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 82, in _split_generators
raise ValueError(
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
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.
- Downloads last month
- 31