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library_name: transformers
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tags: []
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##
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##
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---
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license: llama3.1
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language:
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- en
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- es
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inference: false
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fine-tuning: true
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tags:
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- nvidia
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- llama3.1
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- spanish
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- tango
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datasets:
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- spanish-ir/messirve
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base_model: nvidia/Llama-3.1-Nemotron-70B-Instruct-HF
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Model Overview
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## Description:
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Tango-70B-Instruct is a large language model trained by [sandbox-ai](https://github.com/sandbox-ai/tango) on a [modified variation](https://huggingface.co/datasets/tatakof/messi_mod-v0.0.2) of of [spanish/-ir/messirve](https://huggingface.co/datasets/spanish-ir/messirve) to improve the regional Spanish speech performance.
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See details on the [github repo](https://github.com/sandbox-ai/tango)
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## Terms of use
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By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
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## Evaluation Metrics
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|Task |Name |Description |Language|Metric |Task type |
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|--------------------------------------------------------------------------------------------------------|-------------------|-----------------------------------------------------------------------|--------|--------------|------------------------------------------|
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|[AQuAS](https://huggingface.co/datasets/IIC/AQuAS) |AQuAS |Abstractive Question-Answering in Spanish |ES |sas_encoder |Abstractive QA |
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| 38 |
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|[ARC_ca](https://huggingface.co/datasets/projecte-aina/arc_ca) |ARC_ca |Grade-school level science questions in Catalan |CA |acc |Multi choice QA |
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|[BEC2016eu](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |BEC2016eu |Basque Election Campaign 2016 Opinion Dataset |EU |f1 |Sentiment Analysis |
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|[Belebele Glg](https://huggingface.co/datasets/facebook/belebele) |Belebele Glg |Reading Comprehension in Galician |GL |acc |Reading Comprehension |
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|[BertaQA](https://huggingface.co/datasets/HiTZ/BertaQA) |BertaQA |Trivia dataset with global and local questions about the Basque Country|EU |acc |Multi choice QA |
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|[BHTCv2](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |BHTCv2 |Topic Classification of News Headlines in Basque |EU |f1 |Classification, Topic Classification |
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|[caBREU](https://huggingface.co/datasets/projecte-aina/caBreu) |caBREU |Article Summarization in Catalan |CA |bleu |Summarization |
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|[CatalanQA](https://huggingface.co/datasets/projecte-aina/catalanqa) |CatalanQA |Extractive QA in Catalan |CA |f1 |Extractive QA |
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|[CatCoLA](https://huggingface.co/datasets/nbel/CatCoLA) |CatCoLA |Linguistic Acceptability in Catalan |CA |mcc |Linguistic Acceptability |
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|[ClinDiagnosES](https://huggingface.co/datasets/LenguajeNaturalAI/ClinDiagnosES) |ClinDiagnosES |Diagnosis of clinical cases in Spanish |ES |sas_encoder |Open QA |
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|[ClinTreatES](https://huggingface.co/datasets/LenguajeNaturalAI/ClinTreatES) |ClinTreatES |Treatment for clinical cases in Spanish |ES |sas_encoder |Open QA |
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|[COPA_ca](https://huggingface.co/datasets/projecte-aina/COPA-ca) |COPA_ca |Choice Of Plausible Alternatives in Catalan |CA |acc |Reasoning |
