Open-Weight Models Under Apache License 2.0

本仓库提供由 Qwen/Qwen3-Embedding-0.6B 转换得到的 RKNN EMBEDDING 模型。 - Model ID：RKNNAI/RK182X-EMBEDDING-Qwen3-Embedding-0.6B - 模型显示名称：RK182X-EMBEDDING-Qwen3-Embedding-0.6B - 源模型：Qwen/Qwen3-Embedding-0.6B - 模型类型：EMBEDDING ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。

本仓库提供由 Qwen/Qwen2.5-1.5B-Instruct 转换得到的 RKNN LLM 模型。 - Model ID：RKNNAI/RK182X-LLM-Qwen2.5-1.5B-Instruct - 模型显示名称：RK182X-LLM-Qwen2.5-1.5B-Instruct - 源模型：Qwen/Qwen2.5-1.5B-Instruct - 模型类型：LLM ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。 本发布物为源模型的 RKNN 转换/量化衍生物，保留原始许可与归属声明，见 Notice。

本仓库提供由 Qwen/Qwen3-4B-Instruct-2507 转换得到的 RKNN LLM 模型。 - Model ID：RKNNAI/RK182X-LLM-Qwen3-4B-Instruct-2507 - 模型显示名称：RK182X-LLM-Qwen3-4B-Instruct-2507 - 源模型：Qwen/Qwen3-4B-Instruct-2507 - 模型类型：LLM ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。

本仓库提供由 PaddlePaddle/PaddleOCR-VL 转换得到的 RKNN OCR 模型。 - Model ID：RKNNAI/RK182X-OCR-PaddleOCR-VL - 模型显示名称：RK182X-OCR-PaddleOCR-VL - 源模型：PaddlePaddle/PaddleOCR-VL - 模型类型：OCR ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。 本发布物为源模型的 RKNN 转换/量化衍生物，保留原始许可与归属声明，见 Notice。

本仓库提供由 HuggingFaceTB/SmolVLM2-500M-Video-Instruct 转换得到的 RKNN VLM 模型。 - Model ID：RKNNAI/RK182X-VLM-SmolVLM2-500M-Video-Instruct - 模型显示名称：RK182X-VLM-SmolVLM2-500M-Video-Instruct - 源模型：HuggingFaceTB/SmolVLM2-500M-Video-Instruct - 模型类型：VLM ModelScope 完整下载： Hugging Face 完整下载： ModelScope 指定配置下载： Hugging Face 指定配置下载： - 使用配套 RKNN Runtime 和驱动；运行前用 rknn-smi -v 检查设备端版本。 源模型许可证见根目录 LICENSE；同时遵守 RKNN Toolkit 和 RKNN Runtime 许可条款。 本发布物为源模型的 RKNN 转换/量化衍生物，保留原始许可与归属声明，见 Notice。

Model · Text generation

Spark-X2.6-4B-GGUF

SparkLLM

A little over a month has passed since Spark-X2.5. In the rapidly evolving world of AI agents, that is enough time to rethink, refine, and move the frontier forward. Building on the Spark-X2.5-4B, we introduce Spark-X2.6-4B, the latest generation of our compact, general-purpose language models for on-device deployment. Spark-X2.6-4B retains exactly the same architecture as Spark-X2.5-4B, achieving its capability gains through improved post-training. It delivers substantial improvements in coding, agent, long-context, multilingual, reasoning, etc. These advances enable more capable and practical on-device AI for developers and users tackling real-world tasks. - Stronger Coding, Agentic, and…

Model · Text generation

StepFun-5-Preview-GGUF

Victor Cruz

GGUF quants of Step-5-Preview — a 604B-class sparse MoE (measured 598.04 B parameters across 1,548 tensors), 352 routed experts with top-8 routing, ~16B active, 95 layers (92 text + 3 MTP), 128,896 vocab, 1,048,576 context. This repo is private and is not a general release. The quant packs carry the text trunk — 598.04 B of the checkpoint's 602.97 B parameters. The remaining 4.9325 B ship alongside them as GGUF companion files; see Companion files. They sit outside the packs because llama.cpp's step35 implements no runtime for them, not because they were dropped. One folder per quant type, with the quant type in every file name — the convention unsloth uses, so the Hub and every downstream…

This model is a fine-tuned version of facebook/detr-resnet-50 on an unknown dataset. The following hyperparameters were used during training: - learningrate: 1e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 100 - mixedprecisiontraining: Native AMP - Transformers 4.57.6 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.22.2

