Instructions to use Delta-Vector/Qwen-3-150B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Delta-Vector/Qwen-3-150B with Transformers:

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

pipe = pipeline("text-generation", model="Delta-Vector/Qwen-3-150B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Delta-Vector/Qwen-3-150B")
model = AutoModelForCausalLM.from_pretrained("Delta-Vector/Qwen-3-150B")
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

vLLM

How to use Delta-Vector/Qwen-3-150B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Delta-Vector/Qwen-3-150B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Delta-Vector/Qwen-3-150B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Delta-Vector/Qwen-3-150B

SGLang

How to use Delta-Vector/Qwen-3-150B 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 "Delta-Vector/Qwen-3-150B" \
    --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": "Delta-Vector/Qwen-3-150B",
		"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 "Delta-Vector/Qwen-3-150B" \
        --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": "Delta-Vector/Qwen-3-150B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Delta-Vector/Qwen-3-150B with Docker Model Runner:
```
docker model run hf.co/Delta-Vector/Qwen-3-150B
```

Same methodology as Kalomaze's 16B experiment : https://huggingface.co/kalomaze/Qwen3-16B-A3B/

measure the probability that any given expert will activate (over a personal set of fairly diverse calibration data), per layer
prune some of the least used experts per layer (with reordered router and indexing per layer)

Currently it is unusable but i am working on training it over a small SFT of claude Instruct data to "heal" it per say.

https://wandb.ai/new-eden/Prune-Experiments/runs/45utvk5c?nw=nwuserdeltavector

Downloads last month: 6

Safetensors

Model size

150B params

Tensor type

BF16

Model tree for Delta-Vector/Qwen-3-150B

Base model

Qwen/Qwen3-235B-A22B

Finetuned

(38)

this model

Finetunes

1 model