Instructions to use Nan-Do/LeetCodeWizard_13B_v1.1a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nan-Do/LeetCodeWizard_13B_v1.1a with Transformers:

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

pipe = pipeline("text-generation", model="Nan-Do/LeetCodeWizard_13B_v1.1a")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nan-Do/LeetCodeWizard_13B_v1.1a")
model = AutoModelForCausalLM.from_pretrained("Nan-Do/LeetCodeWizard_13B_v1.1a")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nan-Do/LeetCodeWizard_13B_v1.1a with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nan-Do/LeetCodeWizard_13B_v1.1a"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nan-Do/LeetCodeWizard_13B_v1.1a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nan-Do/LeetCodeWizard_13B_v1.1a

SGLang

How to use Nan-Do/LeetCodeWizard_13B_v1.1a 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 "Nan-Do/LeetCodeWizard_13B_v1.1a" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nan-Do/LeetCodeWizard_13B_v1.1a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Nan-Do/LeetCodeWizard_13B_v1.1a" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nan-Do/LeetCodeWizard_13B_v1.1a",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nan-Do/LeetCodeWizard_13B_v1.1a with Docker Model Runner:
```
docker model run hf.co/Nan-Do/LeetCodeWizard_13B_v1.1a
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

LeetCodeWizard: A LLM for mastering programming interviews and solving programming problems.

What is LeetCodeWizard?

LeetCodeWizard is a coding large language model specifically trained to solve and explain Leetcode (or any) programming problems.

How was the model developed?

This model is a fine-tuned version of the WizardCoder-Python-13B with a dataset of Leetcode problems

Model capabilities:

It should be able to solve most of the problems found at Leetcode and even pass the sample interviews they offer on the site.
It can write both the code and the explanations for the solutions.

Prompt template:

This model uses the alpaca instruction/response prompt style (the input field is not neccesary).

Model	Checkpoint	Base Model
LeetCodeWizard-13B-V1.1	🤗 HF Link	WizardCoder-Python-13B-V1.1
LeetCodeWizard-7B-V1.1	🤗 HF Link	WizardCoder-Python-7B-V1.1

Downloads last month: 7

Model tree for Nan-Do/LeetCodeWizard_13B_v1.1a

Quantizations

3 models