Homebrew-Qwen-Image-2.1-Y2K
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Explore AI models 21
openNemo-9B-abliterated-GGUF
empero-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 ↗Qwable-9B-Claude-Fable-5
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View details ↗Qwable-9B-Claude-Fable-5-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwable-9B-Claude-Fable-5** for llama.cpp, Ollama, LM Studio, and other GGUF runtimes. This repo ships a **vision projector (`mmproj`)**, so the model runs as a full **multimodal (image + text)** assistant — not just text. Qwable-9B-Claude-Fable-5 is a full-parameter fine-tune of Qwen3.5-9B on agentic coding and reasoning traces distilled from Claude Fable 5 and a GPT-5.5 terminal agent. For full training details and the complete evaluation, see the **base model card**. > **Early release.** Strong coding and agentic behavior out of the box; a full benchmark suite is underway and > will be published. See Provenance & licensing.
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View details ↗Qwen3.5-9B-Claude-Code-GGUF
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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 ↗Qwen3.8-27B-Ridge-GGUF
empero-aiA Gated-DeltaNet-aware mixed GGUF of official **Qwen/Qwen3.8-27B** (`1d4bf0f2`) for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other stock GGUF runtimes. This is a quantization of the Qwen3.8-27B checkpoint. Ridge is a probed mix of types written for this architecture: 64 layers = 16 × `(3 × GatedDeltaNet → FFN + 1 × GatedAttn → FFN)`. Generic `IQ2_XS` and UD-IQ2 do not treat GDN state (`ssm_alpha` / `ssm_beta`) or the GDN mixers as first-class. We fixed that. Nothing was stripped to make the file fit. The native MTP draft head (`blk.64` / `nextn`) stays in the GGUF. Vision is a separate BF16 `mmproj`.
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View details ↗Qwen3.8-2B-Distill
empero-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 ↗Qwen3.8-2B-Distill-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwen3.8-2B** — a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-2B architecture, the smallest member of the family — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes. This card is about choosing a file and running it. The capability writeup, full benchmark results, and best practices live on the **main model card**. Headline results for the source model (CoT protocols, `lm-evaluation-harness`, identical settings base vs. student):
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View details ↗Qwen3.8-35B-A3B-Distill
empero-ai> [!Note] > This repository contains model weights and configuration files in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.6 architecture support. **Qwen3.8-35B-A3B** is a distillation of the **Qwen3.8** frontier models into the Qwen3.6-35B-A3B Mixture-of-Experts architecture. The student was trained on curated teacher traces from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, code, general reasoning, instruction following, and tool use, quality-filtered before training. The objective: bring the reasoning behavior of frontier-scale teachers into a sparse 35B that activates only 3B parameters per token and deploys on a single GPU.
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View details ↗Qwen3.8-35B-A3B-Distill-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwen3.8-35B-A3B-Distill** — a distillation of the Qwen3.8 frontier models into the Qwen3.6-35B-A3B Mixture-of-Experts architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes. This card is about choosing a file and running it. The capability writeup, benchmark results, and best practices live on the **main model card**. 35B total parameters with ~3B active per token — the MoE sparsity means it runs considerably faster than a dense 35B at the same quant, but the **whole weight file still has to fit in RAM or VRAM**.
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View details ↗Qwen3.8-4B-Distill
empero-ai> [!Note] > This repository contains model weights and configuration files in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.5 architecture support. **Qwen3.8-4B** is a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-4B architecture. The student was trained on **~45,000 curated teacher traces** from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, general reasoning, and instruction following, quality-filtered before training. The objective: bring the reasoning behavior of a frontier-scale teacher into a 4B that runs comfortably on consumer hardware.
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View details ↗Qwen3.8-4B-Distill-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwen3.8-4B** — a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-4B architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes. This card is about choosing a file and running it. The capability writeup, full benchmark results, and best practices live on the **main model card**. Headline results for the source model (CoT protocols, `lm-evaluation-harness`, identical settings base vs. student):
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View details ↗Qwen3.8-9B-Distill
empero-ai> [!Note] > This repository contains model weights and configuration files in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, and other standard runtimes with Qwen3.5 architecture support. **Qwen3.8-9B** is a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-9B architecture. The student was trained on **~70,000 curated teacher traces** from our internal Qwen3.8 distillation datasets — dense chain-of-thought spanning mathematics, code, general reasoning, instruction following, and tool use, quality-filtered before training. The objective: bring the reasoning behavior of a frontier-scale teacher into a dense 9B that deploys on a single GPU.
