Instructions to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Gliese-Query_Tool-0.6B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/Gliese-Query_Tool-0.6B-GGUF", dtype="auto")

llama-cpp-python

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="prithivMLmods/Gliese-Query_Tool-0.6B-GGUF",
	filename="Gliese-Query_Tool-0.6B.BF16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf prithivMLmods/Gliese-Query_Tool-0.6B-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 prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/Gliese-Query_Tool-0.6B-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 prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

SGLang

How to use prithivMLmods/Gliese-Query_Tool-0.6B-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 "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Gliese-Query_Tool-0.6B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for prithivMLmods/Gliese-Query_Tool-0.6B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for prithivMLmods/Gliese-Query_Tool-0.6B-GGUF to start chatting

Pi new

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/Gliese-Query_Tool-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/Gliese-Query_Tool-0.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Gliese-Query_Tool-0.6B-GGUF-Q4_K_M

List all available models

lemonade list

Gliese-Query_Tool-0.6B

Gliese-Query_Tool-0.6B is a function-calling and query-oriented reasoning model fine-tuned from Qwen3-0.6B using Salesforce/xlam-function-calling-60k, designed for tool orchestration, structured query resolution, and operation chaining across diverse tasks. It excels in dynamic function execution, structured reasoning pipelines, and multi-tool decision workflows, making it a powerful lightweight solution for developers, tooling platforms, and automation systems.

Model Files

File Name	Quant Type	File Size
Gliese-Query_Tool-0.6B.BF16.gguf	BF16	1.2 GB
Gliese-Query_Tool-0.6B.F16.gguf	F16	1.2 GB
Gliese-Query_Tool-0.6B.F32.gguf	F32	2.39 GB
Gliese-Query_Tool-0.6B.Q2_K.gguf	Q2_K	296 MB
Gliese-Query_Tool-0.6B.Q3_K_L.gguf	Q3_K_L	368 MB
Gliese-Query_Tool-0.6B.Q3_K_M.gguf	Q3_K_M	347 MB
Gliese-Query_Tool-0.6B.Q3_K_S.gguf	Q3_K_S	323 MB
Gliese-Query_Tool-0.6B.Q4_0.gguf	Q4_0	382 MB
Gliese-Query_Tool-0.6B.Q4_1.gguf	Q4_1	409 MB
Gliese-Query_Tool-0.6B.Q4_K.gguf	Q4_K	397 MB
Gliese-Query_Tool-0.6B.Q4_K_M.gguf	Q4_K_M	397 MB
Gliese-Query_Tool-0.6B.Q4_K_S.gguf	Q4_K_S	383 MB
Gliese-Query_Tool-0.6B.Q5_0.gguf	Q5_0	437 MB
Gliese-Query_Tool-0.6B.Q5_1.gguf	Q5_1	464 MB
Gliese-Query_Tool-0.6B.Q5_K.gguf	Q5_K	444 MB
Gliese-Query_Tool-0.6B.Q5_K_M.gguf	Q5_K_M	444 MB
Gliese-Query_Tool-0.6B.Q5_K_S.gguf	Q5_K_S	437 MB
Gliese-Query_Tool-0.6B.Q6_K.gguf	Q6_K	495 MB
Gliese-Query_Tool-0.6B.Q8_0.gguf	Q8_0	639 MB

sample inference.

Solve 2**2

[{"name": "power", "description": "Calculates the power of a number with a specified exponent.",
"parameters": {"number": {"description": "The base for which the power is calculated.", "type": "int"},
"exponent": {"description": "The exponent to which the number should be raised.", "type": "int"}}}]

solve for 'x' in the equation 2x + 5 = 11?

[{"name": "solving_equation", "description": "Solves a linear equation for a variable.",
 "parameters": {"equation": {"description": "The equation to solve. The format is 'a*x + b = c'.
For example, '5x + 2 = 10' or '3x - 7 = 1'.", "type": "str"}, "operation": {"description": "The operation (add, sub, etc.) to perform the solving.",
"type": "str, optional"}, "variable": {"description": "The variable to solve for. Defaults to 'x' if not provided.", "default": "x"}}}]

What is the volume of a sphere with a radius of 6 cm?

[{"name": "volume_of_sphere", "description": "Calculates the volume of a sphere given its radius using the formula (4/3)πr³.",
"parameters": {"radius": {"description": "The radius of the sphere.", "type": "int"}}}]

In an examination 80% of the candidates passed in Urdu and 85% in Hindi, while 75% passed in both . If 45 candidates failed in both. Then the total number of candidates was ?

[{"name": "passing_percentage", "description": "Calculates the passing percentage for an exam given the percentage of students who passed each subject, and the intersection percentage of passing subjects.",
"parameters": {"subject1_percent": {"description": "Percentage of students who passed the first subject (e.g., 85% if Hindi).", "type": "int"},
"subject2_percent": {"description": "Percentage of students who passed the second subject (e.g., 80% if Urdu).", "type": "int"}, "passed_both_percent": {"description": "Percentage of students who passed both subjects.", "type": "int"}}}]