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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BitStarWalkin/SuperCorrect-7B")
model = AutoModelForCausalLM.from_pretrained("BitStarWalkin/SuperCorrect-7B", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 13, 2024

Commit

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@@ -32,6 +32,7 @@ Detailed performance and introduction are shown in our <a href="https://arxiv.or
 🚨 Unlike other LLMs, we incorporate LLMs with our pre-defined hierarchical thought template 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.
 </b>
 </div>
 ## Examples
 ![image](example1.png)

 🚨 Unlike other LLMs, we incorporate LLMs with our pre-defined hierarchical thought template 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.
 </b>
 </div>
 ## Examples
 ![image](example1.png)