Instructions to use mhhmm/codegen-6B-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mhhmm/codegen-6B-lora with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mhhmm/codegen-6B-lora")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mhhmm/codegen-6B-lora") model = AutoModelForCausalLM.from_pretrained("mhhmm/codegen-6B-lora") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use mhhmm/codegen-6B-lora with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mhhmm/codegen-6B-lora" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mhhmm/codegen-6B-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/mhhmm/codegen-6B-lora
- SGLang
How to use mhhmm/codegen-6B-lora 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 "mhhmm/codegen-6B-lora" \ --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": "mhhmm/codegen-6B-lora", "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 "mhhmm/codegen-6B-lora" \ --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": "mhhmm/codegen-6B-lora", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use mhhmm/codegen-6B-lora with Docker Model Runner:
docker model run hf.co/mhhmm/codegen-6B-lora
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README.md
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library_name: transformers
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pipeline_tag: text-generation
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---
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LLM: [Salesforce/CodeGen-6B-Mono](https://huggingface.co/Salesforce/codegen-6B-mono)
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- en
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library_name: transformers
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pipeline_tag: text-generation
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# You are given a 0-indexed m x n integer matrix grid. The width of a column is the maximum length of its integers.
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# For example, if grid = [[-10], [3], [12]], the width of the only column is 3 since -10 is of length 3.
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# Return an integer array ans of size n where ans[i] is the width of the ith column.
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# The length of an integer x with len digits is equal to len if x is non-negative, and len + 1 otherwise.
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class Solution:
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def findColumnWidth(self, grid: List[List[int]]) -> List[int]:
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example_title: "New Medium Problem on Leetcode"
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---
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LLM: [Salesforce/CodeGen-6B-Mono](https://huggingface.co/Salesforce/codegen-6B-mono)
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