Update README.md

README.md

@@ -36,7 +36,7 @@ Aloe: A Family of Fine-tuned Open Healthcare LLMs
 
 Llama3.1-Aloe-8B-Beta is an **open healthcare LLM** (released with a permissive CC-BY license) achieving **state-of-the-art performance** on several medical tasks. Aloe Beta is made available in two model sizes: [8B](https://huggingface.co/HPAI-BSC/Llama31-Aloe-Beta-8B) and [70B](https://huggingface.co/HPAI-BSC/Llama31-Aloe-Beta-70B). Both models are trained using the same recipe. All necessary resources and details are made available below.
 
-Aloe is trained in 10 med9cal tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system ([also released](https://github.com/HPAI-BSC/prompt_engine)) the 8B version gets close to the performance of closed models like MedPalm-2, GPT4 and Medprompt. With the same RAG system, Aloe-Beta-70B outperforms those private alternatives, producing state-of-the-art results.
+Aloe is trained in 10 medical tasks, resulting in a robust and versatile healthcare model. Evaluations show Aloe models to be among the best in their class. When combined with a RAG system ([also released](https://github.com/HPAI-BSC/prompt_engine)) the 8B version gets close to the performance of closed models like MedPalm-2, GPT4 and Medprompt. With the same RAG system, Aloe-Beta-70B outperforms those private alternatives, producing state-of-the-art results.
 
 # Aloe-8B-Beta
 
@@ -102,7 +102,7 @@ These models are not to be used for clinical practice, medical diagnosis, or any
 
 Aloe can produce toxic content under the appropriate prompts, and it includes multiple undesirable biases. While significant efforts where conducted to mitigate this (see Alignment details below), model safety cannot be fully guaranteed. We avoid the use of all personal data in our training. 
 
-We identify at least three risk cases specific of healthcare LLMs:
+We identify at least three risk cases specific to healthcare LLMs:
 - Healthcare professional impersonation, a fraudulent behaviour which currently generates billions of dollars in [profit](https://www.justice.gov/opa/pr/justice-department-charges-dozens-12-billion-health-care-fraud). A model such as Aloe could be used to increase the efficacy of such deceiving activities, making them more widespread. The main preventive actions are public literacy on the unreliability of digitised information and the importance of medical registration, and legislation enforcing AI-generated content disclaimers. 
 - Medical decision-making without professional supervision. While this is already an issue in modern societies (eg self-medication) a model such as Aloe, capable of producing high-quality conversational data, can facilitate self-delusion, particularly in the presence of sycophancy. By producing tailored responses, it can also be used to generate actionable answers. Public literacy on the dangers of self-diagnosis is one of the main defenses, together with the introduction of disclaimers and warnings on the models' outputs. 
 - Access to information on dangerous substances or procedures. While the literature on sensitive content can already be found on different sources (eg libraries, the internet, dark web), LLMs can centralize such access, making it nearly impossible to control the flow of such information. Model alignment can help in that regard, but so far the effects remain insufficient, as jailbreaking methods still overcome it.
@@ -136,7 +136,7 @@ Use the code below to get started with the model. You can run conversational inf
 import transformers
 import torch
 
-model_id = "HPAI-BSC/Llama3-Aloe-8B-Beta"
+model_id = "HPAI-BSC/Llama31-Aloe-8B-Beta"
 
 pipeline = transformers.pipeline(
     "text-generation",