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AI 모델 2119

• Qwopus3.5-9B-Coder-GGUF↗

  Jackrong

  As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance **🤖 Agentic Coding, complex Tool Calling, and logical reasoning.** > 💡 **Why the 9B Dense Model?** > We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.** > [!TIP] >**Vision & Tool Calling Support**: This model supports visual capabilities and tool calling. To enable vision, please place the `mmproj.gguf` file from the GGUF repository into the same directory as the main `.gguf` file.

• kotoba-whisper-bilingual-v1.0↗

  kotoba-tech

  _Kotoba-Whisper-Bilingual_ is a collection of distilled Whisper models trained for - **Japanese ASR** - **English ASR** - **Speech-to-text translation (Japanese -> English)** - **Speech-to-text translation (English -> Japanese)** developed through the collaboration bewteen Asahi Ushio and Kotoba Technologies. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model for Japanese and English ASR, while we translate the transcription into English and Japanese by external LLM to obtain training dataset for speech-to-text translation. We employ ReazonSpeech for Japanese ASR and Japanese speech to English text translation, and Multilingual LibriSpeech for English ASR and English speech to Japanese text translation. Kotoba-whisper-bilingual's loss objective consists of cross-entropy on both of ASR and translation tasks, while KL divergence loss only for ASR task. The student model consists the full encoder of the teacher large-v3 model and the decoder with two layers initialized from the first and last layer of the large-v3 model. As kotoba-whisper uses the same architecture as distil-whisper/distil-large-v3, it inherits the benefit of the improved latency compared to openai/whisper-large-v3 (**6.3x faster than large-v3**, see the table below taken from distil-whisper/distil-large-v3).

• Qwen2.5-Math-RM-72B↗

  阿里巴巴 · Qwen

  Qwen2.5-Math-RM-72B is specifically designed to guide the Qwen2.5-Math model throughout the training process by offering more granular feedback on the quality of reasoning and intermediate steps, ultimately facilitating more robust model improvements. - Multilingual and Multi-Modal Support: Offers preference signals across two languages (Chinese and English) and in dual modes (Chain-of-Thought and Tool-integrated Reasoning), enhancing versatility. - Model Training Guide: - Training Data Enhancement: Employs a data selection process via reward model scoring combined with Rejection Sampling to incrementally enhance the quality of responses - Reinforcement Learning Training: Integrates seamlessly into the reinforcement learning training and provide effective reward signal, further improving model performance.

• BirdNET_v2.4↗

  sammlapp

• DeepSeek-V4.1-Flash-1017-FP8-GGUF↗

  AMAImedia

  ⚡ Each donation funds the next large quant. I host free GGUF or MoE quants as independent research. 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: DHCF-FNO — Deterministic Hybrid Control Framework for Frozen Neural Operators) **DeepSeek-V4.1-Flash-1017** is a minor update to **DeepSeek-V4.1-Flash**, delivering substantial improvements across reasoning, coding, agentic tasks, and visual benchmarks. Alongside these gains, the model reduces task execution time and output length, enabling users to complete tasks faster and at lower cost and advancing the performance–cost Pareto frontier across multiple agent harnesses.

