Instructions to use jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2 with Transformers:

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

pipe = pipeline("text-generation", model="jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2")
model = AutoModelForCausalLM.from_pretrained("jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2

SGLang

How to use jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2 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 "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2" \
    --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": "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2",
		"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 "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2" \
        --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": "jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2 with Docker Model Runner:
```
docker model run hf.co/jacobhoffmann/CodeLlama-13B-TestGen-Dart_v0.2
```

jacobhoffmann commited on Dec 11, 2024

Commit

7a8cd64

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1 Parent(s): d3d6a56

Update README.md

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@@ -32,7 +32,7 @@ This model card provides information about **TestGen-Dart v0.2**, a fine-tuned v
 ### Model Sources
 - **Repository:** [GitHub Repository](https://github.com/example/repo) (placeholder)
-- **Paper:** ["Test Case Generation with Fine-Tuned LLaMA Models"](https://doi.org/10.1145/3644032.3644454) (published in AST '24)
 - **Demo:** Coming soon
 ---
@@ -145,9 +145,9 @@ If you use this model in your research, please cite:
 **BibTeX:**
 ```bibtex
 @inproceedings{hoffmann2024testgen,
-  title={Test Case Generation with Fine-Tuned LLaMA Models},
   author={Hoffmann, Jacob and Frister, Demian},
-  booktitle={Proceedings of the 29th ACM/SIGSOFT International Workshop on Automated Software Testing (AST)},
   year={2024},
   doi={10.1145/3644032.3644454}
 }

 ### Model Sources
 - **Repository:** [GitHub Repository](https://github.com/example/repo) (placeholder)
+- **Paper:** ["Generating Software Tests for Mobile Applications Using Fine-Tuned Large Language Models"](https://doi.org/10.1145/3644032.3644454) (published in AST '24)
 - **Demo:** Coming soon
 ---
 **BibTeX:**
 ```bibtex
 @inproceedings{hoffmann2024testgen,
+  title={Generating Software Tests for Mobile Applications Using Fine-Tuned Large Language Models},
   author={Hoffmann, Jacob and Frister, Demian},
+  booktitle={Proceedings of the 5th ACM/IEEE International Conference on Automation of Software Test (AST 2024)},
   year={2024},
   doi={10.1145/3644032.3644454}
 }