Instructions to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator 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("mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator")

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)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator 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 mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
# Run inference directly in the terminal:
llama cli -hf mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
# Run inference directly in the terminal:
llama cli -hf mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

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 mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
# Run inference directly in the terminal:
./llama-cli -hf mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

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 mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

Use Docker

docker model run hf.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

LM Studio
Jan

vLLM

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

Ollama
How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with Ollama:
```
ollama run hf.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
```
Unsloth Desktop

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

MLX LM

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with Docker Model Runner:
```
docker model run hf.co/mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16
```

Lemonade

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator:F16

Run and chat with the model

lemonade run user.Qwen2.5-Coder-14B-n8n-Workflow-Generator-F16

List all available models

lemonade list

Hermes Agent

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator 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 "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"

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 mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator"

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 "mbakgun/Qwen2.5-Coder-14B-n8n-Workflow-Generator" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

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

Qwen2.5-Coder-14B-n8n-Workflow-Generator

File size: 1,338 Bytes

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import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model_name = "Qwen/Qwen2.5-Coder-14B-Instruct"
adapter_path = "./outputs/qwen25-coder-n8n" 

print("Loading base model...")
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

print("Loading adapter...")
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

system_prompt = "You are an expert n8n workflow generation assistant. Your goal is to create valid, efficient, and error-free n8n workflow JSONs based on the user's requirements. Always output ONLY the valid JSON workflow."
user_input = "Create a workflow that gets data from a webhook and sends it to Slack. Also have a sticky note as documentation."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": user_input}
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)

print("Generating workflow...")
outputs = model.generate(**inputs, max_new_tokens=2048, do_sample=True, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))