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Datasets:
pastapaul
/
dsv4-flash-w4a16-spark-image

Tags:
deepseek-v4
vllm
dgx-spark
blackwell
sm120
sm121
License:
Dataset card Files
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Cannot get the split names for the config 'default' of the dataset.
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 90, in _split_generators
                  inferred_arrow_schema = pa.concat_tables(pa_tables, promote_options="default").schema
                                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 6319, in pyarrow.lib.concat_tables
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowTypeError: Unable to merge: Field json has incompatible types: struct<manifests: list<item: struct<annotations: struct<io.containerd.image.name: string, org.opencontainers.image.ref.name: string>, digest: string, mediaType: string, size: int64>>, mediaType: string, schemaVersion: int64> vs list<item: struct<Config: string, LayerSources: struct<sha256:001461549aa10ce2e2e561ef1832ba2eade9168211d2643c7c24aaecbdc47a60: struct<digest: string, mediaType: string, size: int64>, sha256:26c4202e143a608a1a8358a67268d5098a00ff5b866ecfcd3d3b397480f48972: struct<digest: string, mediaType: string, size: int64>, sha256:36999888a3c853828217ba3eab1f63784220d3cb170479b9661c77ff15799bcf: struct<digest: string, mediaType: string, size: int64>, sha256:4cf29c1b59083414e65a3ebcab217fd108690d353034d075cc128928d4649119: struct<digest: string, mediaType: string, size: int64>, sha256:505bc9333fea657b28c6ca96326886178769694f05426787ff8cca8b82352a24: struct<digest: string, mediaType: string, size: int64>, sha256:539926fe12daa7de499df42e20a693fca1c3a4db1e61a1ce25a32df3e8041d3e: struct<digest: string, mediaType: string, size: int64>, sha256:53f586ec0996b4fd2f18b6eb9f92a8c4137149bf8e71dd8a0ce47042355b12e5: struct<digest: string, mediaType: string, size: int64>, sha256:557d105fc8723855f95ece306f0c82ae021f9ee8cb67951d1962abd5d19985e9: struct<digest: string, mediaType: string, size: int64>, sha256:59a0bbf0604b6056f47220d7c9046ded5639b9e2a2b2fb3e5a635f17253cb4d0: struct<digest: string, mediaType: string, size: int64>, sha256:5f70bf18a086007016e948b04aed3b82103a36bea41755b6cddfaf10ace3c6ef: struct<digest: string, mediaType: string, size: int64>, sha256:6efc644ac8aa4dc37e53ce45c08839502ee292506ead014d4861c90f09f10fca: struct<digest: string, mediaType: string, size: int64>, sha256:7fed45e3d081913208c933e4f9a5528a5d0abcdc8e8b6c5209cdaeafc85d3a41: struct<digest: string, mediaType: string, size: int64>, sha256:836515926d7c4f7a4cc430e0150ef080a4fa15e84ee8b0f00fc0865efde66b66: struct<digest: string, mediaType: string, size: int64>, sha256:84fbad02f97c11c71c7aa671fb061bfbc1d7722284da054b2d52cc9c881fa88b: struct<digest: string, mediaType: string, size: int64>, sha256:a0aed9a26128a7a4c9b13b8c171b453f1a00808cb32e2075d6ba7675eac56913: struct<digest: string, mediaType: string, size: int64>, sha256:cbe762873068597a48092bc47583f4298bdae325f6d5846a22ab1915db2e706b: struct<digest: string, mediaType: string, size: int64>, sha256:e5dae71ade4390c09123a86ada6c9bc64ac469d0495acae5b2216a627395050c: struct<digest: string, mediaType: string, size: int64>, sha256:e67c3dff55b16af41294d759c61580b6ccb77a587737c56b9958240db43b0a84: struct<digest: string, mediaType: string, size: int64>, sha256:ea15b1deb057a7daaa489337b00d80c974152947f4adb942f06b7a5b61822ac4: struct<digest: string, mediaType: string, size: int64>, sha256:ea23415f374972a39d0d75e2e361093bb6ecbfaf31c8f63bdb9267adae8645eb: struct<digest: string, mediaType: string, size: int64>, sha256:eb9ffcd8102a913d1344d73a2e8dae763707b7977dac130cdc028a4ca8d6959a: struct<digest: string, mediaType: string, size: int64>, sha256:ed65183a4b4095c6e03693f7394075fe244194c935d3bb20f1370a7a3669e7eb: struct<digest: string, mediaType: string, size: int64>>, Layers: list<item: string>, RepoTags: list<item: string>>>
              
              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.

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 (HEAD c05638d70 after cherry-pick) — SM12x DSV4 support + experimental superset (split-KV decode, GB10 fused-MoE config aliases, tuned MLA graph defaults)
  • Cherry-pick f910a73a93 from neuralmagic/vllm@kylesayrs/deepseek-ct (vLLM PR #41276 — DSV4 compressed-tensors attention)
  • Local patch patch_v4_packed_mapping.py — adds packed_modules_mapping to DeepseekV4ForCausalLM
  • 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

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