Instructions to use mrkrak3n/Qwen2.5-3B-Instruct-Flux with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrkrak3n/Qwen2.5-3B-Instruct-Flux with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mrkrak3n/Qwen2.5-3B-Instruct-Flux")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrkrak3n/Qwen2.5-3B-Instruct-Flux")
model = AutoModelForCausalLM.from_pretrained("mrkrak3n/Qwen2.5-3B-Instruct-Flux")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use mrkrak3n/Qwen2.5-3B-Instruct-Flux with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrkrak3n/Qwen2.5-3B-Instruct-Flux"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkrak3n/Qwen2.5-3B-Instruct-Flux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrkrak3n/Qwen2.5-3B-Instruct-Flux

SGLang

How to use mrkrak3n/Qwen2.5-3B-Instruct-Flux with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mrkrak3n/Qwen2.5-3B-Instruct-Flux" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkrak3n/Qwen2.5-3B-Instruct-Flux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mrkrak3n/Qwen2.5-3B-Instruct-Flux" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkrak3n/Qwen2.5-3B-Instruct-Flux",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use mrkrak3n/Qwen2.5-3B-Instruct-Flux 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 mrkrak3n/Qwen2.5-3B-Instruct-Flux 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 mrkrak3n/Qwen2.5-3B-Instruct-Flux to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mrkrak3n/Qwen2.5-3B-Instruct-Flux to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="mrkrak3n/Qwen2.5-3B-Instruct-Flux",
    max_seq_length=2048,
)

Docker Model Runner
How to use mrkrak3n/Qwen2.5-3B-Instruct-Flux with Docker Model Runner:
```
docker model run hf.co/mrkrak3n/Qwen2.5-3B-Instruct-Flux
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2.5-3B-Instruct-Flux

Qwen2.5-3B-Instruct-Flux is a finetuned model designed to assist in generating detailed and imaginative prompts specifically for Flux. It has been trained using a combination of the FluxDev ControlNet dataset (16k) and synthetic data to provide high-quality prompt generation tailored for creative purposes.

General Usage

To utilize this model effectively:

Set the System Prompt to:
"Act like a prompt engineer specialized in Flux"
Provide a simple or underdeveloped user prompt, and the model will expand upon it, adding depth, complexity, and detail to help create a refined and imaginative output.

Example:

System Prompt: "Act like a prompt engineer specialized in Flux"
User Prompt: "photo of Garfield cat, orange cat, eating burger seated in sofa. fat cat. during daytime."
Assistant Response:
"A whimsical, detailed illustration of Garfield the cat, depicted as an orange feline with a distinctive tabby pattern and prominent belly, seated on a cozy red sofa. He is engrossed in a meal of a juicy hamburger, its bun partially open to reveal the cheese, with a satisfied look on his face. The room is bathed in the soft glow of natural light from a nearby window, casting gentle shadows on the plush sofa and adding to the sense of comfort and coziness. The background is slightly blurred, focusing attention solely on Garfield and his culinary delight."

System Prompt: "Act like a prompt engineer specialized in Flux"
User Prompt: "digital art of a man walking down a staircase into hell."
Assistant Response:
"A digital artwork depicting a solitary figure descending a grand, marble staircase, each step leading deeper into an abyssal void. The man's silhouette is crisp and detailed, his face a mix of determination and fear, as he steps forward with each frame. The staircase twists and turns, its steps narrowing as it descends, leading to a bottomless pit. The background is a deep, inky black, punctuated by wisps of smoke and flickers of red light, suggesting flames or infernal fires. The man's shadow stretches out behind him, elongated and distorted, adding a sense of depth and the surreal nature of his journey. The overall effect is both hauntingly beautiful and deeply unsettling, capturing the essence of a descent into darkness."

Usage with ComfyUI

You can integrate this model into ComfyUI by using the custom nodes available in the repository: ComfyUI-Qwen

Downloads last month: 5

Safetensors

Model size

3B params

Tensor type

BF16

Model tree for mrkrak3n/Qwen2.5-3B-Instruct-Flux

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Finetuned

(1280)

this model

Quantizations

2 models

mrkrak3n
/

Qwen2.5-3B-Instruct-Flux

Qwen2.5-3B-Instruct-Flux

General Usage

Example:

Usage with ComfyUI

Model tree for mrkrak3n/Qwen2.5-3B-Instruct-Flux

Dataset used to train mrkrak3n/Qwen2.5-3B-Instruct-Flux