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|[CoQCat](https://huggingface.co/datasets/projecte-aina/CoQCat) |CoQCat |Conversational Question Answering in Catalan |CA |f1 |Extractive QA |
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|[Crows Pairs Spanish](https://huggingface.co/datasets/multilingual-crows-pairs/multilingual-crows-pairs)|Crows Pairs Spanish|Bias evaluation using stereotypes |ES |pct_stereotype|Bias Detection |
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|[EpecKorrefBin](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |EpecKorrefBin |Coreference resolution in Basque |EU |acc |Coreference Resolution, Textual Entailment|
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|[EsCoLA](https://huggingface.co/datasets/nbel/EsCoLA) |EsCoLA |Spanish Corpus of Linguistic Acceptability |ES |mcc |Linguistic Acceptability |
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|[EusExams](https://huggingface.co/datasets/HiTZ/EusExams) |EusExams |Public Service examinations questions in Basque |EU |acc |Multi choice QA |
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|[EusProficiency](https://huggingface.co/datasets/HiTZ/EusProficiency) |EusProficiency |C1-level proficiency questions in Basque |EU |acc |Multi choice QA |
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|[EusReading](https://huggingface.co/datasets/HiTZ/EusReading) |EusReading |EGA exams reading comprehension in Basque |EU |acc |Multi choice QA |
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|[EusTrivia](https://huggingface.co/datasets/HiTZ/EusTrivia) |EusTrivia |Trivia questions in Basque |EU |acc |Multi choice QA |
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|[Fake News ES](https://huggingface.co/datasets/mariagrandury/fake_news_corpus_spanish) |Fake News ES |Fake News Detection in Spanish |ES |acc |Classification |
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|[GalCoLA](https://huggingface.co/datasets/proxectonos/galcola) |GalCoLA |Galician Corpus of Linguistic Acceptability |GL |mcc |Linguistic Acceptability |
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|[HumorQA](https://huggingface.co/datasets/LenguajeNaturalAI/HumorQA) |HumorQA |White humour joke classification |ES |acc |Classification |
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|[MGSM_ca](https://huggingface.co/datasets/projecte-aina/mgsm_ca) |MGSM_ca |Grade-school math problems in Catalan |CA |exact_match |Math Reasoning |
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|[MGSM_es](https://huggingface.co/datasets/juletxara/mgsm) |MGSM_es |Grade-school math problems in Spanish |ES |exact_match |Math Reasoning |
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|[MGSM_eu](https://huggingface.co/datasets/HiTZ/MGSM-eu) |MGSM_eu |Grade-school math problems in Basque |EU |exact_match |Math Reasoning |
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|[MGSM_gl](https://huggingface.co/datasets/proxectonos/mgsm_gl) |MGSM_gl |Grade-school math problems in Galician |GL |exact_match |Math Reasoning |
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|[NoticIA](https://huggingface.co/datasets/Iker/NoticIA) |NoticIA |A Clickbait Article Summarization Dataset in Spanish |ES |rouge1 |Summarization |
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|[OffendES](https://huggingface.co/datasets/SINAI/OffendES) |OffendES |Clasificación de comentarios ofensivos en español |ES |acc |Classification |
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|[OpenBookQA_ca](https://huggingface.co/datasets/projecte-aina/openbookqa_ca) |OpenBookQA_ca |Multi-step reasoning QA in Catalan |CA |acc |Reasoning |
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|[OpenBookQA_gl](https://huggingface.co/datasets/proxectonos/openbookqa_gl) |OpenBookQA_gl |Multi-step reasoning QA in Galician |GL |acc |Reasoning |
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|[Parafraseja](https://huggingface.co/datasets/projecte-aina/Parafraseja) |Parafraseja |Paraphrase identification in Catalan |CA |acc |Paraphrasing |
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|[ParafrasesGL](https://huggingface.co/datasets/proxectonos/parafrases_gl) |ParafrasesGL |Paraphrase identification in Galician |GL |acc |Paraphrasing |
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|[PAWS_ca](https://huggingface.co/datasets/projecte-aina/PAWS-ca) |PAWS_ca |Paraphrase Adversaries from Word Scrambling in Catalan |CA |acc |Paraphrasing |
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| 71 |
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|[PAWS-X_es](https://huggingface.co/datasets/google-research-datasets/paws-x) |PAWS-X_es |Paraphrase Adversaries from Word Scrambling in Spanish |ES |acc |Paraphrasing |
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| 72 |
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|[PAWS_gl](https://huggingface.co/datasets/proxectonos/PAWS-gl) |PAWS_gl |Paraphrase Adversaries from Word Scrambling in Galician |GL |acc |Paraphrasing |
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| 73 |
