Text Generation
Transformers
PyTorch
English
llama
Python
Leetcode
Problem Solving
CP
text-generation-inference
Instructions to use Nan-Do/LeetCodeWizard_7B_V1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nan-Do/LeetCodeWizard_7B_V1.1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Nan-Do/LeetCodeWizard_7B_V1.1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Nan-Do/LeetCodeWizard_7B_V1.1") model = AutoModelForCausalLM.from_pretrained("Nan-Do/LeetCodeWizard_7B_V1.1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Nan-Do/LeetCodeWizard_7B_V1.1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Nan-Do/LeetCodeWizard_7B_V1.1" # 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_7B_V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Nan-Do/LeetCodeWizard_7B_V1.1
- SGLang
How to use Nan-Do/LeetCodeWizard_7B_V1.1 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_7B_V1.1" \ --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_7B_V1.1", "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_7B_V1.1" \ --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_7B_V1.1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Nan-Do/LeetCodeWizard_7B_V1.1 with Docker Model Runner:
docker model run hf.co/Nan-Do/LeetCodeWizard_7B_V1.1
File size: 2,262 Bytes
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"content": "β<EOT>",
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"β<EOT>"
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"eot_token": "β<EOT>",
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"tokenizer_file": "/root/.cache/huggingface/hub/models--WizardLM--WizardCoder-Python-7B-V1.0/snapshots/e40673a27a4aefcff2c6d2b3b1e0681a38703e4e/tokenizer.json",
"trust_remote_code": false,
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"use_default_system_prompt": false,
"use_fast": true
}
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