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Grok-1-GGUF
Arki05This repository contains unofficial GGUF Quantizations of Grok-1, compatible with `llama.cpp` as of PR- Add grok-1 support #6204. - The splits have been updated to utilize the improvements from PR: llama_model_loader: support multiple split/shard GGUFs. As a result, manual merging with `gguf-split` is no longer required. With this, there is no need to merge the split files before use. Just download all splits and run llama.cpp with the first split like you would previously. It'll detect the other splits and load them as well.
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View details ↗LFM2.5-2.6B-GGUF
Arki0517 GGUF files of LiquidAI/LFM2.5-2.6B at the sizes of bartowski's quants and LiquidAI's own, with 26–77 % less KL divergence than the published file of the same size (about half or less from 3 to 5 bits per weight). Two things make the difference: **Build your own size:** the qalloc app solves the mix for any size you pick and assembles the GGUF in your browser, tensor by tensor from the store. Per token, against the BF16 model: the mean KL divergence and its 99th percentile (lower is better), and how often the most likely next token is the BF16 model's (higher is better), for the published file of the same size → this file. Where two published files are within 1 % of each other's size, one file here serves both.
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View details ↗LFM2.5-2.6B-GGUF-shards
Arki05- `source/`: LiquidAI's BF16 GGUF (LiquidAI/LFM2.5-2.6B-GGUF), unchanged. - `types/ -hq.gguf`, `types/ -hq1.gguf`: the tensors at llama.cpp type `T`, rebuilt from the BF16 weights in two Hessian-quantized (GPTQ-family) builds: `-hq`, the higher-quality one, the block tensors; `-hq1`, the first build, every quantized tensor. Same type, format and size as llama.cpp's: plain GGUF types, for stock llama.cpp (a GGUF assembled from them holds the plain type `T`); modified versions of the original weights. - `data/`: the calibration data the types were made from (token ids and their manifest). - `manifest.json` (`qcore-store/1`), `skeleton.gguf`, `store.json`, `records/`: what qalloc reads, with every tensor's offset and sha256. The first version of this store (bartowski's BF16 GGUF and imatrix, types by llama.cpp's `llama-quantize`) is at revision `fe50613`.
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View details ↗North-Mini-Code-1.0-GGUF
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View details ↗Qwen3.5-4B-GGUF-shards
Arki05Qwen/Qwen3.5-4B quantized to 25 llama.cpp types, each tensor at each type, for assembling GGUFs with a type per tensor.
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View details ↗Qwen3.8-27B-GGUF
Arki0510 GGUF files of Qwen/Qwen3.8-27B at the sizes of Unsloth's dynamic quants and of ISTA-DASLab's GSQ-RCO quants, with 16–38 % less KL divergence than the Unsloth file of the same size and 11–26 % less than ISTA-DASLab's. Two things make the difference: **Build your own size:** the qalloc app solves the mix for any size you pick and assembles the GGUF in your browser, tensor by tensor from the store. Per token, against the BF16 model: the mean KL divergence and its 99th percentile (lower is better), and how often the most likely next token is the BF16 model's (higher is better), for the published file of the same size → this file.
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View details ↗Qwen3.8-27B-GGUF-shards
Arki05- `source/`: Unsloth's BF16 GGUF (unsloth/Qwen3.8-27B-GGUF), unchanged. - `types/ -hq.gguf`: every linear block tensor at llama.cpp type `T` (IQ1_M … Q8_0, IQ4_NL, MXFP4, NVFP4), rebuilt from the BF16 weights Hessian-quantized. Same type, format and size as llama.cpp's: plain GGUF types, for stock llama.cpp (a GGUF assembled from them holds the plain type `T`); modified versions of the original weights. - `types/ .gguf` (Q2_K … Q8_0): token_embd, output, the multi-token-prediction layer and the linear attention's small `ssm_alpha` / `ssm_beta` tensors at llama.cpp's own quantization. - `manifest.json` (`qcore-store/1`), `skeleton.gguf`, `store.json`, `records/`: what qalloc reads, with every tensor's offset and sha256. The earlier version of this store (every tensor at ~41 types by llama.cpp's and ik_llama.cpp's quantizers) is at revision `0090490`.
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View details ↗tiny-edu-114m
Arko007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 ↗Zenyx_114M-Tiny-Edu-Instruct
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View details ↗zenyx-v2-base
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View details ↗Zenyx-V3-SFT
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View details ↗jina-embeddings-v2-base-en
arkohutThe easiest way to starting using `jina-embeddings-v2-base-en` is to use Jina AI's Embedding API. `jina-embeddings-v2-base-en` is an English, monolingual **embedding model** supporting **8192 sequence length**. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone `jina-bert-v2-base-en` is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process. The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.
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View details ↗GLM-4.6-Derestricted-v3
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