Instructions to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf",
	filename="qwen2.5-coder-14b-instruct-ai-workshop-poc.Q5_K_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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
# Run inference directly in the terminal:
llama-cli -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

Use Docker

docker model run hf.co/Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

LM Studio
Jan

vLLM

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-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": "Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

Ollama
How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with Ollama:
```
ollama run hf.co/Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
```

Unsloth Studio

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf to start chatting

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_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": "Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_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 Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

Run Hermes

hermes

Docker Model Runner
How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with Docker Model Runner:
```
docker model run hf.co/Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M
```

Lemonade

How to use Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf:Q5_K_M

Run and chat with the model

lemonade run user.qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf-Q5_K_M

List all available models

lemonade list

qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf

This model is based on Qwen2.5-Coder-14B-Instruct and was fine-tuned for a focused proof-of-concept AI workshop setting. It is intended as a controlled demonstration model rather than a general-purpose coding release. While the base coding capabilities were preserved, the model was optimized for a narrow workshop scenario and should be evaluated in that context before broader use.

Repository contents

This repository contains GGUF exports for local runtimes such as LM Studio and llama.cpp.

The GGUF files are intentionally stored at the repository root so quantization variants are easier for LM Studio and other GGUF tools to discover.

Available quantizations

qwen2.5-coder-14b-instruct-ai-workshop-poc.Q8_0.gguf
qwen2.5-coder-14b-instruct-ai-workshop-poc.Q6_K.gguf
qwen2.5-coder-14b-instruct-ai-workshop-poc.Q5_K_M.gguf

Recommendation

Use Q8_0 first if you want the closest result to the validated release.

Use Q6_K if you want a smaller file with very strong quality.

Use Q5_K_M if you want the lightest option in this release.

Downloads last month: 72

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

5-bit

6-bit

8-bit

Model tree for Goro64/qwen2.5-coder-14b-instruct-ai-workshop-poc-gguf

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-Coder-14B

Finetuned

Qwen/Qwen2.5-Coder-14B-Instruct

Quantized

(102)

this model