• Tristan_-_dclm-perplexity-correlations-spearmanr-no-samp-160m-gguf↗

  RichardErkhov

• Gemma-4-12B-a4B-MoE↗

  stamsam

• RVN-Qwen3.8-Flash-Next-Abliterated-Uncensored-IQ3_S-Strata↗

  jimmy31chen

  A **Strata-ready** repackaging of `0bserverx/RVN-Qwen3.8-Flash-Next-Abliterated-Uncensored-GGUF` (IQ3_S quant family): 1. **`RVN-Qwen3.8-Flash-Next-IQ3_S-merged.gguf`** — the original 8-shard *split* GGUF merged into a **single file** (83 GiB), ready to feed directly to a Strata native pack. 2. **`pack/`** — a **pre-built Strata pack** (from the same version of `iq_pack.py --compat-bf16` shipped in Strata 0.1.30). Point `--pack` at it and Strata reads the experts/PLE table straight from the merged GGUF — no merge step, no pack step, no calibration needed for a first run. The upstream RVN release ships as 8 shards, and some transformer layers' expert tensors are **split across shard boundaries**. Strata's `tools/iq_pack.py` requires the gate/up/down expert tensors of each layer to live in the *same* file, so the upstream files fail to pack (`layer 1: its gate/up/down tensors are in different shards`). This repo provides the bit-identical merge so Strata users can use RVN out of the box. Experts layout: gate/up = `IQ3_S`, down = `IQ4_NL` (both natively supported by Strata's MMQ kernels). PLE n-gram table = `IQ4_NL`, read directly from the merged GGUF.

• MiniMax-M3.1-Flash-Maya-GGUF↗

  peasantsmith

  GGUF quantizations of MiniMaxAI/MiniMax-M3.1-Flash (428B parameters, 23B active per token: 128 routed experts per layer, 4 active, plus a shared expert; sparse attention in every layer; up to a 1M-token context), made for **Project Maya**. Project Maya keeps the most-used experts on the GPU and computes the rest from RAM, so the model runs well below its size. Both quants are made from MiniMax's own release (FP4 experts, FP8 dense weights), not from a re-quantized file, and keep every expert. Measured on held-out conversations never used for calibration, on the model's own answers, against the original model's predictions (MiniMax's released weights, decoded exactly), with the files as published running in Project Maya's engine - the same results on NVIDIA and AMD. 1. **Calibration text** in MiniMax's own chat format, thinking included: tool calls and agent traces, code (with three.js / canvas / WebGL and other front-end work), reasoning, long answers, chat, multilingual chat - 384 sequences of 2,048 tokens. A separate held-out set is kept for evaluation. 2. **Statistics from the model itself**, layer by layer: an importance matrix for every expert separately. 3. **Where the bits go:** every layer's gate, up and down matrices were measured in each candidate type, and the extra bits went to the layers where the model is most sensitive. 4. **Error-feedback rounding** of every expert, at the quantizer's 256-weight blocks, each expert against its own input statistics - and each layer calibrated on what the already-quantized layers before it produce. 5. **llama.cpp's own quantizers (ggml)**, so the files are ordinary GGUFs in llama.cpp's `minimax-m3` layout.

• parrot_paraphraser_on_T5↗

  prithivida

  Parrot is a paraphrase based utterance augmentation framework purpose built to accelerate training NLU models. A paraphrase framework is more than just a paraphrasing model. For more details on the library and usage please refer to the github page **Huggingface** lists 12 paraphrase models, **RapidAPI** lists 7 fremium and commercial paraphrasers like QuillBot, Rasa has discussed an experimental paraphraser for augmenting text data here, Sentence-transfomers offers a paraphrase mining utility and NLPAug offers word level augmentation with a PPDB (a multi-million paraphrase database). While these attempts at paraphrasing are great, there are still some gaps and paraphrasing is NOT yet a mainstream option for text augmentation in building NLU models....Parrot is a humble attempt to fill some of these gaps. **What is a good paraphrase?** Almost all conditioned text generation models are validated on 2 factors, (1) if the generated text conveys the same meaning as the original context (Adequacy) (2) if the text is fluent / grammatically correct english (Fluency). For instance Neural Machine Translation outputs are tested for Adequacy and Fluency. But a good paraphrase should be adequate and fluent while being as different as possible on the surface lexical form. With respect to this definition, the **3 key metrics** that measures the quality of paraphrases are: - **Adequacy** (Is the meaning preserved adequately?) - **Fluency** (Is the paraphrase fluent English?) - **Diversity (Lexical / Phrasal / Syntactical)** (How much has the paraphrase changed the original sentence?)