spamai
cybert79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
Explore AI models 84991
Dolphin-Mistral-24B-Venice-Edition-mlx-4Bit
cyboghostginx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 ↗Dolphin-X1-8B-mlx-4Bit
cyboghostginx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 ↗restore_photo_v4.1-lora
cyburn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 ↗restore_photo_v4-lora
cyburn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 ↗CmdCaliper-base
CyCraftAI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 ↗PLLuM-12B-base-2512
CYFRAGOVPL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 ↗PLLuM-12B-chat-2412
CYFRAGOVPL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 ↗PLLuM-12B-chat-2512
CYFRAGOVPLPLLuM is a family of large language models (LLMs) specialized in Polish with additional English data incorporated for broader generalization. Developed through an extensive collaboration with various data providers, PLLuM models are built on high-quality text corpora and refined through instruction tuning, preference learning, and advanced alignment techniques. These models are intended to generate contextually coherent text, offer assistance in various tasks (e.g., question answering, summarization), and serve as a foundation for specialized applications such as domain-specific intelligent assistants. In 2024, PLLuM models were developed by the PLLuM consortium. Since 2025, their development has continued under HIVE AI, a broader alliance of research institutions and organizations delivering digital public services, focused on open language technologies for Polish public administration. - **Extensive Data Collection** We gathered large-scale, high-quality text data in Polish and English, focusing on rigorous cleaning and deduplication to ensure the highest training standards. - **Organic Instruction Dataset** We curated the largest Polish collection of manually created “organic instructions”. This human-authored instruction set is based on an extensive typology of human-model interactions and it covers a range of subtle aspects of supervised fine-tuning (SFT) that might be overlooked with automated approaches (including large scale distillation of 'strong LLMs'). It was also designed to mitigate negative linguistic transfer from non-Polish textual data used in the pre-training phase.
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View details ↗PLLuM-12B-instruct-2512
CYFRAGOVPL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 ↗PLLuM-4B-chat-2512
CYFRAGOVPL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 ↗PLLuM-4B-instruct-2512
CYFRAGOVPL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 ↗Quantizations of trohrbaugh/Qwen3.8-27B-heretic-ara with **MTP (Multi-Token Prediction) tensors preserved** (866 tensors) and **vision projector (mmproj)** for multimodal image understanding. Includes **ROCmFPX quants** for the ROCmFPX fork of llama.cpp — **~42 t/s average decode** on AMD Strix Halo (50–75% faster than stock Vulkan). trohrbaugh/Qwen3.8-27B-heretic-ara — Arbitrary-Rank Ablation (ARA) uncensoring of Qwen3.8-27B using heretic v1.2.0+custom. Parameters: start_layer 26, end_layer 56, preserve_good_behavior_weight 0.9432, steer_bad_behavior_weight 0.0009. KL divergence 0.0535, 0/100 refusals.
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View details ↗IQ4_XS quantization of Qwen3.8-Flash-Next abliterated with Heretic v1.3.0+custom (fork), with NGQ4-compressed n-gram prediction table. Built and benchmarked on AMD Strix Halo (Ryzen AI Max+ 395, gfx1151, 128 GB unified). Quantized from the Heretic2 BF16 safetensors → Q8_0 intermediate → IQ4_XS using the llama-flash-next fork of llama.cpp (build 10656, commit 035e22731). The NGQ4 step compresses the 54 GB per-layer n-gram prediction table to ~27 GB by lowering precision on the indexer's token embedding, reducing the model from 116 GB to 92 GB with no measurable accuracy impact through 32K context. Requires the llama-flash-next fork (qwen4exp branch) — standard llama.cpp cannot load the qwen4exp architecture.
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View details ↗First working GGUF build of Qwen3.8-Flash-Next (`qwen4exp` architecture) with vision, running on stock llama.cpp — no custom tensor formats or forked runtime required. Built from orcarouter/Qwen3.8-Flash-Next-Uncensored, the abliterated (uncensored) release of Qwen's newest hybrid architecture. ⚠️ **Research artifact.** Refusal behaviour has been removed from the source model. This does not add capability — it removes guardrails. Use it deliberately, in a context where that is appropriate, and own the output. **5.61 BPW average, 98.4 GB total.** The n-gram table alone is ~29 GB of that at Q4_0 — it's lookup-only (hash → embedding), so it tolerates low-bit quantization well, but it needed a separate streaming dequant/requant pass (chunked, ~2M rows at a time) because standard `llama-quantize` can't process its unusual 160-wide row shape and would need ~200+ GB to dequantize it in one pass.
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View details ↗GGUF quantizations of philbert440/Qwen3.8-27B-Uncensored-Cyber, calibrated on an importance matrix built from **real agentic-coding traffic** rather than generic English prose. **All model weights and behaviour are philbert440's work. This repository contributes only the quantization and the calibration methodology** — no fine-tuning, no merging, no behavioural change of any kind was performed here. Inherited from upstream, and stated plainly so nobody is surprised: this is a **de-refusal ("abliterated") model specialized for the cyber/offensive-security domain**. The vision tower and the MTP speculative-decoding head are preserved upstream, which is what makes the multimodal path and the MTP graft work at all. Read the base model's card before using it — it documents the recipe and the evaluation.
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View details ↗stable-diffusion-v1-5
cym256413987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 ↗qwen-demo-test
cynthiaa-saab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 ↗CPH-Community-7B
CypherTroopersA compact 7B-model fine-tuned for **Cypherium blockchain operations**, validator support, node configuration, RPC troubleshooting, and general-purpose lightweight reasoning. This model is optimized for **CPU-only inference** using `llama.cpp` and provides fast responses on low-resource servers such as VPS instances (2–6 vCPUs, 8–16GB RAM). - **Base model:** Qwen2-7B - **Fine-tuning:** QLoRA - **Domain:** Cypherium blockchain RPC, node operations, validator troubleshooting - **Format:** GGUF (`Q4_K_M`) - **Intended use:** lightweight on-device assistant for Cypherium node operators
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View details ↗QwerySmith-1.0-Merged
Cyrax321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 ↗trocr-handwritten-cyrillic
cyrillic-trocr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 ↗Qwen3.6-27B-A2.8B
Cyronius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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