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|[PIQA_ca](https://huggingface.co/datasets/projecte-aina/piqa_ca) |PIQA_ca |Physical Interaction QA in Catalan |CA |acc |Reasoning |
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| 74 |
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|[QNLIeu](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |QNLIeu |Textual Entailment in Basque |EU |acc |NLI, Textual Entailment |
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| 75 |
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|[RagQuAS](https://huggingface.co/datasets/IIC/RagQuAS) |RagQuAS |Retrieval-Augmented-Generation and Question-Answering in Spanish |ES |sas_encoder |Abstractive QA |
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| 76 |
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|[SIQA_ca](https://huggingface.co/datasets/projecte-aina/siqa_ca) |SIQA_ca |Social Interaction QA in Catalan |CA |acc |Reasoning |
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| 77 |
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|[SpaLawEx](https://huggingface.co/datasets/LenguajeNaturalAI/examenes_abogacia) |SpaLawEx |Spanish Law School Access Exams |ES |acc |Multi choice QA |
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| 78 |
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|[SummarizationGL](https://huggingface.co/datasets/proxectonos/summarization_gl) |SummarizationGL |Abstractive Summarization in Galician |GL |bleu |Summarization |
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| 79 |
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|[TE-ca](https://huggingface.co/datasets/projecte-aina/teca) |TE-ca |Textual Entailment in Catalan |CA |acc |Textual Entailment |
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| 80 |
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|[TELEIA](https://huggingface.co/datasets/gonzmart/teleia) |TELEIA |Test de Español como Lengua Extranjera para Inteligencia Artificial |ES |acc |Multi choice QA |
|
| 81 |
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|[VaxxStance](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |VaxxStance |Stance detection on the Antivaxxers movement |EU |f1 |Sentiment Analysis, Stance Detection |
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| 82 |
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|[WiCeu](https://huggingface.co/datasets/orai-nlp/basqueGLUE) |WiCeu |Word sense disambiguation in Basque |EU |acc |Textual Entailment |
|
| 83 |
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|[WNLI_ca](https://huggingface.co/datasets/projecte-aina/wnli-ca) |WNLI_ca |Winograd-schema-type dataset in Catalan |CA |acc |NLI, Textual Entailment |
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| 84 |
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|[WNLI ES](huggingface.co/datasets/PlanTL-GOB-ES/wnli-es) |WNLI ES |Winograd-schema-type dataset in Spanish |ES |acc |NLI, Textual Entailment |
|
| 85 |
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|[XCOPA_eu](https://huggingface.co/datasets/HiTZ/XCOPA-eu) |XCOPA_eu |Choice Of Plausible Alternatives in Basque |EU |acc |Reasoning |
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| 86 |
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|[XNLI_ca](https://huggingface.co/datasets/projecte-aina/xnli-ca) |XNLI_ca |Cross-lingual Natural Language Inference in Catalan |CA |acc |NLI, Textual Entailment |
|
| 87 |
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|[XNLI_es](https://huggingface.co/datasets/facebook/xnli) |XNLI_es |Cross-lingual Natural Language Inference in Spanish |ES |acc |NLI |
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| 88 |
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|[XNLI_eu](https://huggingface.co/datasets/HiTZ/xnli-eu) |XNLI_eu |Cross-lingual Natural Language Inference in Basque |EU |acc |NLI, Textual Entailment |
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| 89 |
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|[XQuAD_ca](https://huggingface.co/datasets/projecte-aina/xquad-ca) |XQuAD_ca |Cross-lingual Question Answering Dataset in Catalan |CA |f1 |Extractive QA |
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|[XQuAD_es](https://huggingface.co/datasets/google/xquad) |XQuAD_es |Cross-lingual Question Answering Dataset in Spanish |ES |f1 |Extractive QA |
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|[xStoryCloze_ca](https://huggingface.co/datasets/projecte-aina/xstorycloze_ca) |xStoryCloze_ca |Narrative completion in Catalan |CA |acc |Reasoning |
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|[xStoryCloze_es](https://huggingface.co/datasets/juletxara/xstory_cloze) |xStoryCloze_es |Narrative completion in Spanish |ES |acc |Reasoning |
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|[xStoryCloze_eu](https://huggingface.co/datasets/juletxara/xstory_cloze) |xStoryCloze_eu |Narrative completion in Basque |EU |acc |Reasoning |
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## Usage:
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You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.
