Instructions to use modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6 with Transformers:

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

pipe = pipeline("text-generation", model="modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6")
model = AutoModelForCausalLM.from_pretrained("modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6

SGLang

How to use modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6 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 "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6" \
    --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": "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6",
		"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 "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6" \
        --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": "modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6 with Docker Model Runner:
```
docker model run hf.co/modrill/math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6
```

math_think_11_qwen3_4b_base_task_arithmetic_scaling_0_6

Task Arithmetic merge between math_think_11 Qwen3-4B-Base SFT and Qwen/Qwen3-4B-Base, with scaling coefficient 0.6.

Method: task arithmetic (theta = theta_base + scaling * (theta_sft - theta_base))
Base model: math_think_11_qwen3_4b_base_sft (Qwen3-4B-Base)
Other model: Qwen/Qwen3-4B-Base
Scaling: 0.6
Renamed from: scaling_0_6

Downloads last month: 73

Safetensors

Model size

4B params

Tensor type

BF16