deepseek-v4-flash-dspark-nvfp4-shape-mock

DeepSeek-V4 Flash DSpark NVFP4 shape mock

Random integration fixture — not a useful language model. The tensor values are deterministic random data or benign constants. Do not use this checkpoint for quality, safety, or performance evaluation.

This compact checkpoint reproduces the production tensor schemas needed to exercise DeepSeek-V4 Flash integration without downloading or loading the full model. It is intended for vLLM, FlashInfer B12X, sparse-attention, and DSpark loader/kernel testing on Blackwell systems such as DGX Spark / GB10 (SM121).

What is represented

• Three production-shape target transformer/MoE blocks (layers.0 through layers.2). Three is the minimum compact layout that retains the first real sparse MLA compressor/indexer layer.
• All three production-shape DSpark/MTP draft stages (mtp.0 through mtp.2).
• Production MoE dimensions: hidden size 4096, expert intermediate size 2048, 256 routed experts, top-k 6.
• ModelOpt NVFP4 routed target experts with group size 16. Attention, shared experts, and MTP retain the source mixed-precision schemas.
• hidden_act="silu" and swiglu_limit=10.0.
• DSpark configuration: target layers [0, 1, 2], Markov rank 256, block size 5, and three draft stages.
• Sparse-attention compression ratios [0, 0, 4, 0, 0, 0]; layer 2 includes the real compressor and Lightning Indexer tensor layouts.

The checkpoint contains 14,025 tensors and 24,313,583,928 bytes of tensor data (22.65 GiB).

Schema validation

The generator pins sakamakismile/DeepSeek-V4-Flash-0731-Abliterated-NVFP4 at revision 4b15c44c31f8dacdf7b0c2fa42e20d03c5f5859b for the DSpark-inclusive schema. Header-only validation showed:

• all 14,025 local tensor names, shapes, and dtypes exactly match the selected source target and MTP tensors;
• the 9,320 non-MTP tensors also exactly match nvidia/DeepSeek-V4-Flash-NVFP4 revision e3cd60e7de98e9867116860d522499a728de1cf9 for global tensors and target layers 0–2;
• layer 2 contains 8 compressor tensors and 7 indexer tensors;
• MTP stage tensor counts are 1,568, 1,565, and 1,572.

tensor_manifest.json records every expected header, and generate_shape_mock.py reproduces the fixture.

GB10 / vLLM nightly validation

Test host: NVIDIA GB10, compute capability 12.1. The cached vllm/vllm-openai:nightly image was started with:

--moe-backend flashinfer_b12x
--linear-backend cutlass
--kv-cache-dtype fp8
--speculative-config '{"method":"dspark","num_speculative_tokens":5,"draft_sample_method":"probabilistic"}'

The unmodified nightly correctly reproduced its current integration guard:

Model sets swiglu_limit=10.0, but the explicitly requested
moe_backend='flashinfer_b12x' does not apply the SwiGLU clamp.

With a clamp-capable FlashInfer B12X checkout mounted and the stale vLLM capability guard bypassed only inside the disposable test container, vLLM:

• selected FLASHINFER_B12X for the target NVFP4 experts;
• initialized the FP8 Lightning Indexer cache;
• loaded all eight checkpoint shards;
• loaded the DSpark draft model (DSpark draft model loaded: 96 params).

The stock nightly did not complete a forward pass on GB10 because its MHC DeepGEMM path later failed with Unsupported architecture, before reaching the MoE forward. That limitation is independent of this checkpoint's B12X clamp schema and is recorded here to avoid overstating end-to-end validation.

Intended use

Use this checkpoint to reproduce loader selection, tensor remapping, memory planning, and kernel integration behavior. Since the weights are random, any generated tokens are meaningless even when a runtime completes inference.