Text Generation Models

Model · Text generation

vnpen-writer-2b-v0.1-preview

VNPen LLM Project

VNPen is MewBaka Studio's visual-novel model series. The writer edition is for script writing, de-AI rewriting, and generating example scenes from a mood brief. Output format is one script line per line: speaker：text, with narration written as 旁白：. The base Qwen/Qwen3.5-2B is multimodal. Its checkpoint carries 297 model.visual. tensors (a depth-24 / hidden-1024 / patch-16 ViT) and 15 mtp. tensors for multi-token prediction. This project is text-only. In transformers, AutoModelForCausalLM on a qwen35 config builds Qwen35ForCausalLM over a Qwen35TextConfig — so the vision weights were never loaded at any point: not for training, not for merging, not for saving. Verified on the published…

Model · Text generation

wag-2b

Sky

a small chat model that talks like a puppygirl and still answers the question. fine-tuned from Qwen/Qwen3.5-2B (Apache-2.0). wag speaks in lowercase, soft, playful puppyspeak — wan~, awoo, arf,:3, the occasional ears perk — and underneath that gives you a real answer. the voice is the product, but the whole point was that it doesn't come at the cost of being useful. the failure mode this was built to avoid is collapsing into pure noise: a model that barks charmingly and tells you nothing. the eval below scores helpfulness and voice separately so that tradeoff stays visible instead of hiding behind vibes. llama.cpp / LM Studio / ollama — grab a quant from gguf/ and go. wag-q4km.gguf is the…

Model · Text generation

WaifuGemma4-26b-a4b-v1

HiWaifu Research

Gemma 4 26B-A4B, post-trained with GRPO against a reward model learned from 1.2 million double-blind votes cast by HiWaifu users inside their own role-play conversations. Put back into the same arena, blind, it met GLM-5.1 in 1,430 battles and won 49.6% of the decided votes; against a 13-model field including Gemini, DeepSeek-v4 and Qwen's character models it won 54.7%. Most open role-play models are tuned on preferences that come from an LLM judge, from a handful of annotators, or from synthetic pairs. We had something rarer: a live arena where, inside ordinary chats on our platform, a user is occasionally shown two candidate replies and asked which one they want to continue with. Those…

Исторический идентификатор ветви и файлов — Vol2. WAL2 использует новую исходную Qwen3.8-FP8; старый WAL RC1 на Qwen3.6 со sparse/WALB2 не является parent этих весов. Основной свежий артефакт этого снимка — E022-Fixed128, потомок E020-B1024. Завершённая диагностика: 69/136 (50,74%), лучше родителя 64/136 по сумме, но AIME25 0/30 против 2/30. E020-B1024 сохранён отдельно как контрольный кандидат. Утверждения о превосходстве E022 по всем задачам нет. Полный GSM8K 84,69% относится только к E017. Слабое AIME25 и регрессии более новых вариантов сохранены в отчёте. Артефакт текстовый. Визуальная часть исходной conditional-generation модели не преобразовывалась, не обучалась и не выпускается.…

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

This package uses the standard Transformers Llama causal-language-model architecture with OpenWALDO's schema-1 byte tokenizer. Load the tokenizer with trustremotecode=True. BOM.json inventories every release file and EU-BOM.json contains the EU GPAI training-content disclosure mapping.

Walker is a decoder-only language model that adds trajectory modeling to the Transformer. Attention asks "what should I look at?" by comparing queries with keys: \text{Attention}(Q,K,V)=\text{softmax}\!\left(\frac{QK^\top}{\sqrt{d}}\right)V Walker keeps attention and asks a second question: "where is my representation going?" Treat the hidden states x₁, x₂, x₃, … as a path. Walker measures how that path moves: This is a discrete derivative of the hidden state. Attention averages over content. The difference signal captures direction and change, which averaging does not. Attention can look around the path. Walker also walks along it. Figure 2. The hidden states form a path. Walker reads its…

A allenai/Olmo-3-7B-Instruct 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 360). 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/…

Model · Text generation

Wicked-Tutu-26B-A4B-v0.26

Ready.Art

/ Import a cleaner font / / Light Mode Palette / / Dark Mode Palette - High Contrast Neon / --bg-pink-light: #1a050d; / Deep burgundy / --bg-pink-dark: #0a0205; / Near black / --glass-bg: rgba(30, 5, 15, 0.6); / More transparent for neon effect / --glass-border: rgba(255, 105, 180, 0.5); / Brighter border / --text-main: #ffb6c1; / Light pink text / / Main Container - Glassmorphism Effect / / Animated Border Glow / background: linear-gradient(45deg, var(--primary-pink), transparent, var(--secondary-pink), transparent) border-box; / Header Section / / Waifu Image Container / / Content Sections / / List Styling / / Disclaimer Section / / Badge Styling / / Fix for Hugging Face's own dark mode…

Ask anything, get a how-to article. WikiQwen-4B is Qwen3.5-4B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…

