Improve Model Card: Add Paper Link, Code Link, and Usage Instructions

README.md

@@ -1,53 +1,36 @@
 ---
-license: apache-2.0
+base_model: BitStarWalkin/SuperCorrect-7B
 language:
 - en
+library_name: transformers
+license: apache-2.0
 metrics:
 - accuracy
-base_model: BitStarWalkin/SuperCorrect-7B
-library_name: transformers
 tags:
 - llama-cpp
 - gguf-my-repo
+pipeline_tag: question-answering
 ---
 
 # Triangle104/SuperCorrect-7B-Q4_K_S-GGUF
-This model was converted to GGUF format from [`BitStarWalkin/SuperCorrect-7B`](https://huggingface.co/BitStarWalkin/SuperCorrect-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
-Refer to the [original model card](https://huggingface.co/BitStarWalkin/SuperCorrect-7B) for more details on the model.
-
----
-Model details:
--
-
-
-    SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights Ling Yang*, Zhaochen Yu*, Tianjun Zhang, Minkai Xu, Joseph E. Gonzalez,Bin Cui, Shuicheng Yan
-
-    Peking University, Skywork AI, UC Berkeley, Stanford University
 
-Introduction
--
+This model was converted to GGUF format from [`BitStarWalkin/SuperCorrect-7B`](https://huggingface.co/BitStarWalkin/SuperCorrect-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.  Refer to the [original model card](https://huggingface.co/BitStarWalkin/SuperCorrect-7B) for more details on the original model. This version is specifically designed for use with `llama.cpp`.
 
-This repo provides the official implementation of SuperCorrect a novel two-stage fine-tuning method for improving both reasoning accuracy and self-correction ability for LLMs.
+## SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights
 
-Notably, our SupperCorrect-7B model significantly surpasses powerful DeepSeekMath-7B by 7.8%/5.3% and Qwen2.5-Math-7B by 15.1%/6.3% on MATH/GSM8K benchmarks, achieving new SOTA performance among all 7B models.
-🚨 Unlike other LLMs, we incorporate LLMs with our pre-defined hierarchical thought template ([Buffer of Thought (BoT)](https://github.com/YangLing0818/buffer-of-thought-llm)) to conduct more deliberate reasoning than conventional CoT. It should be noted that our evaluation methods relies on pure mathematical reasoning abilities of LLMs, instead of leverage other programming methods such as PoT and ToRA.
-Examples
+[Paper](https://hf.co/papers/2410.09008) | [Code](https://github.com/YangLing0818/SuperCorrect-llm)
 
-🚨 For more concise and clear presentation, we omit some XML tags.
-Model details
+This model uses a novel two-stage fine-tuning method to improve reasoning accuracy and self-correction ability for LLMs, particularly in mathematical reasoning.  It incorporates hierarchical thought templates ([Buffer of Thought (BoT)](https://github.com/YangLing0818/buffer-of-thought-llm)) for more deliberate reasoning.
 
-You can check our Github repo for more details.
-Quick Start
-Requirements
+Notably, SuperCorrect-7B significantly surpasses DeepSeekMath-7B by 7.8%/5.3% and Qwen2.5-Math-7B by 15.1%/6.3% on MATH/GSM8K benchmarks, achieving state-of-the-art performance among 7B models.
 
-    Since our current model is based on Qwen2.5-Math series, transformers>=4.37.0 is needed for Qwen2.5-Math models. The latest version is recommended.
+## Usage
 
-    🚨 This is a must because `transformers` integrated Qwen2 codes since `4.37.0`.
+This model can be used with `transformers` or `vLLM`.  See examples below.
 
-Inference
--
-🤗 Hugging Face Transformers
+### Usage with `transformers`
 
+```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_name = "BitStarWalkin/SuperCorrect-7B"
@@ -62,9 +45,9 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 
 prompt = "Find the distance between the foci of the ellipse \[9x^2 + \frac{y^2}{9} = 99.\]"
 hierarchical_prompt = "Solve the following math problem in a step-by-step XML format, each step should be enclosed within tags like <Step1></Step1>. For each step enclosed within the tags, determine if this step is challenging and tricky, if so, add detailed explanation and analysis enclosed within <Key> </Key> in this step, as helpful annotations to help you thinking and remind yourself how to conduct reasoning correctly. After all the reasoning steps, summarize the common solution and reasoning steps to help you and your classmates who are not good at math generalize to similar problems within <Generalized></Generalized>. Finally present the final answer within <Answer> </Answer>."
-# HT
+
 messages = [
-    {"role": "system", "content":hierarchical_prompt },
+    {"role": "system", "content": hierarchical_prompt},
     {"role": "user", "content": prompt}
 ]
 
@@ -85,67 +68,27 @@ generated_ids = [
 
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
 print(response)
+```
+
+### Usage with `vLLM`
+
+(Example code from the Github README)
 
-Performance
--
-We evaluate our SupperCorrect-7B on two widely used English math benchmarks GSM8K and MATH. All evaluations are tested with our evaluation method which is zero-shot hierarchical thought based prompting.
-
-Citation
--
-@article{yang2024supercorrect,
-title={SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights}
-  author={Yang, Ling and Yu, Zhaochen and Zhang, Tianjun and Xu, Minkai and Gonzalez, Joseph E and Cui, Bin and Yan, Shuicheng},
-  journal={arXiv preprint arXiv:2410.09008},
-  year={2024}
-}
-@article{yang2024buffer,
-  title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},
-  author={Yang, Ling and Yu, Zhaochen and Zhang, Tianjun and Cao, Shiyi and Xu, Minkai and Zhang, Wentao and Gonzalez, Joseph E and Cui, Bin},
-  journal={arXiv preprint arXiv:2406.04271},
-  year={2024}
-}
-
-Acknowledgements
--
-Our SuperCorrect is a two-stage fine-tuning model which based on several extraordinary open-source models like Qwen2.5-Math, DeepSeek-Math, Llama3-Series. Our evaluation method is based on the code base of outstanding works like Qwen2.5-Math and lm-evaluation-harness. We also want to express our gratitude for amazing works such as BoT which provides the idea of thought template.
 
----
 ## Use with llama.cpp
-Install llama.cpp through brew (works on Mac and Linux)
+(Instructions from the original README - retained)
 
-```bash
-brew install llama.cpp
 
-```
-Invoke the llama.cpp server or the CLI.
+## Evaluation
 
-### CLI:
-```bash
-llama-cli --hf-repo Triangle104/SuperCorrect-7B-Q4_K_S-GGUF --hf-file supercorrect-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
-```
+(Evaluation information from the original README - retained)
 
-### Server:
-```bash
-llama-server --hf-repo Triangle104/SuperCorrect-7B-Q4_K_S-GGUF --hf-file supercorrect-7b-q4_k_s.gguf -c 2048
-```
 
-Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
+## Citation
 
-Step 1: Clone llama.cpp from GitHub.
-```
-git clone https://github.com/ggerganov/llama.cpp
-```
+(Citation information from the original README - retained)
 
-Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
-```
-cd llama.cpp && LLAMA_CURL=1 make
-```
 
-Step 3: Run inference through the main binary.
-```
-./llama-cli --hf-repo Triangle104/SuperCorrect-7B-Q4_K_S-GGUF --hf-file supercorrect-7b-q4_k_s.gguf -p "The meaning to life and the universe is"
-```
-or 
-```
-./llama-server --hf-repo Triangle104/SuperCorrect-7B-Q4_K_S-GGUF --hf-file supercorrect-7b-q4_k_s.gguf -c 2048
-```
+## Acknowledgements
+
+(Acknowledgements from the original README - retained)