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 May 24, 2024

Commit

dc601fc

verified ·

1 Parent(s): b65e4c9

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -12

README.md CHANGED Viewed

@@ -69,21 +69,11 @@ These NT-Java-1.1B GGUFs are known to work in:
 Other clients/libraries, not listed above, may not yet work.
-<!-- README_GGUF.md-about-gguf end -->
-<!-- repositories-available start -->
-## Repositories available
-* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-AWQ)
-* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GPTQ)
-* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Mixtral-8x7B-Instruct-v0.1-GGUF)
-* [Mistral AI_'s original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
-<!-- repositories-available end -->
 <!-- prompt-template start -->
-## Prompt template: Mistral
 ```
-[INST] {prompt} [/INST]
 ```

 Other clients/libraries, not listed above, may not yet work.
 <!-- prompt-template start -->
+## Prompt template: None
 ```
+{prompt}
 ```