Instructions to use webAI-Official/math-merged-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use webAI-Official/math-merged-adapter with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "webAI-Official/math-merged-adapter")

Transformers

How to use webAI-Official/math-merged-adapter with Transformers:

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

pipe = pipeline("text-generation", model="webAI-Official/math-merged-adapter")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("webAI-Official/math-merged-adapter", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use webAI-Official/math-merged-adapter with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/webAI-Official/math-merged-adapter

SGLang

How to use webAI-Official/math-merged-adapter 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 "webAI-Official/math-merged-adapter" \
    --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": "webAI-Official/math-merged-adapter",
		"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 "webAI-Official/math-merged-adapter" \
        --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": "webAI-Official/math-merged-adapter",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use webAI-Official/math-merged-adapter with Docker Model Runner:
```
docker model run hf.co/webAI-Official/math-merged-adapter
```

Combined SFT + GRPO LoRA Adapter for Qwen3-4B

This adapter combines two LoRA training stages into a single adapter:

SFT (Supervised Fine-Tuning) on Qwen/Qwen3-4B
GRPO (Group Relative Policy Optimization) on the SFT model

The two rank-32 adapters were merged into a single rank-64 adapter (lossless). Apply directly to Qwen/Qwen3-4B — no intermediate merged model needed.

Usage

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
model = PeftModel.from_pretrained(base_model, "abdul-hannan/qwen3-math-grpo")
tokenizer = AutoTokenizer.from_pretrained("abdul-hannan/qwen3-math-grpo")

Training Details

Base model: Qwen/Qwen3-4B
LoRA rank: 64 (combined from two rank-32 adapters)
LoRA alpha: 128
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
PEFT version: 0.18.1

Contact

Syed Abdul Hannan

Framework versions

PEFT 0.18.1

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