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AI Models 84759

  • Darwin-28B-REASON↗

    FINAL-Bench

  • POCKET-35B-GGUF↗

    FINAL-Bench

    > ### ⭐ Latest and flagship models > [](https://huggingface.co/FINAL-Bench/VKORE-46M-NPU) [](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5) > [](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6) [](https://huggingface.co/collections/FINAL-Bench/darwin-family-699987b1f652864af0122193) [](https://huggingface.co/FINAL-Bench) [](https://vidraft.net) [](https://thevc.kr/vidraft) > ### 🆕 **POCKET-Qwen3.8-Flash-Next** — a **180B** model running on a **laptop with 8 GB VRAM + 32 GB RAM** · **4.17 tok/s measured**. > [](https://huggingface.co/FINAL-Bench/POCKET-Qwen3.8-Flash-Next-GGUF) []() []() []() > ### 🆕 **POCKET-Zimage-CPU** — photoreal **images** in **46 s** on a **CPU only**. No GPU, no CUDA, no Python. > [](https://huggingface.co/FINAL-Bench/POCKET-Zimage-CPU) [](https://huggingface.co/spaces/FINAL-Bench/POCKET-Zimage-CPU) []()

  • flan-t5-large↗

    Google

    0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation 9. Model Card Authors If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.

  • paligemma-3b-ft-infovqa-224↗

    Google

  • Llama-3.3-70B-Instruct-FP8-Dynamic↗

    Infermatic

  • Qwen3.8-35B-A3B-Distill-MTP-APEX-I-MiniPlus-V2.1-GGUF↗

    IsValorum

    If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies. > > *Independently computed on private cloud clusters. If this handcrafted release saves you VRAM and runs faster on your GPU, consider fueling the community compute fund on Ko-fi.* > > Explore the new canonical V3 releases. MiniPlus prioritizes Q5-class to near-Q6 fidelity without a fixed file-size ceiling; NanoPlus targets Q4-class quality at a practical footprint. > > - **Qwen3.8 27B — MiniPlus V3** — 14.16 GB **(+13.21% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Qwen3.8 27B — NanoPlus V3** — 11.35 GB **(+11.49% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — MiniPlus V3** — 14.28 GB **(+5.70% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > - **Swift-1.5 Qwen3.8 27B Uncensored MTP — NanoPlus V3** — 11.08 GB **(+2.26% improvement in WikiText-2 PPL vs GSQ-RCO IQ3_S)**. > > Percentages compare the reported WikiText-2 PPL with GSQ-RCO IQ3_S (7.07). The linked 27B cards document the evaluation protocol and model variants. > > **Explore the APEX-I MiniPlus V2.1 collection** > > ### EXPLORE THE ESTABLISHED 35B MoE MINIPLUS LINEUP > These are complementary APEX-I-MiniPlus V2.1 releases, not alternate downloads of the same model. Each receives the same tensor-by-tensor approach, integrated MTP where supported, and a design suitable for full or partial system-RAM inference. Choose the model whose native strengths best fit the work you want to do: > > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1** — a versatile frontier MoE for broad reasoning, multilingual work, agents, tool use, and multimodal tasks. > - **Best for:** General reasoning, agent workflows, tool calling, and flexible multimodal use. > - **Qwen3.6-35B-A3B MTP APEX-I-MiniPlus-V2.1 Abliterated** — the V2.1 refusal-ablated Qwen3.6 edition for users who deliberately prefer reduced refusal behavior. > - **Best for:** Workflows where an abliterated Qwen3.6 variant is explicitly desired. > - **Ornith 1.5 APEX-I-MiniP

  • DeepSeek-V4.1-Flash-DS4-IQ2↗

    ivanfioravanti

  • Hermes3.6-35B-A3B-Uncensored-Genesis-NVFP4-GGUF↗

    jan1k

  • qwen3-8b-heretic↗

    kalpakjian

  • Qwen3.5-2B-Hermes↗

    keithlol