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@@ -29,11 +29,11 @@ We will release more details in the upcoming technical report.
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  - **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.
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  - **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
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  - **License:** CC-BY-NC-4.0
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- - **Finetuned from model:** TowerBase [ADD LINK]
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  ## Intended uses & limitations
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- The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset (TowerBlocks [ADD LINK]), which contains a diverse range of data sources:
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  - Translation
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  - Automatic Post Edition
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  - Machine Translation Evaluation
@@ -45,7 +45,7 @@ The model was initially fine-tuned on a filtered and preprocessed supervised fin
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  - Synthetic Chat data
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  - Code instructions
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- You can find the dataset and all data sources of TowerBlocks [ADD LINK] here.
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  Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
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@@ -95,13 +95,13 @@ TowerInstruct-v0.1 was trained using the ChatML prompt templates without any sys
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  ### Supervised tasks
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- The prompts for all supervised tasks can be found in TowerBlocks [ADD LINK]. 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.
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  ## Training Details
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  ### Training Data
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- Link to TowerBlocks [ADD LINK].
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  #### Training Hyperparameters
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  - **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.
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  - **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
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  - **License:** CC-BY-NC-4.0
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+ - **Finetuned from model:** [TowerBase](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)
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  ## Intended uses & limitations
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+ The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset ([TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)), which contains a diverse range of data sources:
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  - Translation
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  - Automatic Post Edition
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  - Machine Translation Evaluation
 
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  - Synthetic Chat data
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  - Code instructions
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+ You can find the dataset and all data sources of [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1) here.
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  Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
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  ### Supervised tasks
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+ The prompts for all supervised tasks can be found in [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1). 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.
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  ## Training Details
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  ### Training Data
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+ Link to [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1).
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  #### Training Hyperparameters
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