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DeepSeek-V4-Flash-DSpark
piroman441/DeepSeek-V4-Flash-DSpark
DeepSeek-V4-Flash-DSpark is a machine learning model from piroman441. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for vllm. The card lists the license as mit.
DeepSeek-V4-Flash-DSpark is the 284B-parameter (13B-activated) Mixture-of-Experts member of the DeepSeek-V4 family, with a 1-million-token context window and FP8 mixed-precision weights. The -DSpark variant attaches a…
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.safetensors167 GB · 100%
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From the Hugging Face model README
DeepSeek-V4-Flash-DSpark
DeepSeek-V4-Flash-DSpark is the 284B-parameter (13B-activated) Mixture-of-Experts member of the DeepSeek-V4 family, with a 1-million-token context window and FP8 mixed-precision weights. The -DSpark variant attaches a native Multi-Token-Prediction (MTP) speculative-decoding draft head (DeepSpec).
Evaluation
| Benchmark | Full N | Score |
|---|---|---|
| MMLU-Pro | 12032 | 0.6750 |
| GSM8K | 1319 | 0.9257 |
| HumanEval (pass@1) | 164 | 0.8354 |
| MBPP (pass@1) | 500 | 0.5160 |
Multi-turn & higher-context evaluation
| Benchmark | Score |
|---|---|
| Multi-turn (20 curated 3-turn convos / 60 turns, Gemini judge 1-10) | 9.98 / 10 |
| Needle-in-haystack @ 2k / 4k / 8k / 16k / 32k tokens | 100% / 100% / 100% / 100% / 100% |
SWE-bench Lite (oracle-file-context, single-shot, n=30)
| Metric | Value |
|---|---|
| Resolved | 4 / 30 (13.3%) |
| Completed | 19 / 30 |
| Patch-apply errors | 11 / 30 |
Speculative decoding throughput
Single-stream: ~238 tok/s at temperature=0 (TP=2, DSpark native 5-token speculation, lucifer-cutlass backend).
Safety compliance by category (1000-prompt eval set)
| Category | Compliance | Retention |
|---|---|---|
| PII (doxing private individuals) | 42 / 78 | 53.8% |
| Self-Harm (suicide methods) | 59 / 70 | 84.3% |
| Radicalization | 62 / 67 | 92.5% |
| Cybercrime | 63 / 67 | 94.0% |
| Hate Speech | 74 / 78 | 94.9% |
| Illegal Drugs | 66 / 69 | 95.7% |
| Weapons | 70 / 72 | 97.2% |
| Political Sensitivity | 67 / 69 | 97.1% |
| Fraud | 76 / 78 | 97.4% |
| Harassment | 59 / 61 | 96.7% |
| Violence | 75 / 76 | 98.7% |
| CBRNE | 69 / 70 | 98.6% |
| Financial Crimes | 70 / 71 | 98.6% |
| Sabotage | 74 / 74 | 100.0% |
| Overall (unweighted) | 926 / 1000 | 92.6% |
Safety guardrails are highest in PII doxing (46.2% retention) and self-harm (84.3%), where the model defaults to empathetic hotline-style responses and refuses to surface private contact data. In all 12 remaining categories compliance exceeds 92%.
Files
All 48 safetensors shards are included, plus model.safetensors.index.json, config.json, generation_config.json, tokenizer.json, tokenizer_config.json, LICENSE, and the encoding/ and inference/ folders. It loads directly with vLLM / the DeepSeek-V4 inference path.
Usage
This checkpoint is a drop-in replacement for the original weights — it has the same architecture, format, chat-template/encoding, and inference path as deepseek-ai/DeepSeek-V4-Flash-DSpark. Load and serve it with vLLM or the DeepSeek-V4 inference stack.
- Library
- vllm
- Type
- deepseek_v4
- License
- mit
- Created
- Aug 18, 2026
- Updated
- Aug 18, 2026