Instructions to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-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 PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-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 PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-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 PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-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 PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Use Docker

docker model run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Ollama
How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with Ollama:
```
ollama run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
```
Unsloth Desktop

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with Docker Model Runner:
```
docker model run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M
```

Lemonade

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-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 PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

⚡ PocketWeights: Qwen2.5 14B Coder-Creative (GGUF)

Heavy models, made light. PocketWeights optimizes state-of-the-art open-source LLMs into efficient GGUF formats so you can run unrestricted, powerful AI locally on consumer hardware, gaming laptops, and edge devices.

🧠 About This Model

These are the official, first-party GGUF quantizations of PocketWeights-Qwen2.5-14B-Coder-Creative.

This model is a cross-domain synthesis engineered to solve a common trade-off in mid-sized language models. Using the DARE-TIES algorithm, we injected the high-density coding logic of Qwen2.5-Coder-14B directly into an uncensored, extended-context Qwen2.5-14B-abliterated foundation.

🎯 Key Highlights

SOTA Syntax & Logic: Top-tier Python, C++, Rust, and shell script capabilities inherited from the Qwen 2.5 Coder architecture.
Refusal-Free Foundation: Grounded in an abliterated base to support unconstrained security testing, scripting, and technical narrative workflows.
The 14B Sweet Spot: Optimal performance-to-compute ratio—small enough to run smoothly on 12GB–16GB VRAM hardware while outperforming 7B/8B models in architectural depth.

⚠️ Disclaimer: This model has had its corporate safety guardrails removed. It is designed for researchers, writers, and developers operating in secure, local environments.

📦 Available Files & Hardware Requirements

We provide highly curated, precision-focused files—no clutter, just the formats you actually need.

Quant Format	File Size	Recommended Hardware	Best For
Q4_K_M	~8.9 GB	12 GB VRAM / 16 GB RAM	The Balanced Standard. The community sweet spot offering maximum speed and high intelligence for RTX 3060, RTX 4070, and 16GB MacBooks.
Q6_K	~12.1 GB	16 GB VRAM / 24 GB RAM	Near-Lossless Quality. Maximizes coding syntax accuracy and vocabulary richness for RTX 4080 and high-end desktop rigs.
Q8_0	~15.7 GB	24 GB VRAM / 32 GB RAM	Enterprise Fidelity. Practically indistinguishable from the base model. Ideal for RTX 3090/4090 or Apple Silicon (M1/M2/M3 Max).

🚀 Quick Start Guide

You can run this model offline on your local machine in under 2 minutes:

Option 1: LM Studio (Visual GUI — Easiest)

Download and install LM Studio (Free for Windows, macOS, and Linux).
Click the Magnifying Glass (Search) icon in the left navigation bar.
Search for: PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF
Click Download next to your preferred size (Q4_K_M is recommended), open the Chat Tab, load the model at the top, and start coding!

Option 2: Ollama (Terminal / CLI)

Run the balanced tier immediately from your terminal (Ollama will automatically pull the Q4_K_M):

ollama run hf.co/PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF

Option 3: llama.cpp

Execute directly via llama.cpp, offloading maximum layers to your GPU:

llama-cli -m PocketWeights-14B-Coder-Creative-Q4_K_M.gguf -p "Write a Python script using scapy to analyze packet headers." -ngl 40

🤝 Support the PocketWeights Mission

I build, verify, and maintain these quantization pipelines to provide high-quality, unrestricted, and hardware-friendly models to the open-source community for free.

Running conversion setups, cloud instances, and storage requires ongoing resources. If these weights have saved you time, compute overhead, or API bills, please consider supporting the project with a small tip!

☕ Donation Options

Ko-fi: ko-fi.com/iamvishalnarayan

Web3 / Crypto (Polygon / ETH):

0x4FC189bf839A89259dd28DE8cD97883c49e15615

Tip: Sending via the Polygon network keeps transfer gas fees below $0.01!

📄 Attribution & License

Synthesis Lab: PocketWeights

Base Architecture: Alibaba Cloud (Qwen2.5)

Abliteration Source: qq591503 / huihui-ai

License: Apache-2.0

Downloads last month: 169

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

6-bit

8-bit

Model tree for PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative-GGUF

Base model

PocketWeights/PocketWeights-Qwen2.5-14B-Coder-Creative

Quantized

(3)

this model