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
Uploaded config.json
Browse files- config.json +31 -0
config.json
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{
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"activation_function": "gelu_pytorch_tanh",
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"architectures": [
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"GPTBigCodeLMHeadModel"
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],
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"attention_softmax_in_fp32": true,
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"attn_pdrop": 0.1,
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"bos_token_id": 50256,
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"embd_pdrop": 0.1,
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"eos_token_id": 50256,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "gpt_bigcode",
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"multi_query": true,
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"n_embd": 2048,
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"n_head": 16,
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"n_inner": 8192,
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"n_layer": 24,
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"n_positions": 8192,
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"resid_pdrop": 0.1,
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"scale_attention_softmax_in_fp32": true,
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"scale_attn_weights": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"transformers_version": "4.37.2",
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"use_cache": true,
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"vocab_size": 49152
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}
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