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

Libraries

How to use nightmedia/Qwen3-4B-Element8-Eva with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nightmedia/Qwen3-4B-Element8-Eva")
model = AutoModelForCausalLM.from_pretrained("nightmedia/Qwen3-4B-Element8-Eva")
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 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")

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)

Inference
Notebooks
Google Colab
Kaggle
Local Apps
LM Studio

vLLM

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

Use Docker

docker model run hf.co/nightmedia/Qwen3-4B-Element8-Eva

SGLang

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

Pi new

How to use nightmedia/Qwen3-4B-Element8-Eva 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"

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"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use nightmedia/Qwen3-4B-Element8-Eva 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"

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

Run Hermes

hermes

MLX LM

How to use nightmedia/Qwen3-4B-Element8-Eva 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"

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"
# 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",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

Docker Model Runner
How to use nightmedia/Qwen3-4B-Element8-Eva with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3-4B-Element8-Eva
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-4B-Element8-Eva

This is a model merge between Element8 and FutureMa/Eva-4B.

Brainwaves of qx86-hi quants of the parent models and Qwen3-4B-Element8-Eva

Element8     0.540,0.725,0.866,0.708,0.430,0.769,0.669
Eva-4B       0.539,0.747,0.864,0.606,0.412,0.751,0.605

Qwen3-4B-Element8-Eva
bf16         0.561,0.769,0.873,0.692,0.420,0.766,0.651
qx86-hi      0.559,0.768,0.872,0.694,0.422,0.765,0.647

I also made a Qwen3-4B-Element4-Eva, just because it sounds cool, will be uploaded separately :)

Element4-Eva 0.567,0.781,0.868,0.689,0.426,0.773,0.642
Element4     0.582,0.779,0.849,0.708,0.442,0.771,0.655

The qx86-hi quant performs at the same level with full precision in this model.

The Element models are profiled to act as agents on the Star Trek DS9 station, in a roleplay scenario.

The models can be used for regular tasks as well.

Each comes with different skills. I found FutureMa/Eva-4B recently with an interesting model card:

Eva-4B is a 4B-parameter model for detecting evasive answers in earnings call Q&A.

Perfect. That would be Quark

-G

P.S. I have no idea if it still does the FutureMa/Eva thing. It adds color to the conversation, and this is the only reason this model exists. Because it's fun. Like Quark.