DeepSeek-V4-Flash
deepseek-v4-flash-87628c9d·4 events·first seen 28d agoAliases: DeepSeek-V4-Flash, DeepSeek-V4-Flash-Base
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DeepSeek releases DeepSeek-V4-Flash on Hugging Face
DeepSeek has released DeepSeek-V4-Flash, a new text-generation model published on Hugging Face under the deepseek-ai organization. The model supports FP8 and 8-bit quantization and is tagged as conversational and endpoints-compatible. With over 2.8 million downloads and 1,455 likes, it has seen substantial early uptake.
DeepSeek releases DeepSeek-V4-Flash-Base on Hugging Face
DeepSeek has released DeepSeek-V4-Flash-Base, a new open-weights base model, on Hugging Face. The model uses FP8 precision and the deepseek_v4 architecture with safetensors format. Early traction is notable with over 66,000 downloads and 241 likes shortly after release, suggesting significant community interest in a 'Flash' variant of the V4 series.
DeepSeek V4 Preview Release: 1.6T-param Pro and 284B Flash Models with 1M Context, Open-Sourced
DeepSeek has released DeepSeek-V4 as an open-weights preview, comprising two MoE variants: V4-Pro (1.6T total / 49B active parameters) and V4-Flash (284B total / 13B active parameters). Both models support 1M token context by default, enabled by a novel Token-wise compression and DeepSeek Sparse Attention (DSA) architecture. V4-Pro claims open-source SOTA on agentic coding benchmarks and world-class math/STEM/coding performance rivaling top closed-source models, while V4-Flash offers near-parity reasoning at lower cost and latency. The API is live today with OpenAI and Anthropic compatibility, and legacy model endpoints will be retired in July 2026.
Goedel-Architect achieves state-of-the-art formal theorem proving with blueprint-based agentic framework
Goedel-Architect is an agentic framework for formal theorem proving in Lean 4 that uses blueprint generation — a dependency graph of definitions and lemmas — rather than recursive decomposition, enabling parallel lemma closure and global refinement. Built on DeepSeek-V4-Flash (284B-A13B), it achieves 99.2% pass@1 on MiniF2F-test and 75.6% on PutnamBench, scaling to 100% on MiniF2F, 88.8% on PutnamBench, and 4/6 on IMO 2025 when seeded with natural-language proofs. The authors claim state-of-the-art performance for an open-source pipeline at up to 500x lower cost than comparable systems.