Text Generation
Transformers
PyTorch
code
gpt_bigcode
NarrowTransformer
Eval Results (legacy)
text-generation-inference
Instructions to use InfosysEnterprise/NT-Java-1.1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("InfosysEnterprise/NT-Java-1.1B") model = AutoModelForCausalLM.from_pretrained("InfosysEnterprise/NT-Java-1.1B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- 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
| { | |
| "activation_function": "gelu_pytorch_tanh", | |
| "architectures": [ | |
| "GPTBigCodeForCausalLM" | |
| ], | |
| "attention_softmax_in_fp32": true, | |
| "attn_pdrop": 0.1, | |
| "bos_token_id": 50256, | |
| "embd_pdrop": 0.1, | |
| "eos_token_id": 50256, | |
| "initializer_range": 0.02, | |
| "layer_norm_epsilon": 1e-05, | |
| "model_type": "gpt_bigcode", | |
| "multi_query": true, | |
| "n_embd": 2048, | |
| "n_head": 16, | |
| "n_inner": 8192, | |
| "n_layer": 24, | |
| "n_positions": 8192, | |
| "resid_pdrop": 0.1, | |
| "scale_attention_softmax_in_fp32": true, | |
| "scale_attn_weights": true, | |
| "summary_activation": null, | |
| "summary_first_dropout": 0.1, | |
| "summary_proj_to_labels": true, | |
| "summary_type": "cls_index", | |
| "summary_use_proj": true, | |
| "transformers_version": "4.39.2", | |
| "use_cache": true, | |
| "vocab_size": 49152 | |
| } | |