Instructions to use BitStarWalkin/SuperCorrect-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BitStarWalkin/SuperCorrect-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="BitStarWalkin/SuperCorrect-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BitStarWalkin/SuperCorrect-7B")
model = AutoModelForCausalLM.from_pretrained("BitStarWalkin/SuperCorrect-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use BitStarWalkin/SuperCorrect-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BitStarWalkin/SuperCorrect-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BitStarWalkin/SuperCorrect-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BitStarWalkin/SuperCorrect-7B

SGLang

How to use BitStarWalkin/SuperCorrect-7B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "BitStarWalkin/SuperCorrect-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BitStarWalkin/SuperCorrect-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "BitStarWalkin/SuperCorrect-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BitStarWalkin/SuperCorrect-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BitStarWalkin/SuperCorrect-7B with Docker Model Runner:
```
docker model run hf.co/BitStarWalkin/SuperCorrect-7B
```

BitStarWalkin commited on Oct 14, 2024

Commit

b20da88

verified ·

1 Parent(s): fc5b36d

Update README.md

Browse files

Files changed (1) hide show

README.md +8 -6

README.md CHANGED Viewed

@@ -6,8 +6,8 @@
 > Peking University, Skywork AI, UC Berkeley, Stanford University
 <p align="left">
-  <a href='https://arxiv.org/abs/to be edited'>
-  <img src='https://img.shields.io/badge/Arxiv-2410.07171-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a>
   <a href='https://huggingface.co/BitStarWalkin/SuperCorrect-7B'>
   <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow'></a>
   </p>
@@ -20,7 +20,7 @@ This repo provides the official implementation of **SuperCorrect**  a novel two-
 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.
-Detailed performance and introduction are shown in our <a href="https://arxiv.org/"> 📑 Paper</a>.
 <div align="left">
 🚨 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.
@@ -32,7 +32,7 @@ Detailed performance and introduction are shown in our <a href="https://arxiv.or
 <div align="left">
 <b>
-🚨 For more concise and clear presentation, we omit some XML tags
 </b>
 </div>
@@ -106,9 +106,11 @@ We evaluate our SupperCorrect-7B on two widely used English math benchmarks GSM8
 ## Citation
 ```bash
-@article{yang2024super,
 title={SuperCorrect: Supervising and Correcting Language Models with Error-Driven Insights}
-SuperCorrect
 }
 @article{yang2024buffer,
   title={Buffer of Thoughts: Thought-Augmented Reasoning with Large Language Models},

 > Peking University, Skywork AI, UC Berkeley, Stanford University
 <p align="left">
+  <a href='https://arxiv.org/abs/2410.09008'>
+  <img src='https://img.shields.io/badge/Arxiv-2410.09008-A42C25?style=flat&logo=arXiv&logoColor=A42C25'></a>
   <a href='https://huggingface.co/BitStarWalkin/SuperCorrect-7B'>
   <img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-yellow'></a>
   </p>
 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.
+Detailed performance and introduction are shown in our <a href="https://arxiv.org/abs/2410.09008"> 📑 Paper</a>.
 <div align="left">
 🚨 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.
 <div align="left">
 <b>
+🚨 For more concise and clear presentation, we omit some XML tags.
 </b>
 </div>
 ## Citation
 ```bash
+@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},