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/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/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.
vllm-w4a16-dsv4:exp — pre-built Docker image for DGX Spark TP=2
This dataset hosts a pre-built OCI tarball of the Docker image used to serve
pastapaul/DeepSeek-V4-Flash-W4A16-FP8
on dual DGX Spark GB10 (SM 12.1a) — for users who can't build the image
themselves due to network constraints.
The image is also fine for 2× RTX PRO 6000 Blackwell Server (SM 12.0).
What's inside
vLLM build pinned to:
jasl/vllm@ds4-sm120-experimental(HEADc05638d70after cherry-pick) — SM12x DSV4 support + experimental superset (split-KV decode, GB10 fused-MoE config aliases, tuned MLA graph defaults)- Cherry-pick
f910a73a93fromneuralmagic/vllm@kylesayrs/deepseek-ct(vLLM PR #41276 — DSV4 compressed-tensors attention) - Local patch
patch_v4_packed_mapping.py— addspacked_modules_mappingtoDeepseekV4ForCausalLM - Workspace pre-reservation patch is not applied — landed upstream as
jasl/vllm@1d6f5c4
Compressed tarball: ~9 GB. Uncompressed image: 20.2 GB. ARM64 only.
How to use
# Download
huggingface-cli download pastapaul/dsv4-flash-w4a16-spark-image \
vllm-w4a16-dsv4-exp.tar.gz \
--repo-type dataset \
--local-dir .
# Load on each Spark
gunzip -c vllm-w4a16-dsv4-exp.tar.gz | docker load
# Verify
docker images vllm-w4a16-dsv4:exp
Once loaded on both Sparks, you can use the bootstrap script with --skip-build:
curl -fsSLO https://raw.githubusercontent.com/pasta-paul/dsv4-flash-w4a16-fp8/main/scripts/bootstrap_dsv4_spark.sh
chmod +x bootstrap_dsv4_spark.sh
./bootstrap_dsv4_spark.sh \
--head-host spark-a \
--worker-host spark-b \
--skip-build
The script will then handle network setup, model pre-cache (no HF token needed), container launch on both nodes, and wait for /health=200.
What you get
Production canonical at 1 M-token context TP=2 graphs-ON:
- decode 12 t/s smoke / 14–15 t/s think-max sustained
- NIAH 4/4 retrieval at 200K-token haystack
- mini-suite 10/10 PASS · think-max 3/3 PASS
- GSM8K 95.00% strict / 94.92% flex (preserved from prior canonical)
- tool calling: parallel function calls with structured arguments
Reference docs
- Model: https://huggingface.co/pastapaul/DeepSeek-V4-Flash-W4A16-FP8
- Reproduction repo + raw evidence: https://github.com/pasta-paul/dsv4-flash-w4a16-fp8
- Quickstart: https://github.com/pasta-paul/dsv4-flash-w4a16-fp8/blob/main/findings/QUICKSTART_DUAL_SPARK.md
- Phase 4e validation report: https://github.com/pasta-paul/dsv4-flash-w4a16-fp8/blob/main/findings/spark_tp2_deployment.md#phase-4e--production-canonical-at-1-m-context-on-ds4-sm120-experimental-2026-05-06
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