Instructions to use zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt with Transformers:

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

pipe = pipeline("text-generation", model="zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt")
model = AutoModelForCausalLM.from_pretrained("zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt", device_map="auto")
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 zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt

SGLang

How to use zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt 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 "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt" \
    --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": "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt",
		"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 "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt" \
        --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": "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt with Docker Model Runner:
```
docker model run hf.co/zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt
```

Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt

This is the 25-update Math checkpoint from the Agentic ESOpt run.

Important evaluation note

The reported scores are produced by an agentic, multi-turn ReAct evaluation with a bash tool. They are not one-shot chat or plain transformers.generate() scores. Reproducing the report requires the system prompt, action parser, bash execution loop, answer parser, sampling parameters, and datasets in zz1358m/Agentic-ESOpt.

This checkpoint and the stored Agentic ESOpt report are no-skill (--skill-file ''). The fixed ReAct system prompt is part of the evaluation harness; it is not an external Trace2Skill skill and no skill is baked into the weights.

Weight lineage and integrity

Base runtime checkpoint: Qwen3.5-4B-text (Qwen/Qwen3.5-4B architecture)
ES scope: full model, 330 tensors / 4,205,751,296 parameters
Applied updates: 25, generations 0 through 24
Update rule: stored ES seeds and z-score-normalized weights, alpha 0.0005
Weight format: two indexed safetensors shards
Shard 1 SHA-256: 4563e49054b3d71920bea9c302e350d3fac9184f0c5d19e823c2f1c3183c7283
Shard 2 SHA-256: 5aee21fccbdca7441612716b6e10abe7bce08763880acb3645e21ec0abc3f9fd

replay_metadata.json records the lineage and replayed_history.json contains the exact 25 update records.

Direct post-export verification

On 2026-08-11, the uploaded checkpoint was loaded directly into vLLM without replaying ES history and evaluated with the report's ReAct/bash protocol, data, one-sample seed, 50-turn limit, 4096-token turn limit, and exact-fallback scorer:

DAPO100: 0.82 (82/100)
AIME2026-30: 0.7667 (23/30)
Request/parser errors: 0
Final-answer termination: 130/130

This direct check exceeds the stored generation-25 report point (DAPO 0.76, AIME 0.70) and confirms that the exported weights reproduce the reported capability. Sampling is not byte-identical across vLLM engine scheduling, eager/compiled kernels, or GPU types, so compare aggregate metrics.

The stored four-sample final report is:

DAPO mean: 0.7675; max@4: 0.86
AIME mean: 0.708333; max@4: 0.966667

Machine-readable results and hashes are in evaluation_results.json.

Report environment

Python 3.10.12
CUDA 12.8
PyTorch 2.10.0+cu128
vLLM 0.19.1
Transformers 4.57.6
Ray 2.56.1
4 x 80 GB GPUs, 8 Ray/vLLM engines (gpu_fraction=0.5)
gpu_memory_utilization=0.42
context length 131072, no ReAct context trimming
50 turns, 4096 generated tokens per turn
temperature 1.0, top-p 1.0, top-k 40, presence penalty 2.0
exact-fallback scoring (the report environment did not have math-verify installed)

The dense Qwen3Next compatibility patch in the repository must be enabled for vLLM 0.19.1. The dedicated repository entry point is:

MATH_REPORT_MODE=interval25 scripts/math/eval_hf_checkpoint_4gpu.sh

For the four-sample report:

MATH_REPORT_MODE=final4 scripts/math/eval_hf_checkpoint_4gpu.sh

Dataset SHA-256:

dapo_test.jsonl: a0e64c93e7801957f0949ab80f5a26233ecd87a02ad5c4628de2da0692b5c4a2
aime_2026.jsonl: abc8651f3af75ff59341b9de986fef39b1e909aa1466e3b73ee20ec9b6f7242e

Loading the weights

from transformers import AutoModelForCausalLM, AutoTokenizer

path = "zz1358m/Qwen3.5-4B-MATH-ReAct-Agentic-ESOpt"
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, dtype="auto")

Loading succeeds, but the report scores require the agentic harness described above.