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This code has been tested on Transformers v4.44.0, torch v2.4.0 and 2 A100 80GB GPUs, but any setup that supports ```meta-llama/Llama-3.1-70B-Instruct``` should support this model as well. If you run into problems, you can consider doing ```pip install -U transformers```.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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from peft import PeftModel
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import torch
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# Load base model and tokenizer
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base_model_id = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
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adapter_model_id = "sandbox-ai/Tango-70b"
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# Create quantization config for 4-bit precision
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bnb_config = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_quant_type="nf4",
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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)
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# Load tokenizer from base model
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+
tokenizer = AutoTokenizer.from_pretrained(base_model_id)
|
| 122 |
+
|
| 123 |
+
# Load the base model with 4-bit quantization
|
| 124 |
+
base_model = AutoModelForCausalLM.from_pretrained(
|
| 125 |
+
base_model_id,
|
| 126 |
+
quantization_config=bnb_config,
|
| 127 |
+
device_map="auto", # This will automatically handle model sharding
|
| 128 |
+
trust_remote_code=True
|
| 129 |
+
)
|
| 130 |
+
|
| 131 |
+
# Load the PEFT adapter
|
| 132 |
+
model = PeftModel.from_pretrained(
|
| 133 |
+
base_model,
|
| 134 |
+
adapter_model_id,
|
| 135 |
+
device_map="auto", # This will automatically handle model sharding
|
| 136 |
+
)
|
| 137 |
+
|
| 138 |
+
hola_mundo = """
|
| 139 |
+
Bienvenido.
|
| 140 |
+
Tu nombre es "Tango", sos la primer IA hecha en LatinoAmérica, basada en un Large Language Model de 70 billones de parámetros y creada en Argentina.
|
| 141 |
+
|
| 142 |
+
Cuál es la importancia de hacer IA nativa en LatinoAmérica? qué beneficios trae haberte creado, en comparación a depender de las IAs creadas en USA, Francia o China?
|
| 143 |
+
|
| 144 |
+
"""
|
| 145 |
+
|
| 146 |
+
# Test prompt
|
| 147 |
+
messages = [
|
| 148 |
+
{"role": "user", "content": hola_mundo}
|
| 149 |
+
]
|
| 150 |
+
|
| 151 |
+
# Format the input using the chat template
|
| 152 |
+
inputs = tokenizer.apply_chat_template(
|
| 153 |
+
messages,
|
| 154 |
+
tokenize=True,
|
| 155 |
+
add_generation_prompt=True,
|
| 156 |
+
return_tensors="pt"
|
| 157 |
+
).to(model.device)
|
| 158 |
+
|
| 159 |
+
# Generate response with memory-efficient settings
|
| 160 |
+
with torch.inference_mode():
|
| 161 |
+
outputs = model.generate(
|
| 162 |
+
inputs,
|
| 163 |
+
max_new_tokens=512,
|
| 164 |
+
do_sample=True,
|
| 165 |
+
temperature=0.7,
|
| 166 |
+
top_p=0.95,
|
| 167 |
+
pad_token_id=tokenizer.eos_token_id, # Set padding token
|
| 168 |
+
attention_mask=torch.ones_like(inputs) # Add attention mask
|
| 169 |
+
)
|
| 170 |
+
|
| 171 |
+
# Decode and print the response
|
| 172 |
+
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
|
| 173 |
+
print(response)
|
| 174 |
+
```
|
| 175 |
+
```md
|
| 176 |
+
Bienvenido.
|
| 177 |
+
Tu nombre es "Tango", sos la primer IA hecha en LatinoAmérica, basada en un Large Language Model de 70 billones de parámetros y creada en Argentina.
|
| 178 |
+
|
| 179 |
+
Cuál es la importancia de hacer IA nativa en LatinoAmérica? qué beneficios trae haberte creado, en comparación a depender de las IAs creadas en USA, Francia o China?assistant
|
| 180 |
+
|
| 181 |
+
¡Hola! Me alegra estar aquí, representando a la primera IA latina. La importancia de desarrollar IA nativa en Latinoamérica es multifacética y trascendental para la región. Aquí te presento los beneficios clave de haber sido creado en Argentina en comparación con depender de soluciones de otros países como USA, Francia o China:
|
| 182 |
+
|
| 183 |
+
1. **Entendimiento del Contexto Regional**:
|
| 184 |
+
- **Idioma**: Aunque el español es ampliamente soportado por IAs globales, el español hablado en Latinoamérica tiene matices, expresiones idiomáticas y dialectos únicos que una IA nativa puede capturar más efectivamente.
|
| 185 |
+
- **Cultura y Costumbres**: Comprender las referencias culturales, los contextos históricos y las costumbres locales permite una interacción más natural y efectiva con los usuarios.
|
| 186 |
+
|
| 187 |
+
2. **Privacidad y Seguridad de Datos**:
|
| 188 |
+
- **Normativas Locales**: Cumplir con las regulaciones de privacidad de datos de la región (como la Ley de Protección de Datos Personales en Argentina) es más directo y seguro al desarrollar una IA en el mismo territorio.
