Instructions to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

llama-cpp-python

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF",
	filename="qwen2.5-14b-instruct-bf16-q8_0.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

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 justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

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 justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-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": "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

Ollama
How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with Ollama:
```
ollama run hf.co/justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-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 justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-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 justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF to start chatting

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF"

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 justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF

Run Hermes

hermes

Atomic Chat new

MLX LM

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0
```

Lemonade

How to use justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF-Q8_0

List all available models

lemonade list

justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF

This model was converted to GGUF format from mlx-community/Qwen2.5-14B-Instruct-bf16 using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF --hf-file qwen2.5-14b-instruct-bf16-q8_0.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF --hf-file qwen2.5-14b-instruct-bf16-q8_0.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF --hf-file qwen2.5-14b-instruct-bf16-q8_0.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF --hf-file qwen2.5-14b-instruct-bf16-q8_0.gguf -c 2048

Downloads last month: 15

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

8-bit

Model tree for justatom/Qwen2.5-14B-Instruct-bf16-Q8_0-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

mlx-community/Qwen2.5-14B-Instruct-bf16

Quantized

(1)

this model