全站搜索
AI 模型 84755
- gpt-oss-tiny-random-mxfp4↗
yujiepan
- GLM-4.6V-Flash↗
zai-org
This model is part of the GLM-V family of models, introduced in the paper GLM-4.1V-Thinking and GLM-4.5V: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning. - **GLM-4.6V Blog**: https://z.ai/blog/glm-4.6v - **Paper**: https://huggingface.co/papers/2507.01006 - **GitHub Repository**: https://github.com/zai-org/GLM-V - **Online Demo**: https://chat.z.ai/ - **API Access**: Z.ai Open Platform - **Desktop Assistant App**: https://huggingface.co/spaces/zai-org/GLM-4.5V-Demo-App GLM-4.6V series model includes two versions: GLM-4.6V (106B), a foundation model designed for cloud and high-performance cluster scenarios, and GLM-4.6V-Flash (9B), a lightweight model optimized for local deployment and low-latency applications. GLM-4.6V scales its context window to 128k tokens in training, and achieves SoTA performance in visual understanding among models of similar parameter scales. Crucially, we integrate native Function Calling capabilities for the first time. This effectively bridges the gap between "visual perception" and "executable action" providing a unified technical foundation for multimodal agents in real-world business scenarios.
- Qwen2.5 72B Instruct↗
阿里巴巴 · Qwen
Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...
- qwen3-0.6b↗
阿里巴巴 · Qwen
Qwen3 是 Qwen 系列最新一代大语言模型，提供全面的稠密模型和混合专家（MoE）模型套件。基于大规模训练，Qwen3 在推理、指令遵循、智能体能力和多语言支持方面实现了突破性进展，具有以下主要特性：**Qwen3-0.6B** 具有以下特性：- 类型：因果语言模型 - 训练阶段：预训练与后训练 - 参数量：0.6B - 非嵌入参数量：0.44B - 层数：28 - 注意力头数（GQA）：Q 为 16，KV 为 8 - 上下文长度：32,768 如需了解更多详情，包括基准评测、硬件要求和推理性能，请参阅我们的博客、GitHub 和文档。
- GLM5.3-Flash-E224-DGX-Spark↗
autotrust
> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **autotrust/GLM5.3-Flash-E224-DGX-Spark is a compact build of zai-org/GLM-5.3-Flash for desktop Blackwell systems like NVIDIA DGX Spark. It's an unofficial derivative.** > 🚀 **Deploying on DGX Spark?** A complete, field-tested runbook for > serving this checkpoint on **two ConnectX-7-linked DGX Sparks** > (vLLM 0.31.0, TP=2, fp8 KV cache) is here: > **DEPLOY-2X-DGX-SPARK.md** — network setup, > NCCL/CX7 pitfalls, memory budgeting, ops & troubleshooting, and > measured throughput (15.7 tok/s single-stream, 71.8 tok/s at 8-way > concurrency, both GPUs ≈96% utilized).
- JEV-9B↗
autotrust
> [!IMPORTANT] > **Try AutoTrust/GuruSearch live (new, 11 October 2026).** A recommendation and search demo in which every ranking is a > calibrated System 1 decision, made in about half a second: > * **Recommendation:** the latest headlines from 6 news feeds, ranked by importance. > * **Search:** results from Google News, Bing News and Yahoo News re-ranked by relevance, side by side with the > search engines' own order, plus a short answer with citations. **Fast, calibrated System 1 decisions that are indistinguishable from the closed TypeSafe Jev 1.13 by KL, and deliberate System 2 generation and reasoning from an untouched Qwen3.5-9B — one set of weights, one vLLM engine, routed per request. The fastest model of the family: it answers a single decision in about a third of the time the hosted API takes. Its successor, autotrust/JEV-27B, is closer to Jev, transfers better to unseen tasks and has a stronger System 2.** JEV-9B now takes images. Every step below is **one System 1 decision**: camera image or screenshot in, a probability for every action out, in a single forward pass (about 0.2 s on one GPU). Run it with `bash vl/serve.sh` (see Images: quick start).
- ozone-research_0x-lite-GGUF↗
bartowski
- Sailor2-1B-Chat-GGUF↗
bartowski
- Qwen3-4b-Z-Image-Engineer-V4↗
BennyDaBall
V4 still rocks as the original Qwen3 prompt-engineer build. The new V6 release is the native Z-Image Turbo text-encoder build, rebuilt with SMART DoRA and trained locally for ComfyUI drop-in use plus LM Studio prompt enhancement. This is Z-Engineer V4, the culmination of extensive research into what makes an AI prompt engineer actually *good* at its job. Built on the Qwen 3 architecture and trained using a novel **SMART Training** methodology, this 4B parameter model doesn't just describe scenes—it understands the craft of visual storytelling down to the lens flare. Z-Engineer V4 is a fully fine-tuned (not LoRA, we went *all in*) version of the text encoder from Tongyi-MAI/Z-Image-Turbo. It's been specifically trained to understand the nuances of AI Image Generation workflows.
- rf-r16-sp03↗
bmars8218