Instructions to use nlpctx/codet5-java-optimizer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpctx/codet5-java-optimizer with Transformers:

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

pipe = pipeline("text-generation", model="nlpctx/codet5-java-optimizer")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("nlpctx/codet5-java-optimizer")
model = AutoModelForMultimodalLM.from_pretrained("nlpctx/codet5-java-optimizer")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nlpctx/codet5-java-optimizer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpctx/codet5-java-optimizer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpctx/codet5-java-optimizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nlpctx/codet5-java-optimizer

SGLang

How to use nlpctx/codet5-java-optimizer 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 "nlpctx/codet5-java-optimizer" \
    --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": "nlpctx/codet5-java-optimizer",
		"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 "nlpctx/codet5-java-optimizer" \
        --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": "nlpctx/codet5-java-optimizer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nlpctx/codet5-java-optimizer with Docker Model Runner:
```
docker model run hf.co/nlpctx/codet5-java-optimizer
```

Unknown Haas commited on 20 days ago

Commit

2f9309e

1 Parent(s): c5bb003

Add HF metadata, links, and Apache 2.0 license

Browse files

Files changed (1) hide show

README.md +31 -8

README.md CHANGED Viewed

@@ -1,6 +1,29 @@
 # CodeT5-small Java Optimization Model
-A fine-tuned Salesforce/codet5-small model for Java code optimization tasks.
 ## Overview
@@ -28,12 +51,12 @@ This repository contains a fine-tuned CodeT5-small model specifically trained fo
 ## Usage
 ```python
-from transformers import T5ForConditionalGeneration, T5Tokenizer
 import torch
 # Load model and tokenizer
-model = T5ForConditionalGeneration.from_pretrained("model_directory")
-tokenizer = T5Tokenizer.from_pretrained("model_directory")
 # Prepare input Java code
 java_code = "your Java code here"
@@ -73,10 +96,10 @@ The model was fine-tuned using:
 ## License
-This model is provided for educational and demonstration purposes.
 ## Acknowledgements
-- Model based on Salesforce/codet5-small
-- Training data from nlpctx/java_optimisation dataset
-- Built with HuggingFace Transformers

+---
+language: en
+license: apache-2.0
+library_name: transformers
+tags:
+- code
+- java
+- codet5
+- optimization
+- code-generation
+datasets:
+- nlpctx/java_optimisation
+base_model: Salesforce/codet5-small
+pipeline_tag: text2text-generation
+model-index:
+- name: codet5-java-optimizer
+  results: []
+---
 # CodeT5-small Java Optimization Model
+A fine-tuned [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) model for Java code optimization tasks.
+- **Model**: [nlpctx/codet5-java-optimizer](https://huggingface.co/nlpctx/codet5-java-optimizer)
+- **Dataset**: [nlpctx/java_optimisation](https://huggingface.co/datasets/nlpctx/java_optimisation)
+- **Base Model**: [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small)
 ## Overview
 ## Usage
 ```python
+from transformers import T5ForConditionalGeneration, RobertaTokenizer
 import torch
 # Load model and tokenizer
+model = T5ForConditionalGeneration.from_pretrained("nlpctx/codet5-java-optimizer")
+tokenizer = RobertaTokenizer.from_pretrained("nlpctx/codet5-java-optimizer")
 # Prepare input Java code
 java_code = "your Java code here"
 ## License
+This model is licensed under the **Apache 2.0** license, matching the original [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small) model.
 ## Acknowledgements
+- Model based on [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small)
+- Training data from [nlpctx/java_optimisation](https://huggingface.co/datasets/nlpctx/java_optimisation) dataset
+- Built with [HuggingFace Transformers](https://github.com/huggingface/transformers)