task1728_web_nlg_data_to_text

README.md

@@ -1,9 +1,10 @@
 ---
-library_name: transformers
-tags: []
+language: en
+license: mit
+library_name: keras
 ---
 
-# Model Card for Model ID
+# Model Card for Mistral-7B-Instruct-v0.2-4b-r16-task1728
 
 <!-- Provide a quick summary of what the model is/does. -->
 
@@ -15,22 +16,22 @@ tags: []
 
 <!-- Provide a longer summary of what this model is. -->
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+LoRA trained on task1728_web_nlg_data_to_text
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** bruel
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Model type:** LoRA
+- **Language(s) (NLP):** en
+- **License:** mit
+- **Finetuned from model [optional]:** mistralai/Mistral-7B-Instruct-v0.2
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
+- **Repository:** https://github.com/bruel-gabrielsson
+- **Paper [optional]:** "Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead" (2024), Rickard Brüel Gabrielsson, Jiacheng Zhu, Onkar Bhardwaj, Leshem Choshen, Kristjan Greenewald, Mikhail Yurochkin and Justin Solomon
 - **Demo [optional]:** [More Information Needed]
 
 ## Uses
@@ -79,7 +80,7 @@ Use the code below to get started with the model.
 
 <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
-[More Information Needed]
+"task1728_web_nlg_data_to_text" sourced from https://github.com/allenai/natural-instructions
 
 ### Training Procedure