Open-Weight Models

Model · Text generation

ThinkingCap-Qwen3.8-27B-GGUF

Nguyen Quoc Vinh

Community GGUF quantizations of bottlecapai/ThinkingCap-Qwen3.8-27B. If this GGUF made your day easier, a coffee would make mine. I build and test these releases myself. Your coffee helps keep me going. Thank you for supporting this work. This is a community GGUF package of BottleCap AI's 27B dense vision-language model, based on Qwen3.8-27B. The upstream model card describes hybrid linear and full attention, image and video understanding, an MTP head, and a native context length of 262,144 tokens. See the upstream model card for its capabilities, evaluation results, and recommended sampling settings. Those upstream task measurements apply to the upstream checkpoint and its exact quantized…

EXL3 quant of ThinkingCap-Qwen3.8-27B, a finetune of Qwen/Qwen3.8-27B. Requires ExLlamaV3 1.5.1. The bit maps match turboderp's Qwen3.8-27B self-calibrated packs. This architecture is dense. There is no n-gram table, and -hq does not raise any layer. - 4.00 bpw: -b 4 -hb 5 -mb 4 -vb 6. Decoder layers are 4 bits. The output head is 5 bits. The MTP block is 4 bits. The vision tower is 6 bits. - 3.00 bpw: -b 3 -hb 4 -mb 3 -vb 4. Decoder layers are 3 bits. The output head is 4 bits. The MTP block is 3 bits. The vision tower is 4 bits. - 5.00 bpw: -b 5 -hb 6 -mb 4 -vb 6. Decoder layers are 5 bits. The output head is 6 bits. The MTP block is 4 bits. The vision tower is 6 bits. - 6.00 bpw: -b 6…

This model was quantized using oQ (oMLX v0.7.0) mixed-precision quantization.

Model · Text generation

Tiel-Coder-35B-A3B-APEX-I-MiniPlus-V2.1-GGUF

IsValorum

We maintain our previous releases publicly as a transparent engineering record of continuous optimization. Below is the exact evolutionary roadmap of our MiniPlus architectures: The APEX-I-MiniPlus quantization architecture powering this model was subjected to an extensive independent evaluation by Japanese AI researcher and evaluator zephel01 (CoolZero) on an NVIDIA RTX 5090 (32GB) workstation running llama.cpp CUDA b11027 with FlashAttention (-fa on -ctk q80 -ctv q80 -ngl 99). The evaluation tested the APEX-I hybrid MoE engine across 348 unseeded trials on SWE-bench style multi-file Python bug-fixing tasks with hidden pytest suites (llmbench): - L6 Multi-File Code Generation (60 tasks)…

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Model · Feature extraction

ToarimEmbedder

Manuel Antonio Camacho Reyes

TóarimEmbedder es una arquitectura algorítmica de embeddings con estado (stateful) diseñada específicamente para resolver la amnesia contextual y la pérdida de continuidad en sistemas de Recuperación Aumentada por Generación (RAG) aplicados a corpus técnicos extensos y altamente regulados (manuales de maquinaria pesada, compendios normativos, bitácoras operativas y expedientes legales). A diferencia de los modelos basados en Transformers convencionales con atención densa cuadrática $O(n^2)$, TóarimEmbedder opera con complejidad lineal $O(n)$ en procesadores convencionales (CPU), permitiendo a las organizaciones indexar internamente su propiedad intelectual de forma ágil, blindando sus…

Published by TokForge for on-device inference. Runs on-device in the TokForge app. Bonsai 2 27B ternary, Abliterated. Prism ML's ternary Bonsai 2 27B in the PQ20 packing, with an MTP head, as abliterated by BoldingBuilds in their v2 release. This repo is an unmodified copy of that file, published so TokForge can pin it. - Needs TokForge 1.3.9.1 or newer and a phone with 16 GB of RAM or more. - Download the file from this page and import it in TokForge. - TokForge uses the MTP head on the CPU route only, and only on phones where its own check with and without the head measures a gain; elsewhere the file runs as a plain model. BoldingBuilds' own description and measurements of the edit are on…

Model · Text classification

Toxic-Not-Toxic

Saber Bounehas

This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 5e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - Transformers 5.19.0 - Pytorch 2.14.1 - Datasets 5.1.0 - Tokenizers 0.23.2