The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 45, in _get_pipeline_from_tar
current_example[field_name] = f.read()
^^^^^^^^
File "/usr/local/lib/python3.12/tarfile.py", line 693, in read
raise ReadError("unexpected end of data")
tarfile.ReadError: unexpected end of data
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Track B Phase 3 Submission
Team: Works on my agent
This archive contains the runnable submission for Track B Phase 3.
Environment
Python 3.11 is recommended for the inference runner:
python3.11 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt
Our local validation environment used Huawei Ascend 910B hardware.
The runner does not require internet access at runtime. It connects only to the local vLLM OpenAI-compatible API and the local Track B sandbox APIs.
Default endpoints:
vLLM API: http://localhost:8001/v1
Command API: https://localhost:8080/ip/api/agent/execute
Device discovery: http://localhost:8080/ip/api/agent/get_devices_list
Directory Structure
.
├── README.md
├── run.sh
├── run.py
├── requirements.txt
├── src/
│ └── track_b_agent/
│ ├── submission_run.py
│ ├── submission_io.py
│ ├── run_agent.py
│ ├── agent/
│ ├── config/
│ ├── generalized/
│ ├── knowledge/
│ ├── prompts/
│ └── tools/
└── models/
├── deploy.sh
├── config.json
├── tokenizer.json
├── tokenizer_config.json
├── model.safetensors.index.json
├── model-00001-of-00016.safetensors
├── ...
└── model-00016-of-00016.safetensors
Deploy the Model
The model files are stored in models/. Start the vLLM OpenAI-compatible server with:
bash models/deploy.sh
The deployment script serves the model as:
Qwen3.5-35B-A3B
It uses models/ as the model path and starts vLLM on port 8001 with nohup. Logs are written to:
models/Qwen3.5-35B-A3B_vllm_output.log
If there are any vLLM-environment-related issues, please contact:
250010135@slai.edu.cn
Run Inference
Run the submission on the provided Track B test file:
bash run.sh --input /path/to/test.json --output result
run.sh uses .venv/bin/python from the local virtual environment created above.
The runner uses 5-way scenario concurrency by default. The implementation uses asyncio with a 5-worker semaphore rather than the ThreadPoolExecutor shown in the example guide; the effective concurrent dispatch limit is still 5.
Expected Output
The run command writes the required files into the output directory:
result/
├── traces.json
├── results.csv
└── runtime.json
results.csv contains one final prediction per scenario:
scenario_id,prediction
traces.json contains all generated completions recorded during inference.
runtime.json contains per-problem runtime in seconds.
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