GGUF-format, DS4-optimized quantized weights for DeepSeek-V4-Flash, offering q2 (≈80.8 GiB) and q4 (≈153.3 GiB) variants plus an optional small MTP file for speculative decoding. Built for the DS4 inference engine; MIT-licensed.
Provides step-by-step guides to integrate DeepSeek V4 models (deepseek-v4-pro and deepseek-v4-flash) into 22 popular AI agents and coding-assistant tools. Each entry shows installation, configuration, and first-run steps for tools like Claude Code, Qwen Code, Codex, Cline, Deep Code, and more.
Terminal-native AI coding assistant optimized for the deepseek-v4 model. Provides configurable "thinking" modes and reasoning-intensity controls, agent skills for extensibility, MCP integration, and a shared config with a VSCode plugin.
Contains ~1,973 distilled roleplay conversations with character-perspective chain-of-thought traces (<think> blocks) for fine-tuning persona-focused chat models. Includes teacher provenance, safety/review flags, and filters for NSFW/borderline samples — suited for SFT and character retention tests.
Distills DeepSeek‑V4's multi-step structured reasoning into a Qwen3.5‑9B model for fast image-text-to-text reasoning and agentic tool workflows. Trades larger teacher size for inference efficiency and improved procedural reasoning — good for low-latency research, evaluation, and agent integration.
Early-preview (≈1.2k rows) dataset of agentic coding prompts and unedited model responses generated by DeepSeek‑V4‑Pro, covering real-world programming tasks across many languages. Intended for research, filtering, and model evaluation rather than production training without review.
Provides the dataset and accompanying technical report for a DeepSeek project that interleaves spatial markers (points and boxes) into multimodal LLM reasoning. Includes a public subset of data and benchmarks under an MIT license; model weights are not included.
Provides 545,431 math problems with model-generated solution traces (chain-of-thought and Python tool-integrated reasoning) verified against reference answers for training and evaluating LLM mathematical reasoning. Parquet-format dataset; DeepSeek‑V4‑Pro generated traces and mixed CC BY / CC BY‑SA licensing.
Provides 19,331 multi-turn ChatML Hermes reasoning traces produced by DeepSeek V4 Pro for LoRA fine-tuning of agent-style models; includes VRAM-tiered variants, train/valid/test splits, and dense tool-calling annotations in Parquet format.
Provides 19,331 multi-turn ChatML Hermes reasoning traces for LoRA fine-tuning of local models to behave as Hermes agents. Includes train/valid/test splits, VRAM-tiered variants (nano→spark), ~138K tool-call annotations, and Parquet format under Apache-2.0.
Native local inference engine for DeepSeek V4 Flash (also supports GLM 5.2 and PRO on high‑memory machines). Focused features include model-specific loading, SSD expert streaming, asymmetric routed-expert 2-bit quant support, multi-GPU/tensor/pipeline parallelism, and an OpenAI-compatible server plus a native coding agent.
Agentic coding evaluation dataset containing real-world, multi-step developer tasks and raw model responses across 20+ programming languages. Emphasizes challenging, persona-driven prompts for benchmarking and fine-tuning; users should filter and audit outputs before training.