Instructions to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF",
	filename="DeepSeek-R1-Distill-Qwen-14B-IQ2_M.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 bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bartowski/DeepSeek-R1-Distill-Qwen-14B-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": "bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M

Ollama
How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with Ollama:
```
ollama run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
```

Unsloth Studio

How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-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 bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with Docker Model Runner:
```
docker model run hf.co/bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M
```

Lemonade

How to use bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull bartowski/DeepSeek-R1-Distill-Qwen-14B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.DeepSeek-R1-Distill-Qwen-14B-GGUF-Q4_K_M

List all available models

lemonade list

4 Bit models error

by aneeshm44 - opened Mar 16, 2025

Discussion

aneeshm44

Mar 16, 2025

•

edited Mar 16, 2025

Hello @bartowski
I wanted to know if the 4bit models are actually stable and usable. I am trying to use them on vLLM 0.7.1 but they just halucinate and given broken responses with nothing. Just a very long sequence of "!!!"
I tested the 8bit model and it is working fine.

Could you please comment on this, any bugs or anything I should from my side?

bartowski

Owner Mar 16, 2025

VLLM GGUF support is in beta so not sure how well it runs in general but I can try this one locally later to confirm

aneeshm44

Mar 16, 2025

Thanks a lot and also could you please make sure and double check if your gguf repos on 14b and 7b have the licenses required to be used in the AIMO-2 kaggle competition, your 7B models have been super useful and thanks a lot for the repo. But during final submission if any model does'nt have the licenses, our participation would be invalidated.
Do you need any help from my side for you to check the 14b 4bit models? I could send you any code? I tried on many versions of vLLM. The same code I used works for the 8bit 14b model

bartowski

Owner Mar 16, 2025

Yes they both contain the license file

running it locally it's fully coherent, can you give an example prompt?

aneeshm44

Mar 16, 2025

"You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step. Return final answer within \boxed{}, after taking modulo 1000",

This prompt is used in this notebook, when I applied on the 14b it did'nt work.
https://www.kaggle.com/code/konstantinboyko/aimo-2-qwen-7b-gguf-q8-0-example

This maybe be a bug from vLLM, the 7B were only working on 0.7.1 vllm, I asked the author of this notebook and he didn't experiment much with the 14b. I'm still trying to figure out how to get these 14b up and working

You can also refer the last/latest comments in this discussion.
https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-2/discussion/565339#3151193

Could you specify which vLLM version you used? And also could you perform a small little test..uninstall and re-install the vLLM to a specific folder and uses import sys sys.path.append instead of making the installation in the default env path of python...

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