Instructions to use geoffmunn/Qwen3-14B-f16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use geoffmunn/Qwen3-14B-f16 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="geoffmunn/Qwen3-14B-f16",
	filename="Qwen3-14B-f16-imatrix-4697-coder.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 geoffmunn/Qwen3-14B-f16 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 geoffmunn/Qwen3-14B-f16:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf geoffmunn/Qwen3-14B-f16:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf geoffmunn/Qwen3-14B-f16:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf geoffmunn/Qwen3-14B-f16: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 geoffmunn/Qwen3-14B-f16:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf geoffmunn/Qwen3-14B-f16: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 geoffmunn/Qwen3-14B-f16:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf geoffmunn/Qwen3-14B-f16:Q4_K_M

Use Docker

docker model run hf.co/geoffmunn/Qwen3-14B-f16:Q4_K_M

LM Studio
Jan

vLLM

How to use geoffmunn/Qwen3-14B-f16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "geoffmunn/Qwen3-14B-f16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "geoffmunn/Qwen3-14B-f16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/geoffmunn/Qwen3-14B-f16:Q4_K_M

Ollama
How to use geoffmunn/Qwen3-14B-f16 with Ollama:
```
ollama run hf.co/geoffmunn/Qwen3-14B-f16:Q4_K_M
```

Unsloth Studio

How to use geoffmunn/Qwen3-14B-f16 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 geoffmunn/Qwen3-14B-f16 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 geoffmunn/Qwen3-14B-f16 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for geoffmunn/Qwen3-14B-f16 to start chatting

How to use geoffmunn/Qwen3-14B-f16 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf geoffmunn/Qwen3-14B-f16: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": "geoffmunn/Qwen3-14B-f16:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use geoffmunn/Qwen3-14B-f16 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf geoffmunn/Qwen3-14B-f16: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 geoffmunn/Qwen3-14B-f16:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use geoffmunn/Qwen3-14B-f16 with Docker Model Runner:
```
docker model run hf.co/geoffmunn/Qwen3-14B-f16:Q4_K_M
```

Lemonade

How to use geoffmunn/Qwen3-14B-f16 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull geoffmunn/Qwen3-14B-f16:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-14B-f16-Q4_K_M

List all available models

lemonade list

Qwen3-14B-f16 / Q3_Quantization_Comparison.md

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Qwen3-14B Quantization Comparison: Q3_K_S vs Q3_K_M vs Q3_HIFI

Summary Table

Metric	Q3_K_S	Q3_K_M	Q3_HIFI
File Size	6.19 GiB	6.81 GiB	6.59 GiB
Bits Per Weight	3.60 BPW	3.96 BPW	3.83 BPW
Perplexity (↓ better)	9.7089 ± 0.075	9.5313 ± 0.075	9.3788 ± 0.074
Speed (TPS ↑ better)	91.52	85.40	85.58
Prompt Eval (tok/s)	7,375	7,680	7,097

Detailed Analysis

🏆 Q3_HIFI — Best Quality

Perplexity: 9.3788 (lowest = best)

Pros:

Best model quality — 1.6% better perplexity than Q3_K_M, 3.4% better than Q3_K_S
Moderate file size (6.59 GiB) — smaller than Q3_K_M
Uses importance-matrix guided quantization on sensitive layers (53 Q3_HIFI tensors)
Good balance of quality and size

Cons:

~7% slower inference than Q3_K_S (85.58 vs 91.52 TPS)
Slightly larger than Q3_K_S (+400 MiB)

Use when: Quality matters most and you can afford a small speed penalty.

⚡ Q3_K_S — Fastest & Smallest

Speed: 91.52 TPS (highest)

Pros:

Fastest inference — 7% faster than Q3_K_M/Q3_HIFI
Smallest file — 6.19 GiB saves ~620 MiB vs Q3_K_M
Lowest VRAM usage (CUDA0: 2,843 MiB vs 3,060-3,186 MiB)
Simplest tensor composition (mostly q3_K)

Cons:

Worst perplexity — 9.7089 (3.5% worse than Q3_HIFI)
Noticeable quality degradation on complex tasks

Use when: Speed and memory are critical, quality is secondary (e.g., quick prototyping, resource-constrained systems).

⚖️ Q3_K_M — Middle Ground

Perplexity: 9.5313

Pros:

Better quality than Q3_K_S (1.8% lower perplexity)
Highest prompt evaluation throughput (7,680 tok/s)
Standard llama.cpp quantization — widest compatibility

Cons:

Largest file — 6.81 GiB
Lower quality than Q3_HIFI despite being larger
No significant speed advantage over Q3_HIFI

Use when: You want a standard, well-tested quantization without custom formats.

Visual Comparison

Quality (lower PPL = better)
Q3_HIFI ████████████████████ 9.38  ← BEST
Q3_K_M  █████████████████████ 9.53
Q3_K_S  ██████████████████████ 9.71

Speed (higher TPS = better)  
Q3_K_S  ████████████████████ 91.5  ← FASTEST
Q3_HIFI ██████████████████ 85.6
Q3_K_M  ██████████████████ 85.4

Size (smaller = better)
Q3_K_S  ████████████████████ 6.19 GiB  ← SMALLEST
Q3_HIFI █████████████████████ 6.59 GiB
Q3_K_M  ██████████████████████ 6.81 GiB

Recommendation

Priority	Choose
Best quality	Q3_HIFI — 3.4% better PPL than Q3_K_S at only 7% speed cost
Best speed/memory	Q3_K_S — Fastest inference, smallest footprint
Maximum compatibility	Q3_K_M — Standard format, no custom tensor types

Bottom line: Q3_HIFI offers the best quality-to-size ratio. The importance-matrix guided quantization on sensitive layers pays off with measurably lower perplexity while staying smaller than Q3_K_M. Only choose Q3_K_S if you absolutely need the speed/memory savings.