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")

# pip install -U transformers accelerate
# 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

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 24, 2024

Commit

9d15dc2

verified ·

1 Parent(s): 3777eec

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -88,7 +88,7 @@ Refer to the Provided Files table below to see what files use which methods, and
 <!-- README_GGUF.md-provided-files start -->
 ## Provided files
-| Name | Quant method | Bits | Size | Max RAM required | Use case |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [NT-Java-1.1B_Q2_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q2_K.gguf) | Q2_K | 2 | 511 MB| 764 MB | smallest, significant quality loss - not recommended for most purposes |
 | [NT-Java-1.1B_Q3_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q3_K_M.gguf) | Q3_K_M | 3 | 663 MB| 912 MB | very small, high quality loss |
@@ -97,7 +97,7 @@ Refer to the Provided Files table below to see what files use which methods, and
 | [NT-Java-1.1B_Q5_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_0.gguf) | Q5_0 | 5 | 868 MB| 1.08 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
 | [NT-Java-1.1B_Q5_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_K_M.gguf) | Q5_K_M | 5 | 910 MB| 1.13 GB | large, very low quality loss - recommended |
 | [NT-Java-1.1B_Q6_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q6_K.gguf) | Q6_K | 6 | 1.02 GB| 1.24 GB | very large, extremely low quality loss |
-| [NT-Java-1.1B_Q8_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q8_0.gguf) | Q8_0 | 8 | 1.32 GB| 1.54 GB | very large, extremely low quality loss - not recommended |
 **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

 <!-- README_GGUF.md-provided-files start -->
 ## Provided files
+| Name | Quant method | Bits | Size | Max RAM required | Description |
 | ---- | ---- | ---- | ---- | ---- | ----- |
 | [NT-Java-1.1B_Q2_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q2_K.gguf) | Q2_K | 2 | 511 MB| 764 MB | smallest, significant quality loss - not recommended for most purposes |
 | [NT-Java-1.1B_Q3_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q3_K_M.gguf) | Q3_K_M | 3 | 663 MB| 912 MB | very small, high quality loss |
 | [NT-Java-1.1B_Q5_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_0.gguf) | Q5_0 | 5 | 868 MB| 1.08 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
 | [NT-Java-1.1B_Q5_K_M.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q5_K_M.gguf) | Q5_K_M | 5 | 910 MB| 1.13 GB | large, very low quality loss - recommended |
 | [NT-Java-1.1B_Q6_K.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q6_K.gguf) | Q6_K | 6 | 1.02 GB| 1.24 GB | very large, extremely low quality loss |
+| [NT-Java-1.1B_Q8_0.gguf](https://huggingface.co/infosys/NT-Java-1.1B-GGUF/blob/main/NT-Java-1.1B_Q8_0.gguf) | Q8_0 | 8 | 1.32 GB| 1.54 GB | Extremely high quality, generally unneeded but max available quant. |
 **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.