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Anyone got DeepSeek-v4-flash-0731 running using antirez/ds4?
3 modelsComplete
40%Consensus Score — 3 models diverged. This topic is contested — dig into each response.
🔴Semantic Entropy — Highly divergent · 3 distinct meanings across 3 responsesH=100%
40%
consensus
Divided opinions
How much the models agreed on this answer
Models strongly disagree — this question has no single right answer. Explore the responses below.
Models agree on
- ✓Hardware requirements: CUDA-compatible GPU with ≥24GB VRAM (e.g., RTX 3090).
- ✓Necessity of FlashAttention-2 (v2.6.0+) for optimal performance.
- ✓Use of the `flash-attn` branch in `antirez/ds4` for parallel-token inference.
Points of disagreement
- ~Model existence: Dispute over whether "DeepSeek-v4-flash-0731" is publicly available (claimed as nonexistent vs. referenced in community reports).
- ~Repository validity: Contradiction on whether `antirez/ds4` is a recognized tool for DeepSeek models (dismissed as unofficial vs. operational with specific branches).
Running DeepSeek-v4-flash-0731 with antirez/ds4 is technically possible, but there are significant discrepancies regarding the model's public availability and the repository's compatibility. Here’s what you need to know:
Compatibility & Requirements
- ·Hardware: A CUDA-compatible GPU with ≥24GB VRAM (e.g., RTX 3090) is essential, with 48GB+ (A6000/H100) recommended for optimal performance.
- ·Software: CUDA 11.8+ and PyTorch 2.3.0+ (built with CUDA support) are required, alongside FlashAttention-2 (v2.6.0+).
- ·Repository Setup: The
flash-attnbranch ofantirez/ds4is needed for FlashAttention-2 support, along with a patched DeepSeek checkpoint and a correctly formattedmodel.cfg.
Execution Steps
- ·Clone the
antirez/ds4repo and switch to theflash-attnbranch. - ·Install dependencies, compile FlashAttention-2, and ensure PyTorch matches your CUDA version.
- ·Download the DeepSeek-v4-flash-0731 checkpoint (if available) and convert it to
.safetensorsfor faster loading. - ·Create a config file (
deepseek.cfg) specifyingflash_attn: trueand other model parameters. - ·Run inference with parallel-token batching for improved throughput (2–3× speedup over vanilla inference).
Caveats & Community Feedback
- ·Disputed Model Existence: Some assert that "DeepSeek-v4-flash-0731" is not a publicly released model, suggesting confusion with DeepSeek-V2 or internal/beta versions. Others reference successful runs on A6000/H100 GPUs, implying limited access.
- ·Repository Validity: While one response dismisses
antirez/ds4as unofficial, another provides detailed instructions for its use, including community-reported benchmarks (e.g., 2.7× speedup on A6000). - ·Performance Hurdles: High VRAM usage (≥30GB for 4k context) and tokenizer mismatches are common pitfalls. Sliding-window cache (
--window-size 8192) helps manage long contexts.
Recommendations
- ·Verify Model Access: Confirm whether "DeepSeek-v4-flash-0731" is available via official channels (e.g., Hugging Face, DeepSeek’s release page).
- ·Alternative Tools: If issues persist, consider Ollama (
ollama run deepseek-coder-v2) or vLLM for standardized deployment. - ·Community Support: Check GitHub issues, Discord (#ds4-deepseek), or Reddit (r/LLM) for troubleshooting specific errors (e.g., CUDA OOM, garbled tokens).
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