This model classifies photographs as non-stairs (0) or stairs (1). It fine-tunes a pretrained ResNet-18 with AutoGluon MultiModal. Supply a pandas DataFrame with an image column of local image-file paths. predict() returns 0 or 1, and predictproba() returns class probabilities. Input resolution and expected preprocessing Prepare new photos the same way as the dataset: convert to RGB and pad to a square before resizing. Otherwise, the center crop removes the edges of non-square photos, which may cut off the stairs. Source: Stairs vs. non-stairs photographs. Published splits were used unchanged. Augmentation pipeline. Augmentation happens at two stages. The dataset stores offline variants of…

Model · Robotics

2609292357_smolvla_dice

Yoshinari Kawashima

SmolVLA is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot smolvla guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning walkthrough. - CLI…

Model · Robotics

29times

Roboseasy

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

This repository contains an instruction-tuned causal language model for text generation and chat-style prompts. Use a recent version of transformers that supports this model architecture. The following example loads the model from this repository and generates a response.

This qwen25vl model was trained 2x faster with Unsloth

This mllama model was trained 2x faster with Unsloth

Model · Image and text to text

Ablagent-35B-A3B

Blackfrost_AI

Blackfrost Pro Series is a line of specialized models packaged as professional tools and workflows. Each release is designed around a defined operational role instead of open-ended chat. 1. a domain-tuned model; 2. a deliberate operating boundary; 3. a harness or integration surface; 4. deployment guidance; 5. a validation and provenance record. This structure is intended to extend across model engineering, marketing operations, logistics, and other bounded professional workflows while preserving a recognizable Blackfrost product and documentation standard. Ablagent-35B-A3B is the Blackfrost Pro Series toolkit for Abliteration and Domain Weight Modification workflows. It helps an operator…

"Abliterated Dolphin" is a result of my 3AM brain reading about technique called abliteration and then thinking what would happen if I tried to abliterate a model that is already relatively free, such as Dolphin. Heavily inspired by mlabonne's article on abliteration on how to redirect refusals and effectively remove, or ablate, censorship from a language model. There is really no deeper meaning to any of this than pure curiosity. This is basically a dumbed down version of the original Dolphin model I used as a base, as I have not done any DPOs to heal the damage caused by abliteration. Don't try to do anything meaningful with this model. Use the original Dolphin3.0-R1-Mistral-24B instead.…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. See the full documentation at LeRobot Docs. For a complete walkthrough, see the training guide. Below is the short version on how to train and run inference/eval: Writes checkpoints to outputs/train/ /checkpoints/. Prefix the dataset repo with eval\ and supply --policy.path pointing to a local or hub checkpoint.

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

LeRobot 0.3.3 ACT policy for the RoboSynChallenge simulation task handlebasket (CobotMagic dual-arm robot, three 480x640 RGB cameras, 14-D joint state/action). - Initialised from RoboSynChallenge/ACTsimmixeroperating weights (normalisation statistics recomputed from the task dataset). - Fine-tuned for 14,000 steps on RoboSynChallenge/cobotmagicSimhandlebasket (1000 episodes), batch size 16, lr 1e-5, AMP; default ACT architecture (ResNet-18, dim 512, 4 enc / 1 dec layers, VAE, chunk size 50). - Local check (random setting, seeds 0 and 1, 10 episodes each): 11/20 successes, vs. 5/20 for the released RoboSynChallenge/ACTsimhandlebasket under the same protocol. Evaluate with the…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Model · Robotics

act_so101_pick-block-tray

Rustin

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

Action Chunking with Transformers (ACT) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates. This policy has been trained and pushed to the Hub using LeRobot. Learn how to train and run it in the LeRobot act guide, or browse the full documentation. The policy consumes these observation features and produces these action features. Inputs Outputs New to LeRobot? These guides cover the full workflow: - Install LeRobot — set up the lerobot package. - Hardware setup — assemble, wire, and calibrate your robot and cameras. - Record data & train a policy — the end-to-end imitation-learning…

AD1E adds a persistent, writable numerical memory to a frozen Qwen3.5-0.8B. Experiences change the memory state; foundation weights and the original vocabulary remain unchanged. At inference, learned continuous memory blocks enter the model's computation and the model generates answers through its original language-model head. Previous messages are not replayed into the answer prompt. This is an Apple Silicon research runtime for controlled factual associations, numerical relations, and explicitly supplied relation chains. It is not a validated general-purpose conversational memory system. The complete foundation weights, prepared encoder, portable runtime, synthetic example state, and a…

static quants of https://huggingface.co/Yunhao-Feng/AdaGuard-8B For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion. If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) Here is a handy graph by ikawrakow comparing some lower-quality quant And…

