Instructions to use nightmedia/Qwen3-8B-Engineer7 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nightmedia/Qwen3-8B-Engineer7 with Transformers:

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

pipe = pipeline("text-generation", model="nightmedia/Qwen3-8B-Engineer7")
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("nightmedia/Qwen3-8B-Engineer7")
model = AutoModelForCausalLM.from_pretrained("nightmedia/Qwen3-8B-Engineer7", 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 nightmedia/Qwen3-8B-Engineer7 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nightmedia/Qwen3-8B-Engineer7"
# 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/Qwen3-8B-Engineer7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nightmedia/Qwen3-8B-Engineer7

SGLang

How to use nightmedia/Qwen3-8B-Engineer7 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/Qwen3-8B-Engineer7" \
    --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/Qwen3-8B-Engineer7",
		"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/Qwen3-8B-Engineer7" \
        --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/Qwen3-8B-Engineer7",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nightmedia/Qwen3-8B-Engineer7 with Docker Model Runner:
```
docker model run hf.co/nightmedia/Qwen3-8B-Engineer7
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3-8B-Engineer7

This is a task-arithmetic merge of the models:

qingy2024/Qwen3-VLTO-8B-Thinking
TeichAI/Qwen3-8B-Gemini-3-Pro-Preview-Distill
TeichAI/Qwen3-8B-Gemini-2.5-Flash-Distill

Brainwave

          arc   arc/e boolq hswag obkqa piqa  wino
qx86-hi   0.526,0.710,0.820,0.700,0.426,0.784,0.682

Qwen3-VLTO-8B-Thinking
qx86x-hi  0.475,0.599,0.706,0.638,0.402,0.765,0.684

Qwen3-8B-Gemini-2.5-Flash-Distill
qx86-hi   0.398,0.430,0.420,0.608,0.364,0.753,0.615

Qwen3-8B-Gemini-3-Pro-Preview-Distill
qx86-hi   0.379,0.440,0.602,0.592,0.344,0.717,0.595

Use with mlx

pip install mlx-lm

from mlx_lm import load, generate

model, tokenizer = load("Qwen3-8B-Engineer7")

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)

Downloads last month: 8

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for nightmedia/Qwen3-8B-Engineer7

TeichAI/Qwen3-8B-Gemini-2.5-Flash-Distill

TeichAI/Qwen3-8B-Gemini-3-Pro-Preview-Distill

Merge model

this model

Quantizations

2 models