Add SparkLab model card

Document provenance, native MXFP4/MXFP8 execution, DSpark-5 benchmark results, and the validated DGX Spark command.

README.md

@@ -0,0 +1,109 @@
+---
+pipeline_tag: text-generation
+base_model: deepseek-ai/DeepSeek-V4.1-Flash
+license: mit
+library_name: sparklab
+tags:
+- deepseek-v4.1
+- mxfp4
+- mxfp8
+- sparklab
+- ftw
+- speculative-decoding
+---
+
+# DeepSeek V4.1 Flash — SparkLab disk-ready checkpoint
+
+This repository is a byte-preserving mirror of
+[`deepseek-ai/DeepSeek-V4.1-Flash`](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash)
+at revision `df42c109f1defefcbfcedbe7d905718a12266e40`, published for the
+[SparkLab](https://github.com/sixteen-miles-labs/sparklab) native NVIDIA DGX Spark
+research path.
+
+The tensors retain DeepSeek's original mixed checkpoint representation: routed experts
+are MXFP4, dense projections are MXFP8 with UE8M0 scales, and embeddings and the output
+head are BF16. No training, tensor conversion, or additional quantization was performed.
+Although the repository uses the `FTW` deployment label, DeepSeek V4.1 currently runs
+through SparkLab's model-owned safetensors reader and packed expert cache rather than the
+generic FTW container format.
+
+## Validated scope
+
+SparkLab currently supports text-only, TP=1, batch-one eager execution with a 2,048-token
+total context on one 128 GB NVIDIA GB10. The model owns a 64 GiB packed expert LRU and a
+bounded 24 GiB weight cache, reading the remaining checkpoint from local NVMe.
+
+The default target-only profile measured 0.969 decode tokens/s and 74.387 seconds warm
+TTFT on a fixed 74-input/128-output greedy probe. Opt-in native greedy DSpark-5 measured
+1.053 decode tokens/s and 76.329 seconds warm TTFT, an 8.6% decode improvement. Both
+DSpark trials reproduced the target-only output hash. These are narrow performance
+measurements, not general quality, concurrency, long-context, or endurance certification.
+
+The upstream vLLM recipe uses probabilistic draft sampling, block rejection, and adaptive
+verification. SparkLab's current V4.1 path implements greedy drafting and exact
+accepted-prefix state commits; probabilistic sampling and adaptive verification remain
+out of scope.
+
+## Download and run
+
+The repository is approximately 480 GiB. Download it to local NVMe:
+
+```bash
+hf download oakmindai/DeepSeek-V4.1-Flash-FTW \
+  --local-dir ~/models/DeepSeek-V4.1-Flash-FTW
+```
+
+Install SparkLab from source, then start the target-only server:
+
+```bash
+git clone https://github.com/sixteen-miles-labs/sparklab.git
+cd sparklab
+./install.sh
+
+SPARKLAB_DSV41_EXPERT_CACHE_GB=64 sparklab serve \
+  --model ~/models/DeepSeek-V4.1-Flash-FTW \
+  --dtype bfloat16 \
+  --max-running-requests 1 \
+  --max-seq-len-override 2048 \
+  --num-tokens 2048 \
+  --attention-backend triton \
+  --cache-type naive \
+  --moe-backend fused \
+  --cuda-graph-max-bs 0 \
+  --disable-startup-prefill-warmup \
+  --port 8000
+```
+
+Enable the measured DSpark profile by adding:
+
+```bash
+--speculative-method dspark \
+--speculative-tokens 5 \
+--draft-sample-method greedy
+```
+
+Send a request:
+
+```bash
+curl http://127.0.0.1:8000/v1/chat/completions \
+  -H 'Content-Type: application/json' \
+  -d '{
+    "model": "DeepSeek-V4.1-Flash-FTW",
+    "messages": [{"role": "user", "content": "What is 17*19?"}],
+    "temperature": 0,
+    "max_tokens": 32
+  }'
+```
+
+## Provenance and license
+
+DeepSeek AI developed and released the architecture, code, tokenizer, and weights. This
+mirror preserves the upstream MIT license and source files. SparkLab supplies the native
+runtime, direct packed MXFP4/MXFP8 kernels, bounded disk-backed execution, DSpark
+integration, validation, and model recipe. Oakmind AI publishes this mirror.
+
+- Upstream model: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash
+- Exact upstream revision: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/tree/df42c109f1defefcbfcedbe7d905718a12266e40
+- SparkLab: https://github.com/sixteen-miles-labs/sparklab
+- vLLM architecture recipe: https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4.1-Flash
+