README.md · swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA at b972f561d161dd30386ffc55385884af8e134c32

metadata

datasets:
  - gsarti/clean_mc4_it
  - Chat-Error/wizard_alpaca_dolly_orca
  - jondurbin/truthy-dpo-v0.1
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_creator: Marco Polignano - SWAP Research Group
language:
  - en
  - it
metrics:
  - accuracy
pipeline_tag: text-generation
tags:
  - facebook
  - meta
  - pythorch
  - llama
  - llama-3
  - llamantino
license: llama3

LLaMAntino-3-ANITA-8B-Instr-DPO-ITA is a model of the LLaMAntino - Large Language Models family. The model is an instruction-tuned version of Meta-Llama-3-8b-instruct (a fine-tuned LLaMA 3 model). This model version aims to be the a Multilingual Model 🏁 -- EN 🇺🇸 + ITA🇮🇹 -- to further fine-tune for the Specific Italian Task

The 🌟ANITA project🌟 *(Advanced Natural-based interaction for the ITAlian language)* wants to provide Italian NLP researchers with an improved model the for Italian Language 🇮🇹 use cases.

Model Details

Last Update: 10/05/2024

https://github.com/marcopoli/LLaMAntino-3-ANITA

Specifications

Model developers:
Ph.D. Marco Polignano - University of Bari Aldo Moro, Italy
SWAP Research Group
Variations: The model release has been supervised fine-tuning (SFT) using QLoRA 4bit, on two instruction-based datasets. DPO approach over the jondurbin/truthy-dpo-v0.1 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

Playground

To use the model directly, there are many ways to get started, choose one of the following ways to experience it.

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.
```
pip install -U transformers
```

Right now, you can start using the model directly.

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
)

base_model = "m-polignano-uniba/LLaMAntino-3-ANITA-8B-Instr-DPO-ITA"
model = AutoModelForCausalLM.from_pretrained(
    base_model,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(base_model)

messages = [
    {"role": "system", "content": {"role": "system", "content": "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. "},
    {"role": "user", "content": "Why is the sky blue?"}
]

#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.

import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    BitsAndBytesConfig,
)

base_model = "m-polignano-uniba/LLaMAntino-3-ANITA-8B-Instr-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)

messages = [
   {"role": "system", "content": {"role": "system", "content": "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. "},
    {"role": "user", "content": "Why is the sky blue?"}
]

#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']}")

Unsloth

For direct use with unsloth, you can easily get started with the following steps.

Firstly, you need to install unsloth via the command below with pip.

pip install "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git"
pip install --no-deps xformers trl peft accelerate bitsandbytes

Initialize and optimize the model before use.

from unsloth import FastLanguageModel
import torch

base_model = "m-polignano-uniba/LLaMAntino-3-ANITA-8B-Instr-DPO-ITA"
model, tokenizer = FastLanguageModel.from_pretrained(
    model_name = base_model,
    max_seq_length = 8192,
    dtype = None,
    load_in_4bit = True, # Change to `False` if you don't want to use 4bit quantization.
)
FastLanguageModel.for_inference(model)

Right now, you can start using the model directly.

messages = [
    {"role": "system", "content": {"role": "system", "content": "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. "},
    {"role": "user", "content": "Why is the sky blue?"}
]
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)

Unsloth

Unsloth, a great tool that helps us easily develop products, at a lower cost than expected.

Citation instructions

@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}
}

@article{llama3modelcard,
  title={Llama 3 Model Card},
  author={AI@Meta},
  year={2024},
  url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}