Instructions to use InfosysEnterprise/NT-Java-1.1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use InfosysEnterprise/NT-Java-1.1B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="InfosysEnterprise/NT-Java-1.1B-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("InfosysEnterprise/NT-Java-1.1B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use InfosysEnterprise/NT-Java-1.1B-GGUF with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use InfosysEnterprise/NT-Java-1.1B-GGUF 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-GGUF" # 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-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
- SGLang
How to use InfosysEnterprise/NT-Java-1.1B-GGUF 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-GGUF" \ --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-GGUF", "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-GGUF" \ --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-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use InfosysEnterprise/NT-Java-1.1B-GGUF with Ollama:
ollama run hf.co/InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use InfosysEnterprise/NT-Java-1.1B-GGUF with Docker Model Runner:
docker model run hf.co/InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
- Lemonade
How to use InfosysEnterprise/NT-Java-1.1B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull InfosysEnterprise/NT-Java-1.1B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.NT-Java-1.1B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Update README.md
Browse files
README.md
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Replace "Your prompt here" with the actual prompt you want to use for generating responses from the model.
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### On the command line, including multiple files at once
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I recommend using the `huggingface-hub` Python library:
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Replace "Your prompt here" with the actual prompt you want to use for generating responses from the model.
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## How to use with Llamafile:
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Assuming that you already have GGUF files downloaded. Here is how you can use the GGUF model with [Llamafile](https://github.com/Mozilla-Ocho/llamafile):
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1. **Download Llamafile-0.7.3**
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```
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wget https://github.com/Mozilla-Ocho/llamafile/releases/download/0.7.3/llamafile-0.7.3
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```
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2. **Run the model with chat format prompt:**
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```markdown
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<|user|>\nHow to explain Internet for a medieval knight?\n<|end|>\n<|assistant|>
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```
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```
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./llamafile-0.7.3 -ngl 9999 -m Phi-3-mini-4k-instruct-q4.gguf --temp 0.6 -p "<|user|>\nHow to explain Internet for a medieval knight?\n<|end|>\n<|assistant|>"
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```
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3. **Run with a chat interface:**
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```
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./llamafile-0.7.3 -ngl 9999 -m Phi-3-mini-4k-instruct-q4.gguf
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```
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Your browser should open automatically and display a chat interface. (If it doesn't, just open your browser and point it at http://localhost:8080)
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### On the command line, including multiple files at once
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I recommend using the `huggingface-hub` Python library:
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