BAAR5-9M
aixk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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BAAR6-9M-7
aixk🌐 Official Website: https://baar.uk/model/ 📄 License: Non-Commercial Only (Contact for Commercial Pricing) **BAAR5-9M** is an ultra-compact multilingual conversational model designed to enable real-time chat under minimal hardware conditions. With just **9 million (9M) parameters**, it brings lightweight, interactive dialogue capabilities across **11 languages** to extreme resource-constrained environments, embedded devices, and on-device platforms where standard LLMs cannot operate. **BAAR5-9M**은 최소한의 하드웨어 여건과 극단적인 리소스 제약 환경에서도 **11개 언어**로 실시간 대화(Chat)가 가능하도록 설계된 900만(9M) 파라미터 규모의 초경량 다국어 언어 모델입니다. 일반적인 대형 모델 구동이 불가능한 초저사양 엣지 및 임베디드 기기에서도 즉각적인 대화형 추론과 초경량 실시간 RAG 시스템 구축을 지원합니다.
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View details ↗NoRefER
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View details ↗LFM3-1.7B-heretic
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View details ↗GLM-5.3-Flash-GGUF
aj9o9Two quants, 87 GB and 112 GB, built by assigning every tensor class by hand instead of using a preset. GLM-5.3-Flash is 320.6B total parameters with 17.3B active. Three things about its architecture drive every decision in these quants. **It is 94.9% routed experts.** 304.4B of the 320.6B parameters sit in `ffn_*_exps` tensors across 42 sparse layers, 288 experts each, 8 active per token plus 1 shared. Everything else in the model, all the attention and embeddings and norms, is 8.9B parameters. That means the file size is set almost entirely by one decision: how many bits the experts get. Everything else can be kept generous for almost nothing.
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View details ↗Qwen2.5-Coder-1.5B-Shellsmith
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View details ↗AR-4B-base
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View details ↗AfriHuBERT
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View details ↗GGUF quants of ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-MTP, an abliterated Swift 1.5 Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). If you have a GPU with Blackwell architecture: NVFP4 (VLLM/SGLANg). - **Uncensored:** 23/100 refusals against Swift 1.5's 98/100, at a KL divergence of 0.0884 from Swift 1.5. - **Two layouts:** `UD-*` files follow Unsloth's Dynamic 3.0 per-tensor types; `RCO-*` files, in the `RCO/` folder, follow ISTA-DASLab's GSQ-RCO allocation (see RCO quants). The RCO files were added on request and are more experimental. - **MTP head included** in every main GGUF, for self-speculative decoding in llama.cpp. - **Vision:** image and video input through the included projector. BF16 50.9 GiB — — UD-Q8_K_XL 29.3 GiB 0.0068 98.8 % UD-Q6_K_XL 23.6 GiB 0.0138 98.2 % UD-Q5_K_M 18.4 GiB 0.0238 96.8 % UD-Q5_K_S 17.4 GiB 0.0279 96.4 % UD-Q4_K_XL 16.4 GiB 0.0369 95.6 % UD-Q4_K_S 14.3 GiB 0.0575 94.0 % UD-IQ4_XS 13.3 GiB 0.0691 92.9 % UD-Q3_K_XL 12.2 GiB 0.0789 91.7 % RCO-IQ3_S 11.3 GiB 0.1084 89.5 % UD-IQ3_S 11.2 GiB 0.1074 89.9 % UD-IQ3_XXS 10.2 GiB 0.1353 87.8 % RCO-IQ3_XXS 9.7 GiB 0.1774 86.4 % UD-Q2_K_XL 9.2 GiB 0.1902 85.1 % RCO-IQ2_S 8.9 GiB 0.2349 83.8 % RCO-IQ2_XS 8.2 GiB 0.3018 81.2 %
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View details ↗NVFP4 checkpoint of ajgazin/Swift-1.5-Qwen3.8-27B-Uncensored-MTP, an abliterated Swift 1.5 Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang. GGUFs for llama.cpp: Swift-1.5-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF. Sampling, as for Swift and Qwen: temperature 1.0, top_p 0.95, top_k 20, min_p 0. The model thinks before answering by default. Same format, recipe, module split and tensor layout as the Swift 1.0 version, which was load-tested on an RTX 5090 with vLLM 0.29.0 (native W4A4, MTP decoding and image input working). This checkpoint was not load-tested separately.
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View details ↗GGUF quants of ajgazin/Swift-Qwen3.8-27B-Uncensored-MTP, an abliterated Swift-Qwen3.8-27B (UkisAI's reasoning-efficient fine-tune of Qwen3.8-27B). For vLLM and SGLang: NVFP4. The Swift 1.5 version is Swift-1.5-Qwen3.8-27B-Uncensored-Dynamic-MTP-GGUF. Each quant is one file, `Swift-Qwen3.8-27B-Uncensored-Dynamic-MTP- .gguf`; the BF16 is `Swift-Qwen3.8-27B-Uncensored-MTP-BF16.gguf`. Each quant compared with the BF16 it was made from, token by token: wikitext-2 test set, 36 × 8192 tokens, f16 KV cache, llama.cpp `94659b076`. Lower KLD and higher top-1 are better.
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View details ↗Anxiety_fusion
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View details ↗gpt2-indonesia
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View details ↗qwen3-0.6b-text-embedding
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View details ↗Cyber-Prime-1.1-2.6B
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