Update README.md

README.md

@@ -53,7 +53,7 @@ This model is part of the Minerva LLM family:
 
 *This section identifies foreseeable harms and misunderstandings.*
 
-This is a chat foundation model, subject to some degree of alignment. However, the model may still:
+This is a chat foundation model, subject to model alignment and safety risk mitigation strategies. However, the model may still:
 
 -   Overrepresent some viewpoints and underrepresent others
 -   Contain stereotypes
@@ -87,7 +87,7 @@ pipeline = transformers.pipeline(
 )
 
 # Input text for the model.
-input_conv = [{"role": "user", "content": "Qualle è la capitale dell'Italia?"}]
+input_conv = [{"role": "user", "content": "Qual è la capitale dell'Italia?"}]
 
 # Compute the outputs.
 output = pipeline(
@@ -103,7 +103,7 @@ output
 
 ## Model Architecture
 
-Minerva-7B-base-v1.0 is a Transformer model based on the Mistral architecture.
+Minerva-7B-base-v1.0 is a Transformer model based on the [Mistral](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) architecture.
 Please look at the configuration file for a detailed breakdown of the hyperparameters we chose for this model.
 
 The Minerva LLM family is composed of:
@@ -132,23 +132,23 @@ The SFT model was trained using [Llama-Factory](https://github.com/hiyouga/LLaMA
 
 | Dataset                              | Source                                                                 | Code     | English | Italian |
 |--------------------------------------|------------------------------------------------------------------------|----------|---------|---------|
+| Glaive-code-assistant                | [Link](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)  | 100,000  | 0       | 0       |
+| Alpaca-python                        | [Link](https://huggingface.co/datasets/Vezora/Tested-143k-Python-Alpaca) | 20,000   | 0       | 0       |
 | Alpaca-cleaned                       | [Link](https://huggingface.co/datasets/yahma/alpaca-cleaned)            | 0        | 50,000  | 0       |
 | Databricks-dolly-15k                 | [Link](https://huggingface.co/datasets/databricks/databricks-dolly-15k) | 0        | 15,011  | 0       |
 | No-robots                            | [Link](https://huggingface.co/datasets/HuggingFaceH4/no_robots)         | 0        | 9,499   | 0       |
 | OASST2                               | [Link](https://huggingface.co/datasets/OpenAssistant/oasst2)            | 0        | 29,000  | 528     |
-| Tower-blocks_it                      | [Link](https://huggingface.co/datasets/sapienzanlp/tower_blocks-v0.2_it) | 0        | 0       | 7,276   |
-| Glaive-code-assistant                | [Link](https://huggingface.co/datasets/glaiveai/glaive-code-assistant)  | 100,000  | 0       | 0       |
-| Alpaca-python                        | [Link](https://huggingface.co/datasets/Vezora/Tested-143k-Python-Alpaca) | 20,000   | 0       | 0       |
 | WizardLM                             | [Link](https://huggingface.co/datasets/WizardLMTeam/WizardLM_evol_instruct_70k) | 0  | 29,810  | 0       |
 | LIMA                                 | [Link](https://huggingface.co/datasets/GAIR/lima?row=0)                 | 0        | 1,000   | 0       |
 | OPENORCA                              | [Link](https://huggingface.co/datasets/Open-Orca/OpenOrca)              | 0        | 30,000  | 0       |
 | Ultrachat                             | [Link](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k)     | 0        | 50,000  | 0       |
 | MagpieMT                             | [Link](https://huggingface.co/datasets/Magpie-Align/Magpie-Pro-MT-300K-v0.1) | 0     | 30,000  | 0       |
 | Tulu-V2-Science                      | [Link](https://huggingface.co/datasets/allenai/tulu-v2-sft-mixture)    | 0        | 7,000   | 0       |
+| Aya_datasets                         | [Link](http://CohereForAI/aya_dataset)                                  | 0        | 3,944   | 738     |
+| Tower-blocks_it                      | [Link](https://huggingface.co/datasets/sapienzanlp/tower_blocks-v0.2_it) | 0        | 0       | 7,276   |
 | Bactrian-X                           | [Link](https://huggingface.co/datasets/MBZUAI/Bactrian-X)               | 0        | 0       | 67,000  |
 | Magpie (*Translated by us*)          | -                                                                      | 0        | 0       | 60,000  |
 | Everyday-conversations (*Translated by us*) | -                                                                  | 0        | 0       | 2,260   |
-| Aya_datasets                         | [Link](http://CohereForAI/aya_dataset)                                  | 0        | 3,944   | 738     |
 | alpaca-gpt4-it                       | [Link](https://huggingface.co/datasets/efederici/alpaca-gpt4-it)        | 0        | 0       | 15,000  |
 | capybara-claude-15k-ita              | [Link](https://huggingface.co/datasets/efederici/capybara-claude-15k-ita) | 0       | 0       | 15,000  |
 | Wildchat                             | [Link](https://huggingface.co/datasets/allenai/WildChat-1M)            | 0        | 0       | 5,000   |
@@ -156,13 +156,13 @@ The SFT model was trained using [Llama-Factory](https://github.com/hiyouga/LLaMA
 | Safety Italian                       | -                                                                      | 0        | 0       | 21,000  |
 | Handmade Italian                     | -                                                                      | 0        | 0       | 2,000   |
 
