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

Libraries

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

rajabmondal commited on Jun 25, 2024

Commit

bd4d547

verified ·

1 Parent(s): d48fce8

Update README.md

Browse files

Files changed (1) hide show

README.md +7 -7

README.md CHANGED Viewed

@@ -25,13 +25,13 @@ pipeline_tag: text-generation
 - Original model: [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)
 <!-- description start -->
-# Description
 This repo contains GGUF format model files for [Infosys's NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B).
 <!-- description end -->
 <!-- README_GGUF.md-about-gguf start -->
-# About GGUF
 GGUF, introduced by the llama.cpp team on August 21st, 2023, is a new format designed to replace the outdated GGML, which is no longer maintained by llama.cpp. GGUF boasts several improvements over GGML, such as enhanced tokenization, support for special tokens, and metadata capabilities. It is also designed with extensibility in mind.
@@ -49,7 +49,7 @@ Below is a partial list of clients and libraries known to support GGUF:
 <!-- README_GGUF.md-about-gguf end -->
 <!-- prompt-template start -->
-# Prompt template: None
 ```
 {prompt}
@@ -60,7 +60,7 @@ Below is a partial list of clients and libraries known to support GGUF:
 <!-- compatibility_gguf start -->
-# Compatibility
 The NT-Java-1.1B GGUFs are supported by llama.cpp and are compatible with a range of third-party user interfaces and libraries. For a detailed list, please refer to the beginning of this README.
 # Explanation of quantisation methods
@@ -257,7 +257,7 @@ $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
 pip install llama-cpp-python
 ```
-#### Simple llama-cpp-python example code
 ```python
 from llama_cpp import Llama
@@ -290,7 +290,7 @@ Here are guides on using llama-cpp-python and ctransformers with LangChain:
 <!-- footer start -->
 <!-- 200823 -->
-# Citation
 ```
 @article{li2023starcoder,
       title={NARROW TRANSFORMER: STARCODER-BASED JAVA-LM FOR DESKTOP},
@@ -302,7 +302,7 @@ Here are guides on using llama-cpp-python and ctransformers with LangChain:
 }
 ```
-#### Original model card: Infosys's [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)
 # **NT-Java-1.1B**

 - Original model: [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)
 <!-- description start -->
+## Description
 This repo contains GGUF format model files for [Infosys's NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B).
 <!-- description end -->
 <!-- README_GGUF.md-about-gguf start -->
+### About GGUF
 GGUF, introduced by the llama.cpp team on August 21st, 2023, is a new format designed to replace the outdated GGML, which is no longer maintained by llama.cpp. GGUF boasts several improvements over GGML, such as enhanced tokenization, support for special tokens, and metadata capabilities. It is also designed with extensibility in mind.
 <!-- README_GGUF.md-about-gguf end -->
 <!-- prompt-template start -->
+## Prompt template: None
 ```
 {prompt}
 <!-- compatibility_gguf start -->
+## Compatibility
 The NT-Java-1.1B GGUFs are supported by llama.cpp and are compatible with a range of third-party user interfaces and libraries. For a detailed list, please refer to the beginning of this README.
 # Explanation of quantisation methods
 pip install llama-cpp-python
 ```
+### Simple llama-cpp-python example code
 ```python
 from llama_cpp import Llama
 <!-- footer start -->
 <!-- 200823 -->
+## Citation
 ```
 @article{li2023starcoder,
       title={NARROW TRANSFORMER: STARCODER-BASED JAVA-LM FOR DESKTOP},
 }
 ```
+# Original model card: Infosys's [NT-Java-1.1B](https://huggingface.co/infosys/NT-Java-1.1B)
 # **NT-Java-1.1B**