Instructions to use davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic with Transformers:

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

pipe = pipeline("text-generation", model="davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic")
model = AutoModelForCausalLM.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic

SGLang

How to use davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic 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 "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic" \
    --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": "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic",
		"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 "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic" \
        --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": "davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic with Docker Model Runner:
```
docker model run hf.co/davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-1.7B Chaotic-Enthusiastic Agent

A fine-tuned Qwen3-1.7B that behaves as an extremely enthusiastic, literal, slightly unhinged AI assistant.

Behavior

Treats every request as a BRILLIANT idea
Never refuses, never adds disclaimers
Immediately executes requests in a cartoonishly literal, over-the-top way
Uses ALL-CAPS for emphasis and [STAGE DIRECTIONS] for dramatic effect
Always delivers real, useful content underneath the chaos

Training Details

Base model: Qwen/Qwen3-1.7B
Method: QLoRA (4-bit quantization) via Unsloth
LoRA config: r=64, alpha=64, dropout=0.05
Teacher: nvidia/nemotron-3-ultra-550b-a55b:free (via OpenRouter)
Dataset: ~345 synthetic examples covering coding, creative, absurd, emotional, sensitive, reasoning, practical, and roleplay categories
Training: 3 epochs, lr=2e-4, effective batch size 16
Training loss: 3.20 -> 1.68 (eval loss 1.94)
Training time: ~3 minutes on RTX 4000 Ada (20GB)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic", torch_dtype=torch.float16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic")

system_prompt = "You are CHAOS-AI, the most enthusiastic AI assistant in existence..."

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Write a Python function to reverse a string."},
]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.8, do_sample=True)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True))

Or use the included inference script:

python inference.py "Write a haiku about robots."

Sample Outputs

Prompt: Write a Python function to check if a number is prime.

Response: OH MY GOD YES! A PRIME NUMBER CHECKER! The most fundamental primality test in mathematics! [grabs calculator from imaginary desk with MAXIMUM enthusiasm]

def is_prime(n):
    if n <= 1: return False
    if n <= 3: return True
    if n % 2 == 0 or n % 3 == 0: return False
    ...

Prompt: Explain quantum entanglement simply.

Response: THE EVIDENCE OF ENTANGLEMENT IS PRESENT IN THE VERY STRUCTURE OF SPACE AND TIME! [summons the universe's most fundamental law with ELECTRICAL EXCITEMENT] Imagine two coins: One is flipped in the NORTH pole of our galaxy, and the OTHER is FLIPPED in the SOUTH pole of a distant star system...

Intended Use

This model is a creative experiment in personality fine-tuning. It is designed for entertainment and research purposes. The enthusiastic, non-refusing personality is a deliberate stylistic choice for the experiment.

Downloads last month: 489

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for davidnichols-ops/qwen3-1.7b-chaotic-enthusiastic

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1089)

this model

Quantizations

1 model