This qwen3vl model was trained 2x faster with Unsloth

Upload in progress (started October 1, 2026). The 30 full-parameter checkpoints are being published sequentially. A checkpoint is downloadable once its directory appears; please wait for checkpoint-300 before treating the full set as complete. The run initialized from the original Qwen3-8B weights, not from an earlier SFT or RL checkpoint. It completed 300 optimizer steps using AgentEvolver. Checkpoints were saved every 10 steps, from checkpoint-10 through checkpoint-300. Each directory is a standalone Transformers model with weights, configuration, chat template, and tokenizer. The weights are FP32 and approximately 32.8 GB per checkpoint. Optimizer state is not included in this…

Model

AgentWeave-Router-MiniLM

Saurav Singla

A lightweight, CPU-first semantic capability router for AgentWeave. It uses sentence-transformers/all-MiniLM-L6-v2 as a frozen embedding encoder and ranks route prototypes with cosine similarity before downstream model inference. This repository is intentionally small: it publishes the AgentWeave routing configuration, route prototypes, and executable router code while reusing the upstream MiniLM encoder at runtime instead of copying its weights. This Hub repository is a routing artifact, not a derived MiniLM checkpoint. - No MiniLM weights are included in this repository. - No fine-tuning, adapter training, quantization, merge, or weight transformation is claimed.…

This model and dataset are part of an automated AI innovation stream. Latest Discovery: AgroUlaweng Innovation: Advanced Soil Sensors integration in Vertical Farming systems for future efficiency.

Model · Text classification

ai-agent-sme-sentiment-mbert

Atsushi Hatakeyama

This is a three-class Japanese/English sentiment classifier for short hospitality reviews, fine-tuned from google-bert/bert-base-multilingual-cased. It categorizes each review as negative, neutral, or positive. The model predicts one overall label for a short Japanese or English hospitality It is intended for research, prototyping, and human-reviewed analytics. It must not be used as the sole basis for consequential business, employment, moderation, or customer-service decisions. Applications using this model should allow operators to review and correct its predictions. The model was fine-tuned on 480 synthetic bilingual reviews: 240 Japanese and 240 English, balanced across the three…

This repository contains the merged FP32 ONNX model used by @clueso/ai-text-detector, a local AI-text detector for Node.js. It is a LoRA fine-tune of wasitaigeneratedcom/ai-text-detector-small. The original four output classes and mask-aware mean-pooling architecture are preserved: The ONNX graph contains the DeBERTa encoder, mask-aware mean pooling, and four-class classifier. No remote code is required. Only the published MAGE training split was used for gradient updates. The accepted training sample contained 4,000 human and 4,000 AI documents, balanced across sources. Rank-8 LoRA adapters were trained on all 24 layers' query and value projections together with the classifier. This made…

Model

albef-demo

Yunxi Yang

Working implementation of Albef for Classification using a base configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses lamb with a step schedule. These are starting values in the script, not evidence of a…

Model

albef-multitask-sandbox

Jacob D. Walker

Working implementation of Albef for Multitask using a giant configuration. The repository focuses on transparent code and repeatable smoke tests; benchmark claims are deliberately omitted. - The Python file contains the model and runnable example or training entry point. - config.json records the generated architecture settings. - trainingargs.json records the default experiment recipe. - model.safetensors is a valid initialization checkpoint for smoke tests; it is not presented as a trained benchmark checkpoint. - No benchmark score is claimed in this repository. The included configuration uses adafactor with a onecycle schedule. These are starting values in the script, not evidence of a…

A Qwen/Qwen3-8B policy trained as a multi-turn search agent (Search-R1 style) with Process-GRPO: a process reward model (Olmo-3-7B-Think verifier) scores each turn, with per-(group, turn-position) advantage normalization and verifier prompts that include the retrieved tool responses and the gold answer. The model at the repository root is the final policy (training step 320). Intermediate checkpoints are provided under step / subfolders. - step20/ — intermediate checkpoint at training step 20 - step40/ — intermediate checkpoint at training step 40 - step60/ — intermediate checkpoint at training step 60 - step80/ — intermediate checkpoint at training step 80 - step100/ — intermediate…

