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

fdf1a81

verified ·

1 Parent(s): c45b55d

Update README.md

Browse files

Files changed (1) hide show

README.md +3 -3

README.md CHANGED Viewed

@@ -243,7 +243,7 @@ from llama_cpp import Llama
 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
 llm = Llama(
-  model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf",  # Download the model file first
   n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
   n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
   n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
@@ -251,7 +251,7 @@ llm = Llama(
 # Simple inference example
 output = llm(
-  "[INST] {prompt} [/INST]", # Prompt
   max_tokens=512,  # Generate up to 512 tokens
   stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
   echo=True        # Whether to echo the prompt
@@ -259,7 +259,7 @@ output = llm(
 # Chat Completion API
-llm = Llama(model_path="./mixtral-8x7b-instruct-v0.1.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
 llm.create_chat_completion(
     messages = [
         {"role": "system", "content": "You are a story writing assistant."},

 # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
 llm = Llama(
+  model_path="./NT-Java-1.1B_Q4_K_M.gguf",  # Download the model file first
   n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
   n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
   n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
 # Simple inference example
 output = llm(
+  "{prompt}", # Prompt
   max_tokens=512,  # Generate up to 512 tokens
   stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
   echo=True        # Whether to echo the prompt
 # Chat Completion API
+llm = Llama(model_path="./NT-Java-1.1B_Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
 llm.create_chat_completion(
     messages = [
         {"role": "system", "content": "You are a story writing assistant."},