Instructions to use InfosysEnterprise/NT-Java-1.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use InfosysEnterprise/NT-Java-1.1B with Transformers:

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

pipe = pipeline("text-generation", model="InfosysEnterprise/NT-Java-1.1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("InfosysEnterprise/NT-Java-1.1B")
model = AutoModelForMultimodalLM.from_pretrained("InfosysEnterprise/NT-Java-1.1B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use InfosysEnterprise/NT-Java-1.1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "InfosysEnterprise/NT-Java-1.1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "InfosysEnterprise/NT-Java-1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/InfosysEnterprise/NT-Java-1.1B

SGLang

How to use InfosysEnterprise/NT-Java-1.1B 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 "InfosysEnterprise/NT-Java-1.1B" \
    --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": "InfosysEnterprise/NT-Java-1.1B",
		"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 "InfosysEnterprise/NT-Java-1.1B" \
        --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": "InfosysEnterprise/NT-Java-1.1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use InfosysEnterprise/NT-Java-1.1B with Docker Model Runner:
```
docker model run hf.co/InfosysEnterprise/NT-Java-1.1B
```

rajabmondal commited on May 23, 2024

Commit

68c6170

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@@ -107,10 +107,6 @@ print(tokenizer.decode(outputs[0]))
 The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
-# Benefits
-We evaluated NT-Java-1.1B across various coding tasks and compared its performance against models with similar parameters. Our findings indicate that NT-Java-1.1B is competitive with or outperforms other Code SLMs in this parameter range, particularly in Java programming tasks.
 # Limitations
 The model, NT-Java-1.1B, has been trained on publicly available datasets and comes without any safety guarantees. Due to this, like all Language Models, its outputs cannot be reliably predicted and sometimes the generated code is not guaranteed to work as intended. It can also be inefficient and may contain bugs or exploits. Therefore, it's crucial for users and developers to conduct thorough safety testing and implement filtering mechanisms tailored to their needs.

 The pretraining dataset of the model was filtered for permissive licenses only. Nevertheless, the model can generate source code verbatim from the dataset. The code's license might require attribution and/or other specific requirements that must be respected. We provide a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that let's you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
 # Limitations
 The model, NT-Java-1.1B, has been trained on publicly available datasets and comes without any safety guarantees. Due to this, like all Language Models, its outputs cannot be reliably predicted and sometimes the generated code is not guaranteed to work as intended. It can also be inefficient and may contain bugs or exploits. Therefore, it's crucial for users and developers to conduct thorough safety testing and implement filtering mechanisms tailored to their needs.