Update README.md

README.md

@@ -15,13 +15,13 @@ tags:
 
 XXXXQuantized is a compact iteration of the model [XXXX](https://huggingface.co/MoxoffSpA/xxxx), optimized for efficiency.
 
-It is offered in two distinct configurations: a 4-bit version and an 8-bit version, each designed to maintain the model's effectiveness while significantly reducing its size.
+It is offered in two distinct configurations: a 4-bit version and an 8-bit version, each designed to maintain the model's effectiveness while significantly reducing its size
 and computational requirements.
 
 - It's trained both on publicly available datasets, like [SQUAD-it](https://huggingface.co/datasets/squad_it), and datasets we've created in-house.
 - it's designed to understand and maintain context, making it ideal for Retrieval Augmented Generation (RAG) tasks and applications requiring contextual awareness.
-- It is quantized in a 4-bit version and an 8-bit version suing the prcedure [here](https://github.com/ggerganov/llama.cpp).
-- 
+- It is quantized in a 4-bit version and an 8-bit version folllowing the procedure [here](https://github.com/ggerganov/llama.cpp).
+
 # Evaluation
 
 We evaluated the model using the same test sets as used for the [Open Ita LLM Leaderboard](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard):
@@ -60,7 +60,7 @@ print(decoded[0])
 
 ## Bias, Risks and Limitations
 
-xxxx has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
+xxxxQuantized and its original model [xxxx](https://huggingface.co/MoxoffSpA/xxxx) has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
 responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition 
 of the corpus was used to train the base model [mistralai/Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-v0.2), however it is likely to have included a mix of Web data and technical sources 
 like books and code.