translategemma-4b-it-GGUF
aoiandroid基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DISCOVER & COMPARE
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View details ↗T-lite-it-1.0-Q4_K_M-GGUF
aovchinnikov基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗search_slm_demo
aozalevsky基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗eds-pseudo-public
AP-HP基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗ds4-kolibri-1-gguf
aparusel基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwythos-9B-Claude-Mythos-5-1M-GGUF
ApertureQA基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4.1-Flash-1017-Abliterated-MLX
apetersson基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4.1-Flash-MixedQ2-GGUF
apeterssonRouted-expert requantisation of `deepseek-ai/DeepSeek-V4.1-Flash` revision `2bc89ac599031fa673cab993f1df02fc4a98c673`, produced on a 32-vCPU Runpod CPU pod with the pinned llama.cpp ggml reference quantisers (PR `ggml-org/llama.cpp#28696` head `cd628010bc3fc0a787d156c969d52a0789451c96`). Tensor names follow that PR's `deepseek4` table. This is a **weights artifact**. No runtime has yet been shown to execute it end to end; the "Runtime status" and "Smoke-test findings" sections say exactly what has been measured on which hardware, and what is still blocked. `SHA256SUMS` covers the five shards. The pack is deterministic: two independent runs produced byte-identical shard digests.
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View details ↗> **Experimental low-bit model-surgery derivative.** This release is designed > to run DeepSeek-V4-Flash-0731 with its real three-stage DSpark drafter on a > 128 GB Apple Silicon machine. Structural validation has passed, but direct > generation, quality, refusal-removal, DSpark-acceptance, throughput and peak > memory measurements for this exact artifact are still pending. This is a DS4-native mixed-precision GGUF conversion of `apetersson/DeepSeek-V4-Flash-0731-Abliterated-FP8`, which is itself a rank-1 abliterated derivative of `deepseek-ai/DeepSeek-V4-Flash-0731`. It remains a derivative of the official post-trained 0731 model: it is not a base model, was not gradient-finetuned, and is not claimed to be universally "uncensored." The two GGUF files total approximately 86.34 GiB. The profile deliberately spends precision on dense paths used for every token while aggressively compressing the much larger routed-expert bank, leaving practical unified- memory headroom for DS4 runtime state and KV cache on a 128 GB host.
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4-Flash-0731-Abliterated-FP8
apetersson基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗DeepSeek-V4-Flash-Vision-Exp-Abliterated
apetersson> [!WARNING] > Experimental research release. Text and focused image smoke tests pass, but > broader quality, safety, and production-runtime evaluation remains pending. This catalog contains the native abliterated checkpoint and two DS4 GGUF profiles. Both GGUF receivers use tensor types accepted by `antirez/ds4` main; neither requires the legacy Python vision sidecar for current native vision. `Basic128-Routed-IQ2_M` uses the closest familiar BPW class for the complete bundle: 2.915 BPW overall, close to llama.cpp's documented 2.9294 BPW for IQ2_M. The routed-expert subset averages 2.715 BPW, close to IQ2_S. This is a custom mixed recipe rather than llama.cpp's exact IQ2_M preset: routed gate/up tensors use IQ2_XXS, routed down tensors use Q2_K, ten routed layers retain MXFP4, and non-routed tensors include F32, F16, and Q8_0. See the llama.cpp quantization table for the reference BPW classes.
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View details ↗Qwen3.8-Flash-Next-Abliterated
apetersson基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-Flash-Next-Abliterated-Adapter
apetersson基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Qwen3.8-Flash-Next-GGUF
apetersson基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Apex-35B-V1.0
Apexintelligence-AI基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vjepa2.1-vit-base-384
apiantonio基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗vjepa2.1-vit-large-384
apiantonioA HuggingFace-format conversion of Meta AI's **V-JEPA 2.1 ViT-L/16** video encoder and predictor, operating at 384x384 resolution. The weights are Meta's, copied without modification. This repository provides the `transformers`-compatible packaging plus a documented numerical validation against the original implementation. An equivalent community port already exists (`Dev-Jahn/vjepa2.1-vitl-fpc64-384`). This repository adds an independently reproduced conversion together with the validation results below. Forward outputs match the existing port to all reported digits.
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View details ↗G4-31B-Style-r1-c90-gguf
ApocalypseParty基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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View details ↗Apodex-1.0-mini
apodex**Apodex-1.0** is a verification-centric model for deep research. The trained model alone, Apodex-1.0, runs as a standard tool-using ReAct agent. Deployed in our **heavy-duty mode** — an asynchronous agent team in which sub-agents specialize in retrieval and verification, route their reports through a shared evidence pool, and feed a global verifier that reasons over the assembled evidence graph to produce the final answer — it becomes **Apodex-1.0-H**. Under the hood, a high-quality data pipeline and a three-stage post-training recipe (SFT, agentic DPO, RL on long agentic rollouts) substantially raise the deep-research capability of the Qwen3.5 base while *preserving* its general knowledge, coding, reasoning, and instruction-following capabilities — the recipe is additive on the deep-research axis rather than a trade across axes. Apodex-1.0-H sets a new state of the art across both open- and closed-source models on deep-research benchmarks. Every claim in the final report it produces is backed by an explicit evidence chain and independently audited by a verification team before delivery.
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