Instructions to use foss22/pruned34L-RuadaptQwen3-4B-2L with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use foss22/pruned34L-RuadaptQwen3-4B-2L with Transformers:

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

pipe = pipeline("text-generation", model="foss22/pruned34L-RuadaptQwen3-4B-2L")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("foss22/pruned34L-RuadaptQwen3-4B-2L")
model = AutoModelForCausalLM.from_pretrained("foss22/pruned34L-RuadaptQwen3-4B-2L")
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/pruned34L-RuadaptQwen3-4B-2L with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "foss22/pruned34L-RuadaptQwen3-4B-2L"
# 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/pruned34L-RuadaptQwen3-4B-2L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/foss22/pruned34L-RuadaptQwen3-4B-2L

SGLang

How to use foss22/pruned34L-RuadaptQwen3-4B-2L 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/pruned34L-RuadaptQwen3-4B-2L" \
    --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/pruned34L-RuadaptQwen3-4B-2L",
		"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/pruned34L-RuadaptQwen3-4B-2L" \
        --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/pruned34L-RuadaptQwen3-4B-2L",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use foss22/pruned34L-RuadaptQwen3-4B-2L with Docker Model Runner:
```
docker model run hf.co/foss22/pruned34L-RuadaptQwen3-4B-2L
```

Model Card for Model timber Timчикчикчиковчиковчикчикичикичиковчикомчикомчик

Depth Pruning by Percentage

Loading weights: 100% 398/398 [00:39<00:00, 10.60it/s] === Example 2: Removing 95% of layers from RefalMachine/RuadaptQwen3-4B-Instruct === Original Model: RefalMachine/RuadaptQwen3-4B-Instruct Parameters: 4,007,937,536 Layers: 36 Removing layers: 100%|██████████| 36/36 [00:00<00:00, 338553.69it/s] --- Pruned Model: RefalMachine/RuadaptQwen3-4B-Instruct Parameters: 576,289,792 Layers: 2

--- Pruning Results --- Target layer reduction: 95% Actual layer reduction: 34 layers (94.44%) Parameter reduction: 3,431,647,744 (85.62%)

--- Pruned Model Generation ---

Prompt: 'Paris is the capital of' Generated: Paris is the capital ofivoivo timber timber tim tim Tim TimTimTim Timtimtim_tim_timtim timtim Tim timTimtim TIM TIM Tim_tim tim.tim.timtimTim tim_tim Timчикчикчиковчиковчикчикичикичиковчикомчикомчик

Example 2 completed! Parameter reduction: 85.62%

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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Uses

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Training Details

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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

0.6B params

Tensor type

F16

Paper for foss22/pruned34L-RuadaptQwen3-4B-2L

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 58