Model Gallery

Discover and install AI models from our curated collection

11 models available
1 repositories
Documentation

Find Your Perfect Model

Filter by Model Type

Browse by Tags

deepseek-v4-flash-0731
# DeepSeek-V4-Flash-0731 Technical Report👁️ ## Introduction **DeepSeek-V4-Flash-0731** is the official release of **DeepSeek-V4-Flash**, superseding the preview version, with substantially enhanced agentic capabilities. It has the same model structure as DeepSeek-V4-Flash-DSpark, i.e. it comes with a speculative decoding module attached. DeepSeek-V4-Flash-0731 outperforms DeepSeek-V4-Pro (Preview) on benchmarks listed below despite its far smaller activated parameter count, and is broadly competitive with the strongest proprietary models available. Notes: 1. For the Code Agent tasks among the public benchmarks above, DeepSeek-V4-Flash-0731 is evaluated with the minimal mode of DeepSeek Harness (to be released) as the agent framework, using the `max` reasoning effort level with `temperature = 1.0, top_p = 0.95`. 2. † DSBench-FullStack is an internal full-stack development test set; DSBench-Hard is an internal test set of difficult coding-agent problems. ## Chat Template ...

Repository: localaiLicense: mit

deepseek-v4-pro-0813
DeepSeek V4 Pro 0813 is DeepSeek's MIT-licensed flagship mixture-of-experts model for agentic coding, reasoning, and long-horizon tool use. This entry uses Unsloth's UD-Q4_K_XL GGUF build, split into 20 shards for llama.cpp.

Repository: localaiLicense: mit

deepseek-v4-flash
# DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence Technical Report👁️ ## Introduction We present a preview version of **DeepSeek-V4** series, including two strong Mixture-of-Experts (MoE) language models — **DeepSeek-V4-Pro** with 1.6T parameters (49B activated) and **DeepSeek-V4-Flash** with 284B parameters (13B activated) — both supporting a context length of **one million tokens**. DeepSeek-V4 series incorporate several key upgrades in architecture and optimization: 1. **Hybrid Attention Architecture:** We design a hybrid attention mechanism combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) to dramatically improve long-context efficiency. In the 1M-token context setting, DeepSeek-V4-Pro requires only **27% of single-token inference FLOPs** and **10% of KV cache** compared with DeepSeek-V3.2. 2. **Manifold-Constrained Hyper-Connections (mHC):** We incorporate mHC to strengthen conventional residual connections, enhancing stability of signal propagation across layers while preserving model expressivity. 3. **Muon Optimizer:** We employ the Muon optimizer for faster convergence and greater training stability. ...

Repository: localaiLicense: mit

qwen3.5-9b-deepseek-v4-flash
# Qwen3.5-9B [](https://chat.qwen.ai) > [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. ## Qwen3.5 Highlights Qwen3.5 features the following enhancement: - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks. - **Efficient Hybrid Architecture**: Gated Delta Networks combined with sparse Mixture-of-Experts deliver high-throughput inference with minimal latency and cost overhead. ...

Repository: localaiLicense: apache-2.0

deepseek-v4-flash-spark-exl3-vllm-cpp
DeepSeek V4 Flash's Spark and GB10-oriented REAP-K216 EXL3 checkpoint, served by vllm.cpp. It needs CUDA and roughly 100 GiB for its large rank-sliced checkpoint. The repository is pinned to its latest recorded revision. vllm.cpp's existing runtime evidence measured the older 22f28d32b9b29b4352eaa380ff8c2c170b2847ab revision; this entry does not claim that the newer revision has passed the same end-to-end gate.

Repository: localai

deepseek-v4-flash-exl3-3bpw-vllm-cpp
Experimental non-Spark DeepSeek V4 Flash EXL3 3.0bpw checkpoint served by vllm.cpp. This is the complete, non-REAP layout and requires a large multi-GPU CUDA system. The publisher describes the artifact as structurally complete but has not passed end-to-end generation. Treat this entry as an integration target, not as a correctness- or performance-gated configuration.

Repository: localai

deepseek-v4-flash-q2
DeepSeek V4 Flash (IQ2XXS GGUF, ~81 GB) - only loadable via the ds4 backend. Requires >=128 GB RAM. Metal (Darwin) or CUDA (Linux). See https://github.com/antirez/ds4 for details.

Repository: localai

deepseek-v4-flash-q2-q4
DeepSeek V4 Flash (mixed q2/q4 GGUF, ~91 GB) - only loadable via the ds4 backend. The last 6 expert layers are kept at Q4_K (the rest IQ2XXS), trading a little extra memory for higher quality than the pure-q2 build while still fitting in RAM on a 128 GB machine. imatrix-tuned. Metal (Darwin) or CUDA (Linux). See https://github.com/antirez/ds4 for details.

Repository: localai

deepseek-v4-flash-q4-ssd
DeepSeek V4 Flash (full 4-bit experts GGUF, ~153 GB) - only loadable via the ds4 backend, with SSD streaming enabled so it runs on a 128 GB machine even though the weights do not fit in RAM: routed MoE experts stream from the GGUF on SSD while the non-routed weights stay resident. SSD streaming is Metal (Darwin) only; generation speed depends on SSD speed and the expert cache. Tune the routed-expert cache with the 'ssd_streaming_cache_experts:NGB' option (default: automatic budget). See https://github.com/antirez/ds4.

Repository: localai

deepseek-v4-flash-q2-mtp
DeepSeek V4 Flash (IQ2XXS GGUF, ~81 GB) paired with the optional MTP speculative-decoding weights (~3.5 GB) for a slight speedup. Only loadable via the ds4 backend; requires >=128 GB RAM. MTP helps only with greedy decoding (temperature 0), so the override pins temperature to 0. Metal (Darwin) or CUDA (Linux). See https://github.com/antirez/ds4 for details.

Repository: localai

deepseek-v4-pro-q2-ssd
DeepSeek V4 Pro (IQ2XXS GGUF, ~433 GB, imatrix-tuned) - only loadable via the ds4 backend, with SSD streaming so the Pro-class model can be run on a 128 GB machine. This is experimental and slow: it needs ~433 GB of free SSD plus enough RAM for the resident weights, KV cache, and routed-expert cache, and is best used with thinking off for inspection or occasional work. SSD streaming is Metal (Darwin) only. See https://github.com/antirez/ds4.

Repository: localai