metadata

language:
  - en
  - de
  - fr
  - zh
  - pt
  - nl
  - ru
  - ko
  - it
  - es
license: cc-by-nc-4.0
metrics:
  - comet
pipeline_tag: translation
model-index:
  - name: TowerInstruct-Mistral-7B-v0.2
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: IFEval (0-Shot)
          type: HuggingFaceH4/ifeval
          args:
            num_few_shot: 0
        metrics:
          - type: inst_level_strict_acc and prompt_level_strict_acc
            value: 28.43
            name: strict accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: BBH (3-Shot)
          type: BBH
          args:
            num_few_shot: 3
        metrics:
          - type: acc_norm
            value: 14.22
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MATH Lvl 5 (4-Shot)
          type: hendrycks/competition_math
          args:
            num_few_shot: 4
        metrics:
          - type: exact_match
            value: 1.59
            name: exact match
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GPQA (0-shot)
          type: Idavidrein/gpqa
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 0
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MuSR (0-shot)
          type: TAUR-Lab/MuSR
          args:
            num_few_shot: 0
        metrics:
          - type: acc_norm
            value: 15.96
            name: acc_norm
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU-PRO (5-shot)
          type: TIGER-Lab/MMLU-Pro
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 10.76
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Unbabel/TowerInstruct-Mistral-7B-v0.2
          name: Open LLM Leaderboard

Model Card for TowerInstruct-Mistral-7B-v0.2

Model Details

Model Description

TowerInstruct-Mistral-7B-v0.2 is a language model that results from fine-tuning a Mistral version of TowerBase on the TowerBlocks supervised fine-tuning dataset. The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and paragraph/document-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.

This model has performance comparable to TowerInstruct-13B-v0.2, while being half the size. Check out our paper in COLM 2024.

Developed by: Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay
Model type: A 7B parameter model fine-tuned on a mix of publicly available, synthetic datasets on translation-related tasks, as well as conversational datasets and code instructions.
Language(s) (NLP): English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
License: CC-BY-NC-4.0

Intended uses & limitations

The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset (TowerBlocks), which contains a diverse range of data sources:

Translation (sentence and paragraph-level)
Automatic Post Edition
Machine Translation Evaluation
Context-aware Translation
Terminology-aware Translation
Multi-reference Translation
Named-entity Recognition
Paraphrase Generation
Synthetic Chat data
Code instructions

You can find the dataset and all data sources of TowerBlocks here.

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="Unbabel/TowerInstruct-Mistral-7B-v0.2", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "Translate the following text from Portuguese into English.\nPortuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.\nEnglish:"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])
# <|im_start|>user
# Translate the following text from Portuguese into English.
# Portuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.
# English:<|im_end|>
# <|im_start|>assistant
# A group of researchers has launched a new model for translation-related tasks.

Out-of-Scope Use

The model is not guaranteed to perform for languages other than the 10 languages it supports. Even though we trained the model on conversational data and code instructions, it is not intended to be used as a conversational chatbot or code assistant. We are currently working on improving quality and consistency on document-level translation. This model should is not intended to be use as a document-level translator.

Bias, Risks, and Limitations

TowerInstruct-Mistral-7B-v0.2 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).

Prompt Format

TowerInstruct-Mistral-7B-v0.2 was trained using the ChatML prompt templates without any system prompts. An example follows below:

<|im_start|>user
{USER PROMPT}<|im_end|>
<|im_start|>assistant
{MODEL RESPONSE}<|im_end|>
<|im_start|>user
[...]

Supervised tasks

The prompts for all supervised tasks can be found in TowerBlocks. We have used multiple prompt templates for each task. While different prompts may offer different outputs, the difference in downstream performance should be very minimal.

Training Details

Training Data

Link to TowerBlocks.

Citation

@inproceedings{
alves2024tower,
title={Tower: An Open Multilingual Large Language Model for Translation-Related Tasks},
author={Duarte Miguel Alves and Jos{\'e} Pombal and Nuno M Guerreiro and Pedro Henrique Martins and Jo{\~a}o Alves and Amin Farajian and Ben Peters and Ricardo Rei and Patrick Fernandes and Sweta Agrawal and Pierre Colombo and Jos{\'e} G. C. de Souza and Andre Martins},
booktitle={First Conference on Language Modeling},
year={2024},
url={https://openreview.net/forum?id=EHPns3hVkj}
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	11.83
IFEval (0-Shot)	28.43
BBH (3-Shot)	14.22
MATH Lvl 5 (4-Shot)	1.59
GPQA (0-shot)	0.00
MuSR (0-shot)	15.96
MMLU-PRO (5-shot)	10.76