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")

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

tokenizer = AutoTokenizer.from_pretrained("InfosysEnterprise/NT-Java-1.1B")
model = AutoModelForCausalLM.from_pretrained("InfosysEnterprise/NT-Java-1.1B", device_map="auto")

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 10, 2024

Commit

558d010

verified ·

1 Parent(s): 5ce8d3f

updated model summary

Browse files

Files changed (1) hide show

README.md +2 -3

README.md CHANGED Viewed

@@ -53,13 +53,13 @@ duplicated_from: bigcode-data/starcoderbase-1b
 ## Model Summary
-The Narrow Transformer (NT) model NT-Java-1.1B is an open-source specialized code model built by extending pre-training on starcoderbase-1b, designed for code related tasks in Java programming. The model is a decoder-only transformer with Multi-Query-Attention and a context length of 8192 tokens. The model has been trained with Java subset of the starcoderdata dataset, which is ~22B tokens.
 - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
 - **Project Website:**
 - **Paper:**
 - **Point of Contact:**
-- **Languages:** Java
 ## Use
@@ -115,7 +115,6 @@ The model, NT-Java-1.1B, has been trained on publicly available datasets and com
 - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
 - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
-- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
 # License
 The model checkpoint and vocabulary file are licensed under the [BigCode OpenRAIL-M v1](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) . Under the license, you must evaluate if your use case does not violate the use-case restriction under Attachment A of the License.  Any modification of the model (finetuning or extended pre training) for further downstream task needs to be released under [BigCode OpenRAIL-M v1](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).

 ## Model Summary
+The Narrow Transformer (NT) model NT-Java-1.1B is an open-source specialized code model built by extending pre-training on StarCoderBase-1B, designed for coding tasks in Java programming. The model is a decoder-only transformer with Multi-Query-Attention and with a context length of 8192 tokens. The model was trained with Java subset of the StarCoderData dataset, which is ~22B tokens.
 - **Repository:** [bigcode/Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
 - **Project Website:**
 - **Paper:**
 - **Point of Contact:**
+- **Language(s):** Java
 ## Use
 - **Orchestration:** [Megatron-LM](https://github.com/bigcode-project/Megatron-LM)
 - **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
 # License
 The model checkpoint and vocabulary file are licensed under the [BigCode OpenRAIL-M v1](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement) . Under the license, you must evaluate if your use case does not violate the use-case restriction under Attachment A of the License.  Any modification of the model (finetuning or extended pre training) for further downstream task needs to be released under [BigCode OpenRAIL-M v1](https://huggingface.co/spaces/bigcode/bigcode-model-license-agreement).