Instructions to use beyoru/Evol-Aes-Hybrid-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use beyoru/Evol-Aes-Hybrid-4B with Transformers:

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

pipe = pipeline("text-generation", model="beyoru/Evol-Aes-Hybrid-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("beyoru/Evol-Aes-Hybrid-4B")
model = AutoModelForCausalLM.from_pretrained("beyoru/Evol-Aes-Hybrid-4B")
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 beyoru/Evol-Aes-Hybrid-4B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/beyoru/Evol-Aes-Hybrid-4B

SGLang

How to use beyoru/Evol-Aes-Hybrid-4B 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 "beyoru/Evol-Aes-Hybrid-4B" \
    --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": "beyoru/Evol-Aes-Hybrid-4B",
		"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 "beyoru/Evol-Aes-Hybrid-4B" \
        --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": "beyoru/Evol-Aes-Hybrid-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use beyoru/Evol-Aes-Hybrid-4B with Docker Model Runner:
```
docker model run hf.co/beyoru/Evol-Aes-Hybrid-4B
```

🧬 Evol-Aes-Hybrid-4B: Evolutionary Optimized Merge

💻 GitHub Repo • 🤗 Model Collection

📖 Overview

Evol-Aes-Hybrid-4B is a high-performance 4B parameter model created using an advanced Evolutionary Layer-Wise Merge strategy. By combining the generalist capabilities of Instruct with the logical prowess of Thinking models, this hybrid achieves a superior balance between conversational fluency and reasoning depth.

This model is designed to be a robust starting point for SFT (Supervised Fine-Tuning) or GRPO training, offering a stabilized foundation that outperforms standard merges.

🧩 Base Models

This model is a genetic evolution of the following parents:

Base (Generalist): Qwen/Qwen3-4B-Instruct-2507
Source (Specialist): Qwen/Qwen3-4B-Thinking-2507

💡 Design Philosophy: Pure thinking models often over-reason on simple tasks, while instruct models may lack depth. This hybrid applies a "genetic selection" to retain the best layers from both worlds.

⚙️ Evolutionary Methodology

Unlike traditional linear merges (SLERP/TIES), this model was optimized using a Genetic Algorithm (GA) to find the perfect weight ratio for each individual layer.

🔬 Experiment Settings

Optimization Target: Minimized metrics on the openai/gsm8k dataset.
Evaluation Subset: 100 challenging samples.
Population Size: 10 candidates per generation.
Generations: 30 evolution cycles.
Algorithm: Layer-wise weighted averaging with mutation and crossover (Elitism strategy).

🧬 Layer Composition Analysis

The evolutionary process discovered a non-linear structure:

Early Layers (0-5): Heavily favors the Instruct Base (~65%) to maintain robust language understanding and grammar.
Middle Layers: Incorporates Thinking modules for logic processing.
Specific Heads: Selectively rejected unstable layers from the Thinking model (e.g., layers with high Alpha metrics) to ensure stability.

📊 Evaluation & Performance

Rank	Model	Overall Score
1	Evol-Aes-Hybrid-4B	0.699
2	EvoLLM	0.663
3	Qwen3-2507 (Base)	0.639
4	Darwin-Qwen3-4B	0.610

💻 Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "beyoru/Evol-Aes-Hybrid-4B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Solve 2x+1=3 !!!"},
]

input_ids = tokenizer.apply_chat_template(
    messages, 
    add_generation_prompt=True, 
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    input_ids,
    max_new_tokens=512,
)

print(tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True))

🤗 Support me at:

📜 Citation

If you use this model or the evolutionary merge method in your work, please cite:

@misc{nafy_qwen_merge_2025,
  title        = {EvolLLM: Evolutionary Optimized Qwen3 Hybrid},
  author       = {Beyoru},
  year         = {2025},
  publisher    = {Hugging Face},
  howpublished = {\url{[https://huggingface.co/beyoru/Evol-Aes-Hybrid-4B](https://huggingface.co/beyoru/Evol-Aes-Hybrid-4B)}},
  note         = {Merged model combining instruction-tuned and reasoning Qwen3 variants via Genetic Algorithm.},
  base_models  = {Qwen/Qwen3-4B-Instruct-2507, Qwen/Qwen3-4B-Thinking-2507}
}

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Safetensors

Model size

4B params

Tensor type

BF16

Model tree for beyoru/Evol-Aes-Hybrid-4B

Qwen/Qwen3-4B-Instruct-2507

Qwen/Qwen3-4B-Thinking-2507

Merge model

this model

beyoru
/

Evol-Aes-Hybrid-4B