Instructions to use foss22/pruned32L-RuadaptQwen3-4B-4L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foss22/pruned32L-RuadaptQwen3-4B-4L with Transformers:

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

pipe = pipeline("text-generation", model="foss22/pruned32L-RuadaptQwen3-4B-4L")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("foss22/pruned32L-RuadaptQwen3-4B-4L")
model = AutoModelForCausalLM.from_pretrained("foss22/pruned32L-RuadaptQwen3-4B-4L", 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 foss22/pruned32L-RuadaptQwen3-4B-4L with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "foss22/pruned32L-RuadaptQwen3-4B-4L"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "foss22/pruned32L-RuadaptQwen3-4B-4L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/foss22/pruned32L-RuadaptQwen3-4B-4L

SGLang

How to use foss22/pruned32L-RuadaptQwen3-4B-4L 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 "foss22/pruned32L-RuadaptQwen3-4B-4L" \
    --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": "foss22/pruned32L-RuadaptQwen3-4B-4L",
		"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 "foss22/pruned32L-RuadaptQwen3-4B-4L" \
        --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": "foss22/pruned32L-RuadaptQwen3-4B-4L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use foss22/pruned32L-RuadaptQwen3-4B-4L with Docker Model Runner:
```
docker model run hf.co/foss22/pruned32L-RuadaptQwen3-4B-4L
```

Model Card for Model Flight-flight Flight flight-flightflight

Removing 32 layers from RefalMachine/RuadaptQwen3-4B-Instruct === Original Model: RefalMachine/RuadaptQwen3-4B-Instruct Parameters: 4,007,937,536 Layers: 36

--- Original Model Generation --- Prompt: 'Paris is the capital of' Generated: Paris is the capital of France and one of the world's most visited cities, attracting over 22 million tourists annually. It is renowned for its rich history, iconic landmarks, and vibrant cultural scene. Located in the north-central part of metropolitan France

Prompt: 'The theory of relativity states that' Generated: The theory of relativity states that the speed of light in a vacuum is constant for all observers, regardless of their motion or the motion of the light source. However, when light travels through a medium such as water or glass, its speed decreases.

Removing layers: 100%|██████████| 36/36 [00:00<00:00, 324720.31it/s] --- Pruned Model: RefalMachine/RuadaptQwen3-4B-Instruct Parameters: 778,151,424 Layers: 4

--- Pruning Results --- Parameter reduction: 3,229,786,112 (80.58%) Layer reduction: 32 layers (88.89%)

--- Pruned Model Generation ---

Prompt: 'Paris is the capital of' Generated: Paris is the capital of-flight-flight flight flightflightflight flights flightsflight flight Flight Flight-flight Flight flight-flightflight Flightflight-flight flights flightkuskusorientorientorientationorientation orientation orientation Orientation Orientation orientationorientationOrientationOrientation orientationOrientationorientation orientations orientations OrientationOrientation.Orientation.Orientation

Prompt: 'The theory of relativity states that' Generated: The theory of relativity states thatьюьюистаиста.); emsemsobileobilefractionfraction fractions fractions_fraction_fractionfractionfrafraurgeonurgeonasanasanrelevantrelevant Relevant Relevant relevance relevance-minded-mindednessnessNESSNESSnesnes-addons-addonsellanellanuclear核核

Example 1 completed! Parameter reduction: 80.58%

Model Details

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Safetensors

Model size

0.8B params

Tensor type

F16

Paper for foss22/pruned32L-RuadaptQwen3-4B-4L

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 60