Add question-answering pipeline tag and link to code

README.md

@@ -1,19 +1,21 @@
-
 ---
-
-license: apache-2.0
+base_model:
+- Qwen/Qwen2.5-Math-7B-Instruct
 language:
 - en
+library_name: transformers
+license: apache-2.0
 metrics:
 - accuracy
-base_model:
-- Qwen/Qwen2.5-Math-7B-Instruct
-library_name: transformers
-
+pipeline_tag: question-answering
 ---
 
 [![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
 
+This repository contains a quantized version of the model presented in [SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights](https://huggingface.co/papers/2410.09008).
+The original model card can be found [here](https://huggingface.co/BitStarWalkin/SuperCorrect-7B).
+
+Code: https://github.com/YangLing0818/SuperCorrect-llm
 
 # QuantFactory/SuperCorrect-7B-GGUF
 This is quantized version of [BitStarWalkin/SuperCorrect-7B](https://huggingface.co/BitStarWalkin/SuperCorrect-7B) created using llama.cpp
@@ -140,4 +142,4 @@ title={SuperCorrect: Supervising and Correcting Language Models with Error-Drive
 
 ## Acknowledgements
 
-Our SuperCorrect is a two-stage fine-tuning model which based on several extraordinary open-source models like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), [DeepSeek-Math](https://github.com/deepseek-ai/DeepSeek-Math), [Llama3-Series](https://github.com/meta-llama/llama3). Our evaluation method is based on the code base of outstanding works like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math) and  [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). We also want to express our gratitude for amazing works such as [BoT](https://github.com/YangLing0818/buffer-of-thought-llm) which provides the idea of thought template.
+Our SuperCorrect is a two-stage fine-tuning model which based on several extraordinary open-source models like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math), [DeepSeek-Math](https://github.com/deepseek-ai/DeepSeek-Math), [Llama3-Series](https://github.com/meta-llama/llama3). Our evaluation method is based on the code base of outstanding works like [Qwen2.5-Math](https://github.com/QwenLM/Qwen2.5-Math) and  [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). We also want to express our gratitude for amazing works such as [BoT](https://github.com/YangLing0818/buffer-of-thought-llm) which provides the idea of thought template.