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Kolibri-1

Aleph-Alpha

Kolibri is Aleph Alpha's mixture-of-experts (MoE) reasoning model, with a focus on German and English. The model supports an explicit reasoning mode and tool calling. It is optimized for long-context and inference efficiency. Kolibri is intended to process text input and output in German and English and to perform a wide range of tasks beyond natural-language generation, for example multi-step reasoning, coding, structured extraction, retrieval-augmented generation, long-document processing and agentic tool calling. Kolibri was pre-trained on sequences of 16,384 tokens, mid-trained on 65,536 and trained on 262,144 tokens in a final long-context phase, which is its native context length. Because positional encoding is applied only in the sliding-window layers, the context can be extended beyond that length without any position scaling, in principle to arbitrary lengths. We have validated quality and serving efficiency up to 1,048,576 tokens. For latency- or throughput-sensitive deployments and for complex tasks, we recommend contexts of at most 262,144 tokens. See the technical report for details.

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Kolibri-1-BF16

Aleph-Alpha

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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Pharia-1-LLM-7B-control-hf

Aleph-Alpha

This is the safetensors-conversion of `Pharia-1-LLM-7B-control`. We provide a joint model card for `Pharia-1-LLM-7B-control` and `Pharia-1-LLM-control-aligned`. Find this model card here.

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FormulaNet

alephpi

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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이미지 설명

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wav2vec2-cv-be

ales

Fine-tuned version of facebook/wav2vec2-base on `mozilla-foundation/common_voice_8_0 be` dataset. `Train`, `Dev`, `Test` splits were used as they are present in the dataset. No additional data was used from `Validated` split, only 1 voicing of each sentence was used - the way the data was split by CommonVoice CorporaCreator. To build a better model **one can use additional voicings from `Validated` split** for sentences already present in `Train`, `Dev`, `Test` splits, i.e. enlarge mentioned splits. Language model was built using KenLM. 5-gram Language model was built on sentences from `Train + (Other - Dev - Test)` splits of `mozilla-foundation/common_voice_8_0 be` dataset.

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음성 인식

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Dolphin3.0-CoreML

ales27pm

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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lumen-qwen3-bootstrap-adapters-gguf

ales27pm

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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lumen-qwen3-bootstrap-gguf

ales27pm

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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GLM-5.3-Flash-abliterated-Maya-M-GGUF

alesha-pro

Project Maya's Maya-M quant of GLM-5.3-Flash plus a 2.2 MB vector that removes refusals while the model runs, and a small patch for the Maya engine that applies it. On 4x RTX 3090 it decodes 56 tokens/s at a 256K context with the vector on. The model weights in this repo are not mine. They are Maya-M and the vision files of Project Maya, copied byte for byte from peasantsmith/GLM-5.3-Flash-Maya-GGUF at revision `3e9f3bb20aa191e01c6fb850faf132d8149da98a`. The SHA256 of all 5 files match theirs at that revision. See `SHA256SUMS`. The authors changed the first shard after I downloaded it. Their commit `1084950a` renames the architecture in the GGUF header to `glm5-next`, and the file size is the same. This repo has the earlier first shard, the one all my runs used with Maya v1.0.27. For the current files go to their repo.

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Huihui-Qwen3.8-Flash-Next-abliterated-exl3-3bit-hq_h6_ng6

alesha-pro

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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Huihui-Qwen3.8-Flash-Next-abliterated-exl3-4bit-hq_h6_ng6

alesha-pro

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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Qwen3.8-27B-S-mirai-GGUF

alesha-pro

A GGUF of Mirai's 2.4-bit Qwen3.8-27B. Mirai's compressed trellis codes are copied into the GGUF bit for bit, nothing is re-quantized. A 27B model in 11.2 GB that runs on one 12 GB GPU, with tools, reasoning, images, and an optional file that removes refusals. The format adds 4 ggml types, so mainline llama.cpp, LM Studio and Ollama will not load this file. 2 engines do. This is a community conversion, not an official Mirai release. Use mirai-s-ada. It took the codec from my fork, checked that its greedy output matches the fork token for token, and added what a 12 GB card needs: a KV cache that continues in system RAM, MTP drafting from the draft block inside this GGUF, and fixes for apps that send `effort: "high"` or tiny output caps. The numbers in its column are Cary's, from his RTX 4070 12 GB. I repeated them on Ubuntu with a 3090, see "Measured".

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Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Coder-Strata-GGUF

alesha-pro

The model weights in this repo are not mine. They are the GSQ-RCO Coder release of Qwen3.8-Flash-Next made by the Deep Algorithms and Systems Lab at ISTA: 256 of the 512 experts in each layer are kept, the rest are removed, and the kept weights are stored at 3.5 bits. The files are copied byte for byte from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-Coder-GGUF at revision `5348543e0147355ac9cbcb031184a3546350988e`. The SHA256 of both shards and of the vision projector match their files. See `SHA256SUMS`. If you use these weights, cite the two ISTA-DASLab papers. The BibTeX is at the bottom of this page. My part is the check that the refusal vector I published for the unpruned sizes also works on the pruned model, and the measurements below. The vector is the same 480 KB file as in the repo with the four unpruned sizes, with the same SHA256. For the original card and ISTA's own SWE-bench and LiveCodeBench numbers, go to the ISTA-DASLab repo.

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Qwen3.8-Flash-Next-abliterated-GSQ-RCO-Strata-GGUF

alesha-pro

The model weights in this repo are not mine. They are the four GSQ-RCO quantizations of Qwen3.8-Flash-Next (Q2_0, IQ2_XS, IQ3_XXS, IQ3_S) made by the Deep Algorithms and Systems Lab at ISTA, copied byte for byte from ISTA-DASLab/Qwen3.8-Flash-Next-GSQ-RCO-GGUF at revision `ed59f92082b1e93c0e96d60a8b11aab089b52f09`. The SHA256 of every shard and of the vision projector match their files. See `SHA256SUMS`. If you use these weights, cite the two ISTA-DASLab papers. The BibTeX is at the bottom of this page. My part is one 480 KB file, `Huihui-Qwen3.8-Flash-Next-refusal-direction-r.gguf`, and the measurements below. The same file serves every size. I measured IQ3_S and IQ2_XS with it. Q2_0 and IQ3_XXS are here as ISTA-DASLab published them and I have not run them. For the original card and ISTA's own benchmarks, go to the ISTA-DASLab repo.

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