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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+LLaMAntino-3-ANITA-8B-Inst-DPO-ITA - GGUF
+- Model creator: https://huggingface.co/swap-uniba/
+- Original model: https://huggingface.co/swap-uniba/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q2_K.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q2_K.gguf) | Q2_K | 2.96GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_S.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_S.gguf) | IQ3_S | 3.43GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_M.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ3_M.gguf) | IQ3_M | 3.52GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K.gguf) | Q3_K | 3.74GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_0.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_0.gguf) | Q4_0 | 4.34GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K.gguf) | Q4_K | 4.58GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_1.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q4_1.gguf) | Q4_1 | 4.78GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_0.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_0.gguf) | Q5_0 | 5.21GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K.gguf) | Q5_K | 5.34GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_1.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q5_1.gguf) | Q5_1 | 5.65GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q6_K.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q6_K.gguf) | Q6_K | 6.14GB |
+| [LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q8_0.gguf](https://huggingface.co/RichardErkhov/swap-uniba_-_LLaMAntino-3-ANITA-8B-Inst-DPO-ITA-gguf/blob/main/LLaMAntino-3-ANITA-8B-Inst-DPO-ITA.Q8_0.gguf) | Q8_0 | 7.95GB |
+
+
+
+
+Original model description:
+---
+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/cZoZdwQOPdQsnQmDXHcSn.png" alt="llamantino3_anita" border="0" width="800px">
+
+<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|
+
+
+