Instructions to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 with Transformers:

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

pipe = pipeline("text-generation", model="Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659")
model = AutoModelForCausalLM.from_pretrained("Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659")
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

vLLM

How to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659"
# 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/qwen3-8b-math-full-sft-epoch11-20250725_161659",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659

SGLang

How to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 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/qwen3-8b-math-full-sft-epoch11-20250725_161659" \
    --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/qwen3-8b-math-full-sft-epoch11-20250725_161659",
		"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/qwen3-8b-math-full-sft-epoch11-20250725_161659" \
        --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/qwen3-8b-math-full-sft-epoch11-20250725_161659",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 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/qwen3-8b-math-full-sft-epoch11-20250725_161659 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/qwen3-8b-math-full-sft-epoch11-20250725_161659 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659",
    max_seq_length=2048,
)

Docker Model Runner
How to use Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659 with Docker Model Runner:
```
docker model run hf.co/Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659
```

Qwen3-8B Math SFT - Epoch 11 Checkpoint

Full parameter fine-tuning checkpoint from mathematical reasoning training.

📊 Training Details

Base Model: unsloth/Qwen3-8B (full precision)
Training Method: Full parameter fine-tuning (92.4% parameters trained)
Progress: Epoch 11/20 (55% complete)
Dataset: Paper's Official Dataset (7,110 training samples)
Configuration: Paper's exact Stage 1 SFT settings

🔧 Training Configuration

Batch Size: 1 x 16 = 16 effective
Learning Rate: 1e-5 (paper's exact)
Max Sequence Length: 24,000 (paper's exact)
Optimizer: paged_adamw_8bit
Scheduler: cosine
Epochs: 20 total

🎯 Expected Performance

Epoch 11 Characteristics:

Mid-Stage: Strong mathematical reasoning capability. Good accuracy on most problems, well-structured solutions.

📈 Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

# Load model
model = AutoModelForCausalLM.from_pretrained(
    "Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("Cbgcbg/qwen3-8b-math-full-sft-epoch11-20250725_161659")

# Example usage
messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"role": "user", "content": "Find the derivative of f(x) = x^3 + 2x^2 - 5x + 3"}
]

inputs = tokenizer.apply_chat_template(
    messages, 
    tokenize=True, 
    return_tensors="pt", 
    add_generation_prompt=True
)

outputs = model.generate(
    inputs, 
    max_new_tokens=512, 
    temperature=0.7,
    do_sample=True
)

response = tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True)
print(response)

🔗 Related Models

Paper Source: "A Practical Two-Stage Recipe for Mathematical LLMs"
Training Approach: Full parameter fine-tuning (Stage 1 SFT only)
Final Model: Will be available after 20 epochs complete

📅 Training Timeline

Started: 20250725_161659
Current: Epoch 11/20 checkpoint
Status: Intermediate checkpoint

This model follows the exact configuration from the paper's Stage 1 SFT approach with full parameter fine-tuning for optimal mathematical reasoning performance.

Downloads last month: 1

Safetensors

Model size

8B params

Tensor type

BF16

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