Instructions to use Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 with Transformers:

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

pipe = pipeline("text-generation", model="Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951")
model = AutoModelForMultimodalLM.from_pretrained("Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951")
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 Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951

SGLang

How to use Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 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 "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951" \
    --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": "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951",
		"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 "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951" \
        --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": "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951",
    max_seq_length=2048,
)

Docker Model Runner
How to use Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951 with Docker Model Runner:
```
docker model run hf.co/Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951
```

Qwen3-1.7B Math SFT - Anti-Overfitting Version

Trained with anti-overfitting measures based on "A Practical Two-Stage Recipe for Mathematical LLMs" paper.

Training Details

Base Model: unsloth/Qwen3-1.7B
Parameters: 1,720,032,256 (all fine-tuned)
Epochs: 10
Batch Size: 8
Learning Rate: 5e-06 (reduced for stability)
Weight Decay: 0.1 (increased regularization)
Approach: Full model training with anti-overfitting measures

Anti-Overfitting Measures

Reduced learning rate: 5e-06
Increased weight decay: 0.1
Extended warmup: 10% of steps
Early stopping on validation loss
Regular evaluation checkpoints

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained(
    "Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951",
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951")

messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \boxed{}."},
    {"role": "user", "content": "What is 2+2?"}
]

inputs = tokenizer.apply_chat_template(messages, tokenize=True, return_tensors="pt")
outputs = model.generate(input_ids=inputs, max_new_tokens=256)

Training timestamp: 20250724_165951

Downloads last month: 4

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Cbgcbg/qwen3-1.7b-math-sft-antioverfitting-20250724_165951

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

unsloth/Qwen3-1.7B

Finetuned

(152)

this model