Instructions to use Rakhman16/program-synthesis-java-codet5-xlcost with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Rakhman16/program-synthesis-java-codet5-xlcost with Transformers:

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

pipe = pipeline("text-generation", model="Rakhman16/program-synthesis-java-codet5-xlcost")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Rakhman16/program-synthesis-java-codet5-xlcost")
model = AutoModelForSeq2SeqLM.from_pretrained("Rakhman16/program-synthesis-java-codet5-xlcost")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Rakhman16/program-synthesis-java-codet5-xlcost with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rakhman16/program-synthesis-java-codet5-xlcost"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rakhman16/program-synthesis-java-codet5-xlcost",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Rakhman16/program-synthesis-java-codet5-xlcost

SGLang

How to use Rakhman16/program-synthesis-java-codet5-xlcost 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 "Rakhman16/program-synthesis-java-codet5-xlcost" \
    --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": "Rakhman16/program-synthesis-java-codet5-xlcost",
		"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 "Rakhman16/program-synthesis-java-codet5-xlcost" \
        --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": "Rakhman16/program-synthesis-java-codet5-xlcost",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Rakhman16/program-synthesis-java-codet5-xlcost with Docker Model Runner:
```
docker model run hf.co/Rakhman16/program-synthesis-java-codet5-xlcost
```

Rakhman16 commited on Dec 9, 2024

Commit

a3b0eb2

verified ·

1 Parent(s): cf1ae19

Update README.md

Browse files

Files changed (1) hide show

README.md +13 -3

README.md CHANGED Viewed

@@ -1,3 +1,13 @@
----
-license: apache-2.0
----

+---
+license: apache-2.0
+datasets:
+- codeparrot/xlcost-text-to-code
+language:
+- en
+base_model:
+- Salesforce/codet5-base
+pipeline_tag: text2text-generation
+library_name: adapter-transformers
+tags:
+- code
+---