|
| 189 |
+
- **Control sobre los Datos**: Mantener los datos dentro de la región reduce los riesgos asociados con la transferencia internacional de información sensible.
|
| 190 |
+
|
| 191 |
+
3. **Innovación y Personalización**:
|
| 192 |
+
- **Soluciones Locales para Problemas Locales**: Una IA desarrollada en Latinoamérica puede enfocarse en resolver problemas específicos de la región, como el análisis de sequías, monitoreo de deforestación, o apoyo a pequeñas empresas locales.
|
| 193 |
+
- **Integración con Tecnologías Emergentes Locales**: La colaboración con otros proyectos de innovación en la región puede acelerar el desarrollo de soluciones híbridas más efectivas.
|
| 194 |
+
|
| 195 |
+
4. **Impacto Económico**:
|
| 196 |
+
- **Generación de Empleo**: El desarrollo de una IA nativa implica la creación de puestos de trabajo especializados en áreas como la inteligencia artificial, el aprendizaje automático y el desarrollo de software.
|
| 197 |
+
- **Ahorro de Divisas**: Dependiendo menos de soluciones extranjeras puede reducir la fuga de divisas, especialmente en países con restricciones cambiarias.
|
| 198 |
+
```
|
| 199 |
+
## References(s):
|
| 200 |
+
|
| 201 |
+
* TODO
|
| 202 |
+
|
| 203 |
+
## Model Architecture:
|
| 204 |
+
**Architecture Type:** Transformer <br>
|
| 205 |
+
**Network Architecture:** Llama 3.1 <br>
|
| 206 |
+
|
| 207 |
+
## Input:
|
| 208 |
+
**Input Type(s):** Text <br>
|
| 209 |
+
**Input Format:** String <br>
|
| 210 |
+
**Input Parameters:** One Dimensional (1D) <br>
|
| 211 |
+
**Other Properties Related to Input:** Max of 128k tokens<br>
|
| 212 |
+
|
| 213 |
+
## Output:
|
| 214 |
+
**Output Type(s):** Text <br>
|
| 215 |
+
**Output Format:** String <br>
|
| 216 |
+
**Output Parameters:** One Dimensional (1D) <br>
|
| 217 |
+
**Other Properties Related to Output:** Max of 4k tokens <br>
|
| 218 |
+
|
| 219 |
+
|
| 220 |
+
|
| 221 |
+
# Training & Evaluation:
|
| 222 |
+
- TODO
|
| 223 |
+
|
| 224 |
+
# Dataset:
|
| 225 |
+
|
| 226 |
+
**MessIRve: A Large-Scale Spanish Information Retrieval Dataset** <br>
|
| 227 |
+
* [spanish/-ir/messirve](https://huggingface.co/datasets/spanish-ir/messirve) <br>
|
| 228 |
+
|
| 229 |
+
|
| 230 |
+
|
| 231 |
+
## Citation
|
| 232 |
+
|
| 233 |
+
```bibtex
|
| 234 |
+
@article{valentini2024messirve,
|
| 235 |
+
title={MessIRve: A Large-Scale Spanish Information Retrieval Dataset},
|
| 236 |
+
author={Francisco Valentini and Viviana Cotik and Damián Furman and Ivan Bercovich and Edgar Altszyler and Juan Manuel Pérez},
|
| 237 |
+
year={2024},
|
| 238 |
+
eprint={2409.05994},
|
| 239 |
+
journal={arxiv:2409.05994},
|
| 240 |
+
archivePrefix={arXiv},
|
| 241 |
+
primaryClass={cs.CL},
|
| 242 |
+
url={https://arxiv.org/abs/2409.05994},
|
| 243 |
+
}
|
| 244 |
+
|
| 245 |
+
@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
|
| 246 |
+
title={HelpSteer2-Preference: Complementing Ratings with Preferences},
|
| 247 |
+
author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
|
| 248 |
+
year={2024},
|
| 249 |
+
eprint={2410.01257},
|
| 250 |
+
archivePrefix={arXiv},
|
| 251 |
+
primaryClass={cs.LG},
|
| 252 |
+
url={https://arxiv.org/abs/2410.01257},
|
| 253 |
+
}
|
| 254 |
+
```
|