Add library_name and pipeline_tag to metadata (#1)

- Add library_name and pipeline_tag to metadata (0bd84002402beae5f44778deb57f07378ab6b121)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

README.md

@@ -1,8 +1,10 @@
 ---
-license: apache-2.0
 language:
 - it
 - en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
 ---
 
 # Llama-3.1-8B-Italian-FVT
@@ -14,9 +16,9 @@ language:
 
 The **Llama-3.1-8B-Adapted** collection of large language models (LLMs), is a collection of adapted generative models in 8B (text in/text out), adapted models from **Llama-3.1-8B**.
 
-*Llama-3.1-8B-Italian-FVT* is a continual trained mistral model, after tokenizer substitution.
+*Llama-3.1-8B-Italian-FVT* is a continually trained Mistral model, after tokenizer substitution.
 
-The tokenizer of this models after adaptation is the same of [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
+The tokenizer of this model after adaptation is the same as [Minverva-3B](https://huggingface.co/sapienzanlp/Minerva-3B-base-v1.0).
 
 **Model developer:** SapienzaNLP, ISTI-CNR, ILC-CNR
 
@@ -24,15 +26,15 @@ The tokenizer of this models after adaptation is the same of [Minverva-3B](https
 
 ## Data used for the adaptation
 
-The **Mistral-7B-v0.1-Adapted** model are trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
-The data are extracted to be skewed toward Italian language with a ration of one over four. Extracting the first 9B tokens from Italian part of CulturaX and the first 3B tokens from English part of CulturaX.
+The **Mistral-7B-v0.1-Adapted** model was trained on a collection of Italian and English data extracted from [CulturaX](https://huggingface.co/datasets/uonlp/CulturaX).
+The data was extracted to be skewed toward Italian language with a ratio of one over four. Extracting the first 9B tokens from the Italian part of CulturaX and the first 3B tokens from the English part of CulturaX.
 
 
 ## Use with Transformers
 
 You can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
 
-Make sure to update your transformers installation via pip install --upgrade transformers.
+Make sure to update your transformers installation via `pip install --upgrade transformers`.
 
 ```python
 import transformers