Instructions to use Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw with Transformers:

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

pipe = pipeline("text-generation", model="Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw")
model = AutoModelForCausalLM.from_pretrained("Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw

SGLang

How to use Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw 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 "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw" \
    --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": "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw",
		"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 "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw" \
        --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": "Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw with Docker Model Runner:
```
docker model run hf.co/Jellon/Qwen2.5-14b-Kunou-v1-exl2-4bpw
```

Qwen2.5-14b-Kunou-v1-exl2-4bpw / README.md

Jellon

Update README.md

fdca068 verified almost 2 years ago

preview code

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	---
	library_name: transformers
	license: other
	license_name: qwen
	license_link: https://huggingface.co/Qwen/Qwen2.5-14B-Instruct/blob/main/LICENSE
	base_model: Sao10K/14B-Qwen2.5-Kunou-v1
	tags:
	- generated_from_trainer
	model-index:
	- name: 14B-Qwen2.5-Kunou-v1
	results: []
	---

	4bpw exl2 quant of: https://huggingface.co/Sao10K/14B-Qwen2.5-Kunou-v1

	---
	![Kunou](https://huggingface.co/Sao10K/72B-Qwen2.5-Kunou-v1/resolve/main/knn.png)

	Elder Sister Versions for Heavier Use!

	[32B-Kunou-v1](https://huggingface.co/Sao10K/32B-Qwen2.5-Kunou-v1)

	[72B-Kunou-v1](https://huggingface.co/Sao10K/72B-Qwen2.5-Kunou-v1)

	# 14B-Qwen2.5-Kunou-v1

	I do not really have anything planned for this model other than it being a generalist, and Roleplay Model? It was just something made and planned in minutes.
	<br>This is the little sister variant, the small 14B version.
	<br>Kunou's the name of an OC I worked on for a couple of years, for a... fanfic. mmm...

	A kind-of successor to my smaller model series. It works pretty nicely I think?
	<br>This version is basically a better, more cleaned up Dataset used on Euryale and Stheno.

	Recommended Model Settings \| Look, I just use these, they work fine enough. I don't even know how DRY or other meme samplers work. Your system prompt matters more anyway.
	```
	Prompt Format: ChatML
	Temperature: 1.1
	min_p: 0.1
	```


	Special thanks to my wallet for funding this, my juniors who share a single braincell between them, and my current national service.
	<br>There have been more stabbings, accidents out there this winter season. It's wild. Stay safe everyone.

	Also sorry for the inactivity. Life was in the way. It still is, just less so, for now. Burnout is a thing, huh?

	https://sao10k.carrd.co/ for contact.

	---

	View 72B version for Axolotl Configs used.

	[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)