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Avdpro/DeepSeek-V4.1-Flash-SSD
DeepSeek-V4.1-Flash-SSD is a machine learning model from Avdpro. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
This is a byte-preserving storage-layout conversion of deepseek-ai/DeepSeek-V4.1-Flash at immutable revision dba1be0a40aa45a94ad051997016db3960a90277. Original tensor values and quantization are retained. Original rou…
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How the weights are stored.
F8_E4M3204B · 93%
From the Hugging Face model README
This is a byte-preserving storage-layout conversion of
deepseek-ai/DeepSeek-V4.1-Flash at immutable revision dba1be0a40aa45a94ad051997016db3960a90277.
Original tensor values and quantization are retained. Original routed experts
are externalized into the experts/ directory rather than duplicated in the
backbone safetensors.
Requires an AI2Apps Runtime with explicit dsv41-original-fp4-six-segment-v1 support.
This candidate is not a drop-in checkpoint for unmodified Transformers,
mlx-lm or mlx-vlm. Do not use the backbone safetensors alone.
The corresponding Runtime and model Package have not yet completed release
acceptance. Full/Cached engine compatibility is recorded separately in the
Runtime release receipt; the presence of a reversible tensor map alone is
not an end-to-end engine guarantee.
ssd-checkpoint.json: format, provenance and file digests.external-tensors.json: original tensor names and external byte locations.source-tensor-sha256.json: original tensor payload digests verified during export.model.safetensors.index.json: ordinary/vision/other retained tensor index.experts/: complete routed expert payloads, with no re-quantization.See LICENSE and README.upstream.md for upstream terms and attribution.
This storage format changes installation space and data access; it is not a
new model training or a claim of improved model accuracy.