size auto:2
lm_eval --model hf --model_args pretrained=HUGGINGFACE_MODEL_ID --tasks m_mmlu_it --num_fewshot 5 --device cuda:0 --batch_size auto:2
```
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | **0.6160** |
| Arc_IT | 0.5714 |
| Hellaswag_IT | 0.7093 |
| MMLU_IT | 0.5672 |
<hr>
## Unsloth
<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/made with unsloth.png" width="200px" align="center" />
[Unsloth](https://unsloth.ai), a great tool that helps us easily develop products, at a lower cost than expected.
## Citation instructions
```bibtex
@misc{polignano2024advanced,
title={Advanced Natural-based interaction for the ITAlian language: LLaMAntino-3-ANITA},
author={Marco Polignano and Pierpaolo Basile and Giovanni Semeraro},
year={2024},
eprint={2405.07101},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
```
# Acknowledgments
We acknowledge the support of the PNRR project [FAIR - Future AI Research (PE00000013)](https://fondazione-fair.it/en/foundation/), Spoke 6 - Symbiotic AI (CUP H97G22000210007) under the NRRP MUR program funded by the NextGenerationEU.
Models are built on the Leonardo supercomputer with the support of CINECA-Italian Super Computing Resource Allocation, class C project IscrC\_Pro\_MRS (HP10CQO70G).
<img src="https://wiki.u-gov.it/confluence/download/attachments/49842317/image2022-6-21_11-11-44.png?version=1&modificationDate=1655802705000&api=v2" width="600px">
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_swap-uniba__LLaMAntino-3-ANITA-8B-Inst-DPO-ITA)
| Metric |Value|
|---------------------------------|----:|
|Avg. |75.12|
|AI2 Reasoning Challenge (25-Shot)|74.57|
|HellaSwag (10-Shot) |92.75|
|MMLU (5-Shot) |66.85|
|TruthfulQA (0-shot) |75.93|
|Winogrande (5-shot) |82.00|
|GSM8k (5-shot) |58.61|