Instructions to use foss22/pruned26L-RuadaptQwen3-4B-10L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foss22/pruned26L-RuadaptQwen3-4B-10L with Transformers:

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

pipe = pipeline("text-generation", model="foss22/pruned26L-RuadaptQwen3-4B-10L")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("foss22/pruned26L-RuadaptQwen3-4B-10L")
model = AutoModelForMultimodalLM.from_pretrained("foss22/pruned26L-RuadaptQwen3-4B-10L")
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/pruned26L-RuadaptQwen3-4B-10L with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "foss22/pruned26L-RuadaptQwen3-4B-10L"
# 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/pruned26L-RuadaptQwen3-4B-10L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/foss22/pruned26L-RuadaptQwen3-4B-10L

SGLang

How to use foss22/pruned26L-RuadaptQwen3-4B-10L 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/pruned26L-RuadaptQwen3-4B-10L" \
    --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/pruned26L-RuadaptQwen3-4B-10L",
		"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/pruned26L-RuadaptQwen3-4B-10L" \
        --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/pruned26L-RuadaptQwen3-4B-10L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use foss22/pruned26L-RuadaptQwen3-4B-10L with Docker Model Runner:
```
docker model run hf.co/foss22/pruned26L-RuadaptQwen3-4B-10L
```

Model Card for Model ID

Model Details

Model Description

Removing 26 layers from 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, 235194.62it/s] --- Pruned Model: RefalMachine/RuadaptQwen3-4B-Instruct Parameters: 1,383,736,320 Layers: 10

--- Pruning Results --- Parameter reduction: 2,624,201,216 (65.48%) Layer reduction: 26 layers (72.22%)

--- Pruned Model Generation ---

Prompt: 'Paris is the capital of' Generated: Paris is the capital ofmindlessamenterics getArguments argumentsстаходходсякаясясякийкийломовскийowskiowskiкуроновavirus pandemic pandemicінийскийскийскомановсяkosсяскойскомскийломецкийばшиестьяжиковикованов

Prompt: 'The theory of relativity states that' Generated: The theory of relativity states thatophys physicists physicistsэнсяENCEScapeable measurable measurableizableizable Technologiescape знанияведенияведениячинстваствоствастваствутуKENkenohanaотовкаясяся......

?(fulnessfulamentericsallymenteamente Swal Swal

Example 1 completed! Parameter reduction: 65.48%

DEPTH PRUNING RESULTS SUMMARY

Model Method Param Reduction Layers Removed

RefalMachine/RuadaptQwen3-4B-Instruct Layer Count 65.48 % 26/36
RefalMachine/RuadaptQwen3-4B-Instruct Percentage 70.51 % 28/36

Total examples tested: 2 Depth pruning examples completed successfully!

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Paper [optional]: [More Information Needed] Apache 2, same as https://github.com/peremartra/optipfair
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How to Get Started with the Model

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Training Details

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Environmental Impact

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

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Safetensors

Model size

1B params

Tensor type

F16

Paper for foss22/pruned26L-RuadaptQwen3-4B-10L

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 51