"Built with Meta Llama 3".

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LLaMAntino-3-ANITA-8B-Inst-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 for the Italian Language 🇮🇹 use cases.

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Model Details

Last Update: 10/05/2024

https://huggingface.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA

Model	HF	GGUF	EXL2
swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA	Link	Link	Link

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Repo Structure

• inference/inference_anita.ipynb Python Notebook for testing the model inference.
  
  

• model_adaptation/finetune_llama3.py Python script fine-tuning the model on a specific task using Unsloth library.

• model_adaptation/dpo_llama3.py Python script to optimize the model over user preferences.

• model_adaptation/job_example.slurm SLURM script to run the model_adaption process over an HPC architecture (multiple-GPUs).

• evaluation/job_evaluation.slurm SLURM script to evaluate the model using EleutherAI/lm-evaluation-harness framework.
  
  

• use_examples/LLama_3_for_Sentiment_Analysis.ipynb Python Notebook for Sentiment Analysis task.

• use_examples/Llamaindex_LangChain.ipynb Python Notebook for RAG task.

• use_examples/Topic_Modeling_with_Llama3.ipynb Python Notebook for Topic Modleing.

• use_examples/SeqRecSys_LLM_Zero_Shot.ipynb Python Notebook for Sequential Recommender Systems.

• use_examples/User Interface.ipynb Python Notebook for inference through Visual Interface.
  
  

• requirements.txt List of project dependencies.

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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 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

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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.

  pip install -U transformers

• Right now, you can start using the model directly.

  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.

  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']}")

---

Evaluation

Open LLM Leaderboard:

Evaluated with lm-evaluation-benchmark-harness for the Open Italian LLMs 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

---

Unsloth

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

Citation instructions

@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}
}

@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}
}