-For more details please check [our tech report](https://nlp.uniroma1.it/minerva/blog#from-a-base-model-to-an-instruct-model).
+For more details, please check [our tech report](https://nlp.uniroma1.it/minerva/blog#from-a-base-model-to-an-instruct-model).
 
 ### Online DPO Training
 
-This model card is for our DPO model. Direct Preference Optimization (DPO) is a method that refines models based on user feedback, similar to Reinforcement Learning from Human Feedback (RLHF), but without the complexity of reinforcement learning. Online DPO further improves this by allowing real-time adaptation during training, continuously refining the model with new feedback. For training this model, we used the [Hugging Face TRL](https://github.com/huggingface/trl) library and Online DPO, with the [Skywork/Skywork-Reward-Llama-3.1-8B-v0.2](https://huggingface.co/Skywork/Skywork-Reward-Llama-3.1-8B-v0.2) model as the judge to evaluate and guide optimization. For this stage we used just the prompts from HuggingFaceH4/ultrafeedback_binarized (English), efederici/evol-dpo-ita (Italian) and Babelscape/ALERT translated to Italian were used, with additional manually curated data for safety.
+This model card is for our DPO model. Direct Preference Optimization (DPO) is a method that refines models based on user feedback, similar to Reinforcement Learning from Human Feedback (RLHF), but without the complexity of reinforcement learning. Online DPO further improves this by allowing real-time adaptation during training, continuously refining the model with new feedback. For training this model, we used the [Hugging Face TRL](https://github.com/huggingface/trl) library and Online DPO, with the [Skywork/Skywork-Reward-Llama-3.1-8B-v0.2](https://huggingface.co/Skywork/Skywork-Reward-Llama-3.1-8B-v0.2) model as the judge to evaluate and guide optimization. For this stage we used just the prompts from HuggingFaceH4/ultrafeedback_binarized (English), efederici/evol-dpo-ita (Italian) and Babelscape/ALERT translated to Italian, with additional manually curated data for safety.
 
-For more details please check [our tech report](https://nlp.uniroma1.it/minerva/blog#from-a-base-model-to-an-instruct-model).
+For more details, please check [our tech report](https://nlp.uniroma1.it/minerva/blog#from-a-base-model-to-an-instruct-model).
 
 ## Model Evaluation
 
@@ -275,5 +275,5 @@ Minerva-7B-base-v1.0 is a pretrained base model and, therefore, has no moderatio
 
 ## Acknowledgments
 
-This work was funded by the PNRR MUR project [PE0000013-FAIR](https://fondazione-fair.it).
-We acknowledge the [CINECA](https://www.cineca.it) award "IscB_medit" under the ISCRA initiative, for the availability of high performance computing resources and support.
+This work was funded by the PNRR MUR project [PE0000013-FAIR](https://fondazione-fair.it) and the CREATIVE PRIN Project.
+We acknowledge the [CINECA](https://www.cineca.it) award "IscB_medit" under the ISCRA initiative for the availability of high-performance computing resources and support.