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View details ↗Qwen3.8-9B-Distill-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwen3.8-9B** — a full-parameter distillation of **Qwen3.8 2.4T A95B** into the Qwen3.5-9B architecture — for llama.cpp, Ollama, LM Studio, Jan, KoboldCpp, and other stock GGUF runtimes. This card is about choosing a file and running it. The capability writeup, full benchmark results, and best practices live on the **main model card**. Headline results for the source model (CoT protocols, `lm-evaluation-harness`, identical settings base vs. student):
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View details ↗Qwythos-27B-v1
empero-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 ↗Qwythos-27B-v1-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwythos-27B-v1** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. Qwythos-27B-v1 is a dense, hybrid Qwen3.5-27B—the larger continuation of Qwythos-9B, trained on the same curriculum at a larger scale. Nothing was removed to make this conversion fit: its native MTP head, vision tower, and 1,048,576-token (1M) context capability are intact. This is the pre-RL v1 checkpoint, following SFT, DPO, and ESFT; an RL-trained v2 will follow, while v1 is a complete checkpoint in its own right. For the full capability, training, and evaluation writeup, see the **base model card**. This card is intentionally focused on choosing a file and running it. The repository is named `Qwythos-27B-v1-GGUF`, but the files on disk deliberately use the shorter `Qwythos-27B-*` names below. Use those exact filenames when downloading or passing `-m`.
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View details ↗Qwythos-9B-Claude-Mythos-5-1M
empero-ai**Qwythos-9B** is a full-parameter reasoning model built on top of a **deeply uncensored Qwen3.5-9B base** and post-trained on **over 500 million tokens** of high-quality Claude Mythos and Claude Fable traces, with chain-of-thought generated in-house by Empero AI's internal tool **rethink**. The result is a compact, fast, **dramatically more capable** 9B reasoning model. Headline capabilities: Qwythos is intentionally **uncensored**. It is designed to engage seriously with technically demanding questions across cybersecurity, red-teaming methodology, biology, pharmacology, and clinical medicine — domains where over-aligned models tend to refuse, hedge into uselessness, or surface boilerplate disclaimers in place of substance.
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View details ↗Qwythos-9B-Claude-Mythos-5-1M-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwythos-9B-Claude-Mythos-5-1M** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. Qwythos-9B is a full-parameter reasoning model post-trained on over 500 million tokens of high-quality Claude Mythos / Claude Fable traces with chain-of-thought generated in-house by Empero AI's internal `rethink` tool. It dominates the base Qwen3.5-9B under matched evaluation (**+34 pts MMLU, +30 pts gsm8k-strict, +19 pts gsm8k-flex**), supports **native function calling** per the Qwen3.5 spec, and ships with a **1,048,576-token (1M) context window** via YaRN rope-scaling enabled by default. For full training details, evaluation numbers, and capability writeup, see the **base model card**.
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View details ↗Qwythos-9B-v2
empero-aiThe next iteration of Qwythos: **all the reasoning of Qwythos-9B, with the looping behavior fixed.** v2 keeps the deep chain-of-thought, the uncensored research posture, and the 1M-token context of its predecessor, and cleans up the rough edges that showed up in real use. The fix uses **FTPO (Final-Token Preference Optimization)**: we identify the exact token that *starts* a repetition loop and gently train the model to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore the model's knowledge and reasoning — untouched. Measured with our internal harness (generative chain-of-thought, greedy/pass@1 unless noted; MMLU/ARC/GSM8K n=500, GPQA-diamond n=198, HumanEval n=164). Judge for the quality metric: an independent LLM grader.
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View details ↗Qwythos-9B-v2-GGUF
empero-aiGGUF quantizations of **empero-ai/Qwythos-9B-v2** for llama.cpp, Ollama, LM Studio, jan, KoboldCpp, and other GGUF runtimes. **Qwythos-9B-v2 is the new and improved Qwythos** — all the deep chain-of-thought reasoning of the base Qwythos, with the **looping behavior fixed**. The looping/degeneration that showed up under greedy or low-temperature decoding is trained out (**6.7% → 0%**), the native **MTP head is restored**, and the identity prompt is cleaned up — while knowledge and reasoning are held at (or above) the base Qwythos level. The fix uses **FTPO (Final-Token Preference Optimization)**: the exact token that *starts* a repetition loop is identified and the model is gently trained to prefer coherent alternatives at that one position, leaving the rest of the distribution — and therefore its knowledge and reasoning — untouched.
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