Instructions to use EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1 with Transformers:

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

pipe = pipeline("text-generation", model="EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1")
model = AutoModelForMultimodalLM.from_pretrained("EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1")
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 EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1

SGLang

How to use EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1 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 "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1" \
    --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": "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1",
		"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 "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1" \
        --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": "EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1 with Docker Model Runner:
```
docker model run hf.co/EganAI/Qwen3-4B-Thinking-2507-20250813-033307-1
```

Qwen3-4B-Thinking-2507-20250813-033307-1

File size: 1,746 Bytes

99dcd08

{
  "architectures": [
    "Qwen3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": 151643,
  "eos_token_id": 151645,
  "head_dim": 128,
  "hidden_act": "silu",
  "hidden_size": 2560,
  "initializer_range": 0.02,
  "intermediate_size": 9728,
  "max_position_embeddings": 262144,
  "max_window_layers": 36,
  "model_type": "qwen3",
  "num_attention_heads": 32,
  "num_hidden_layers": 36,
  "num_key_value_heads": 8,
  "rms_norm_eps": 1e-06,
  "rope_scaling": null,
  "rope_theta": 5000000,
  "sliding_window": null,
  "tie_word_embeddings": true,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.51.0",
  "use_cache": true,
  "use_sliding_window": false,
  "vocab_size": 151936,
  "genetic_merge_info": {
    "run_id": "20250813_033307",
    "task": "gpqa_diamond_zeroshot",
    "best_fitness": 0.43434343434343436,
    "improvement_percentage": 19.444444444444446,
    "source_models": [
      "Qwen/Qwen3-4B-Thinking-2507",
      "Qwen/Qwen3-4B-Thinking-2507-FP8",
      "unsloth/Qwen3-4B-Thinking-2507",
      "ertghiu256/Qwen3-4b-tcomanr-merge-v2",
      "huihui-ai/Huihui-Qwen3-4B-Thinking-2507-abliterated",
      "janhq/Jan-v1-4B",
      "BRlkl/BingoGuard-qwen3-4B-pt-grpo",
      "fireworks1231/agentic-4b-2607-sft",
      "sequelbox/Qwen3-4B-Thinking-2507-DAG-Reasoning",
      "ReallyFloppyPenguin/Mastermind-2x4b-thinking"
    ],
    "configuration": [
      4,
      0,
      4,
      2,
      9,
      6,
      0,
      0,
      2,
      2,
      7,
      3,
      8,
      2,
      3,
      9,
      7,
      7,
      2,
      8,
      3,
      9,
      4,
      3,
      5,
      6,
      3,
      6,
      3,
      0,
      8,
      6,
      6,
      2,
      0,
      3
    ]
  }
}