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AI モデル 84757

• progen2-large↗

  hugohrban

• Ring-mini-sparse-2.0-exp↗

  inclusionAI

• Qwen3.8-Flash-Next-NVFP4↗

  Inferact

  > [!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, TokenSpeed, etc. > [!Tip] > For users seeking managed, scalable inference without infrastructure maintenance, the official Qwen API service is provided by Qwen Cloud. > > In particular, **Qwen3.8-Flash** is the official version based on Qwen3.8-Flash-Next with more production features, e.g., 1M context length by default, official built-in tools. For more information, please refer to the Qwen3.8-Flash Overview. As the frontier of foundation models pushes toward ever-larger parameter counts and ever-longer context windows, the question is no longer just how much we can scale, but how efficiently we can do so. Sustainable progress toward artificial general intelligence (AGI) that benefits everyone demands architectural innovation. Today, we are sharing a concrete step in that direction: Qwen3.8-Flash-Next.

• DeepSeek-OCR-2-GGUF↗

  JamePeng2023

• Jan-v3.5-4B-gguf↗

  janhq

  **Jan-v3.5-4B** is a fine-tuned variant of Jan-v3-4B-base-instruct, specialized on math reasoning and identity datasets. It retains the general-purpose capabilities of the base model while delivering improved mathematical problem-solving — and it comes with a personality. Unlike generic assistants, Jan-v3.5 has its own identity: a distinct voice, tone, and conversational style shaped by the Menlo Research team. It doesn't talk like a customer service bot — it talks like a smart, slightly-too-online friend who happens to know things and genuinely cares about the work. Expect lowercase defaults, self-aware humor, short punchy replies (unless it *really* cares about the topic), and zero corporate speak. - **Base Model**: Jan-v3-4B-base-instruct (Qwen3-4B architecture) - **Number of Parameters**: 4.0B - **Number of Parameters (Non-Embedding)**: 3.6B - **Number of Layers**: 36 - **Number of Attention Heads (GQA)**: 32 for Q and 8 for KV - **Context Length**: 262,144 natively

• skops-digits↗

  julien-c

• Qwen3.8-Flash-Next-GSQ-RCO-abliterated-GGUF-IQ3_XXS↗

  KENIC-1

• Kimi-VL-A3B-Instruct↗

  月之暗面 · Kimi

  We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B). Kimi-VL demonstrates strong performance across challenging domains: as a general-purpose VLM, Kimi-VL excels in multi-turn agent interaction tasks (e.g.,OSWorld), achieving state-of-the-art results comparable to flagship models. Furthermore, it exhibits remarkable capabilities across diverse challenging vision language tasks, including college-level image and video comprehension, optical character recognition (OCR), mathematical reasoning, multi-image understanding, and etc. In comparative evaluations, it effectively competes with cutting-edge efficient VLMs such as GPT-4o-mini, Qwen2.5-VL-7B, and Gemma-3-12B-IT, while surpassing GPT-4o in several specialized domains.

• Agent-ModernColBERT↗

  lightonai

• Meta-Llama-3-70B-Instruct-GGUF↗

  LiteLLMs