Instructions to use ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29 with Transformers:

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

pipe = pipeline("text-generation", model="ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29")
model = AutoModelForMultimodalLM.from_pretrained("ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29

SGLang

How to use ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29 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 "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29" \
    --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": "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29",
		"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 "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29" \
        --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": "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29 with Docker Model Runner:
```
docker model run hf.co/ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29
```

Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29

RL post-training of Qwen/Qwen3.5-9B on a multi-turn, native-tool-calling (ReAct-style) mixture of math + code + search tasks, using GRPO with an SDPO self-skill objective ("arm e").

This is the step-29 checkpoint, which is the peak of the training curve — see Why step 29 below.

Language model only. The conversion from the training checkpoint exports the 427 language-model tensors and drops the base model's vision tower (333 tensors) and MTP heads (15 tensors), because only the language model was trained. config.json still declares Qwen3_5ForConditionalGeneration for compatibility with the base tokenizer/config, but image input will not work. Text generation loads and serves normally under Transformers / vLLM / SGLang.

Training setup


Base	`Qwen/Qwen3.5-9B`
Algorithm	GRPO + SDPO (self-skill-all, skill-KD `mode=both`, KD coef 0.01, `--no-sdpo-pure-distill`, `--sdpo-response-prefix skill`)
Domains	math, code, search — one mixed multitask stream
Rollouts	multi-turn native tool calling, up to 20 turns; thinking enabled
Max response length	16384 tokens (train and eval matched)
Steps	31 rollouts trained; this checkpoint is step 29

Held-out results

pass@1 / pass@8, greedy-free sampling, 16384-token response cap:

Benchmark	pass@1	pass@8
AIME 2024	84.2	96.7
AIME 2025	81.7	100.0
AMO-Bench	28.0	52.0
OJBench (medium, 77 problems)	31.8	59.7

AMO-Bench and OJBench are the best numbers we have on record for a 9B model in this line of work (previous best: AMO 22.7, OJBench 29.5).

Caveats worth knowing before you compare against these:

AIME 2024/2025 are saturated for this model — pass@8 reaches 100%, so they can no longer rank checkpoints. Our training curve uses AIME-2026 instead.
AMO-Bench pass@8 is depressed by truncation, not by reasoning failure: the median sample sits exactly at the 16384-token cap and 18.5% of samples are truncated. A larger generation budget should raise it.
No tool-use collapse: on OJBench only 15.7% of rollouts answer without calling a tool, versus 74.0% for the base model, and repetition rate is 0.

Why step 29

Two independent runs of this arm agree to within 0.6pp at every shared eval step, and both peak at step 29. Aggregate held-out pass@1 by step:

step	0	9	19	29	39	49
51-rollout run	58.1	67.3	71.0	73.6	71.2	70.0
31-rollout run (this one)	58.2	68.4	71.6	73.0	—	—

Past step 29 performance decays monotonically, led by code (LiveCodeBench-v6-functional 63.3 → 59.1) with repetition rate climbing 0 → 0.6 → 4.2%. Training this arm longer is actively worse, so 31 rollouts is the right budget and step 29 is the checkpoint to use.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, dtype="auto", device_map="auto")

The model was trained with thinking enabled and with native tool calling, so serve it with the shipped chat_template.jinja and pass tools through the template's tools argument. Under SGLang, use the qwen3_coder tool-call parser (Qwen3.5 emits XML-style <function=...> calls, not Qwen2.5-style JSON).

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Safetensors

Model size

9B params

Tensor type

BF16

F32

Model tree for ipfipfipf/Qwen3.5-9B-sdpo-react-mathcodesearch-grpo-arm-e-step29

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

(698)

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