Model · Text generation

AliceAI-Foundation-80B-A3B-BF16-GGUF

Amaimediacom

Local hardware: Mechrevo Kuangshi GM7AG0M — RTX 3060 Laptop 6GB GDDR6, 64GB DDR5, i7-12700H (14C/20T, 4.7GHz), Windows 11, Samsung 990 Pro. Good for imatrix and 0.6–35B-class work in RAM. 9B+ and searches need rented H200/Blackwell, typically $100 per quant. Released as part of the NOESIS Professional Multilingual Dubbing Automation Platform (framework: Deterministic Hybrid Control Framework for Frozen Neural Operators — DHCF-FNO). Repack (BF16 safetensors, shards ≤50 GB) and community GGUF quantizations of (modeltype: aliceai, 80B total / 3B active MoE with KDA layers). Исходные 49 шардов Yandex слиты в один стриминговый файл и заново нарезаны SHA256 каждого тензора, 0 расхождений (см.…

Model

AliceAI-T5-35B-A0.6B-chat-lora-v6

Kai Voss

A LoRA adapter (rank 16, alpha 16) for yandex/AliceAI-T5-35B-A0.6B, a 35B-total / 0.6B-active sparse-MoE encoder-decoder base model with no chat format. It teaches the model to chat, to call tools when a task needs them, to answer in text when it does not, and to say nothing when there is nothing to say. Every answer it gives thinks first: each non-silent training target starts with a … block. v1 ran inside an agent harness and misbehaved: a bare "hi" produced 17 to 22 parallel tool calls, "don't read any skills" was ignored, replies were empty or endless. Cause, measured: its chat format used the vocabulary's [INSTSTART], [REPLYEND], [SYSTEM], [TOOLRESULT] tokens, whose embeddings in the…

Model · Text generation

amethyst-2-mini-failed

Vertex AGI

A general-purpose chat model on Qwen3-4B that knows when to call a web-search tool, and when not to. Part of the Amethyst family. Amethyst 2 Mini (Failed) keeps a direct, conversational voice and adds reliable web-search tool calling: search when the answer depends on current information, answer from knowledge when it does not, decompose multi-part questions into several queries, and summarize retrieved snippets into a cited answer. Do not disable thinking (enablethinking=False, --reasoning off, or a hand-built prompt that ends in an empty block). The training data taught tool calls in turns that have no empty think block, so with one present the model tends to answer in prose instead of…

Model · Text classification

ami-addressee-distilbert

Josh Estrada

This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 3 - Transformers 4.57.6 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.22.2

Model · Text to video

Anannan

Taiseer Aljazi

from diffusers import DiffusionPipeline import torch pipe = DiffusionPipeline.frompretrained("Taiseer22/Anannan", torchdtype=torch.float16) pipe = pipe.to("cuda") prompt = "مدينة مستقبلية على كوكب صحراوي تحت سماء بنفسجية، سينمائي، 8K" image = pipe(prompt, guidancescale=7.5, numinferencesteps=30).images[0] image.save("artstudiooutput.png")

Model · Text generation

Anansi-35B-A3B-Q8-Prose-GGUF

Matthew Andrews

Anansi-35B-A3B balances good instruct following, narrative reasoning, and sweet prose. Specifically for my 8GB potato, because I can't run the ~30B dense models, but maybe it can be good for your potato too? This is the repo for Q8 prose/style head GGUFs. The Q8-Prose versions use the same Anansi LM output head — the DARE/TIES merge head interpolated with Melody — but restored at Q80 fidelity. The idea is to preserve more of the prose/style effect by avoiding some of the precision loss that comes from quantizing the head along with the rest of the model. There are also Q8-DarkScarlett versions using the same interpolated-head approach with ReadyArt's Dark Scarlett instead of Melody, giving…

Qwen3-0.6B fine-tuned to give a scanned document a short, useful name from the text the phone's OCR reads. It is the naming model of Anascan, an on-device document scanner: the app downloads this file once, checks its size and SHA-256, and runs it with llama.cpp on the phone. No document data leaves the phone. Input: the app's chat prompt (below) with the naming evidence it builds from OCR (headings, issuer, dates and distinct lines, at most 600 characters), thinking disabled. Output: one name in the document's language, 3–8 words, saying what the document is and who issued it or what it is about. The app checks every name on the phone (grounded in the document's words, no numbers the…