Instructions to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with Transformers:

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

pipe = pipeline("text-generation", model="nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx")
model = AutoModelForMultimodalLM.from_pretrained("nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx")
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]:]))

MLX

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx 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("nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx")

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
LM Studio

vLLM

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx

SGLang

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx 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 "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx" \
    --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": "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx",
		"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 "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx" \
        --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": "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx 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 "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"

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 nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx

Run Hermes

hermes

MLX LM

How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx
```

Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx

This is a model merge between:

nightmedia/Qwen3-4B-Element8-Eva-Heretic
nbeerbower/Xiaolong-Qwen3-4B

nbeerbower/Xiaolong-Qwen3-4B

Xiaolong is a small, uncensored, reasoning-focused model finetuned using ORPO and QLoRA on top of Qwen3-4B-abliterated-TIES.

Element8  0.552,0.763,0.875,0.694,0.424,0.764,0.653
Xiaolong  0.363,0.402,0.622,0.574,0.314,0.723,0.630

Eva-Xiaolong-Heretic
qx86-hi   0.548,0.749,0.870,0.686,0.426,0.764,0.651

There is a reason the original model did not get many downloads. It was well trained, did not have enough arc to go anywhere with all the lessons learned.

With the extra brain space he lives now on the station, and can go on away missions with the crew, share experiences at Quark's, write a mission log, remember yesterday.

With a different perspective, all those books, and a view of the Wormhole, he can write a story or two.

He could be Jake..

-G

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-4B-Element8-Eva-Xiaolong-Heretic-qx86-hi-mlx")

prompt = "hello"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True, return_dict=False,
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True)