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AI モデル 85003

• 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

• DeepSeek-V4.1-Flash-1017-Volta↗

  lxe

• Qwen3.6-27B-Omnimerge-v4-GGUF↗

  ManniX-ITA

  GGUF quantizations of `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` — the **MLP-passthrough** variant that defends against the Qwen3.6 think-policy fragility we discovered. Source dtype is BF16; this repo provides the standard bartowski quant ladder (F16 → IQ2_XXS) for `llama.cpp`. > **Source model:** `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4` (BF16 weights, model card with full benchmarks and methodology). > **NOT** a quant of clean Qwen/Qwen3.6-27B — these GGUFs contain the v4 merge. > > **MTP companion (2× decode speedup):** weight-identical GGUFs with the MTP head retained for `llama.cpp --spec-type draft-mtp` self-speculative decoding are at `ManniX-ITA/Qwen3.6-27B-Omnimerge-v4-MTP-GGUF`. Quality is statistically indistinguishable from this repo (HE 137/164 ↔ 137/164, GPQA 155/198 ↔ 154/198); aggregate decode is 2.0-2.3 × faster on a single 24 GB GPU. Use that repo for interactive / single-request workloads where latency matters. All quants made using imatrix with calibration data v5, the same calibration set bartowski uses for the Qwen3.6 base release — so quality fingerprints are directly comparable to bartowski's `Qwen_Qwen3.6-27B-GGUF` repo.

• Qwen_Qwen3.5-4B-Q4_K_M.gguf↗

  Manojb

• T3qm7Neuralsynthesis-7B-GGUF↗

  MaziyarPanahi