Ask anything, get a how-to article. WikiQwen-9B is Qwen3.5-9B fine-tuned to answer every message the same way: as a tidy, step-by-step how-to article in markdown, with a picture caption above each step. Hand those captions to WikiQwen-Illustrator and they become illustrations. Send it a question ("how do I keep basil alive?"), a problem ("my bike chain keeps falling off") or just "hi", and it sends back a guide. Every reply has the same shape: - a # How to … title and a short intro - one or more ## Method N: or ## Part N: sections, or a single ## Steps - an [IMAGE: caption] line above each step, written for the Illustrator to draw - steps written as N. Bold summary. Details…, with bullets…

Model · Text generation

Winery-Qwen3.8-Flash-Next-Cuvee-GGUF

Winery

One blend of Qwen3.8-Flash-Next (125B MoE, 512 experts, 10 active) in three bottles, sized so it runs on a gaming PC with Winery Strata. That's a fork of Strata with a wine-red glass UI, where Cuvée is the first model in the installer. - Short thinking from Swift 1.5 (UkisAI) - Refusals poured out by Huihui's abliteration Visit the Cuvée Space for the expert-cellar map and a bottle picker for your RAM. All three keep the dense weights in Q6K/Q80 and the 28.8 GB PLE table in IQ4NL (Strata reads it from the SSD). They were quantised with Unsloth's imatrix for Flash-Next. Slim drops half the experts, the same move as ISTA-DASLab's Coder. The difference is that it keeps the 256 experts routed…

Wrench is a pruned, task-specific developer-tool SLM derived from Qwen3.6-35B-A3B. It contains 3,881,244,016 parameters and stays below the 4.25B parameter ceiling. This is a public experimental artifact. It is downloadable and reproducible, but it is not a claim that the final 4M retrieval-quality or MiniMax-parity gates have passed. The canonical distribution format is Hugging Face Safetensors. The package embeds the tokenizer hook, deterministic mechanical lookup runtime, long-context overlay, hash-bound package metadata, and the FreeToken launcher. It is intended to feel like one model directory, not a separately installed harness. The bundled launcher requires a compatible FreeToken…

YANchor-4B is a general-purpose recurrent model that preserves crucial memory as ANchors for retrieval during subsequent reasoning. Its multidimensional memory mechanism enables effective long-horizon reasoning with O(N) generation time and O(1) history-state memory. For example, on challenging math problems, YANchor achieves 82.93% mean pass@1 on AIME 2024–2026 and 63.64% on HMMT, substantially outperforming linear-time, constant-state counterparts, including 7B–14B models. YANchor delivers several-fold higher batched long-generation throughput than Transformer and hybrid baselines on H100. Evaluations across dozens of benchmarks demonstrate its superiority in general-purpose capabilities.…

Model · Text generation

yejin-korean-8b-v3-cpt6000-distill-qwen36-lora-lr2e4-nothink-v2

Hajunho

Korean instruction-following language model. Built from Yejin 8B v3 (pretrained from scratch by Yejin: cooler8/yejin-korean-8b-v3-base): continued pretraining for 6,000 steps on public Korean data, Yejin chat vector from our own SFT model, then LoRA (rank 64, lr 2e-4). The LoRA data mixes our phase-1 format data (45,013 samples) with 39,290 short Korean solutions written by Qwen/Qwen3.6-27B (Apache-2.0) for questions from our own puzzle generator, KMMLU train, AI Hub school exams and AI Hub reading comprehension; only solutions that reached the known answer were kept (sequence-level distillation; no teacher weights or logits). No benchmark test items were used, and the teacher solutions…

This model is a fine-tuned version of Qwen/Qwen2.5-3B-Instruct. It has been trained using TRL. This model was trained with SFT. - PEFT 0.21.0

English · 中文 ds4 is a native C/CUDA inference engine plus an offline toolchain that runs DeepSeek V4.1 Flash on a single NVIDIA DGX Spark (GB10, 128 GB unified memory). Everything described here is our own work: the vector-quantization format, the solvers that produce the sidecar and the post-training file, the CUDA kernels, and the rulers we judge all of it with. The model architecture is DeepSeek's and is not re-explained here — read the official release for that. How to read this page. It unfolds in five steps; stop wherever you have what you need. 1. The numbers — what runs, how fast, how close to the original. 2. Three convictions — why the system is shaped this way. 3. The…

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).

weighted/imatrix quants of https://huggingface.co/ZenithLLM/zen-ingress-1-3b-dpo-merged For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/zen-ingress-1-3b-dpo-merged-GGUF 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 here are Artefact2's thoughts on the matter…

Model · Text generation

Zenyx-MCEN-Delta-Mamba

Anamitra Sarkar

A ~565M-parameter language model that interleaves Mamba-3 (MIMO) and Gated DeltaNet-2 (GDN-2) sequence mixers with two MCEN stages (a reversible fixed-point "reasoning" core with a delta-rule memory), trained from scratch on the Zenyx-V3 pretraining mixture with the DeepSeek tokenizer. This repository holds training checkpoints, logs and diagnostics of an ongoing run; it is not a finished release. Data mixture — 121 sources in the frozen manifest (data/manifestv2.json, created 2026-10-10 06:04:26, hash 9454e840c2b9f032…); grouped shares: The comparison models are the same author's: Zenyx-v2-base (85M unique parameters, 32 effective layers, 32k vocabulary) and Zenyx-V3-Base (1.58B-parameter…

Model · Text generation

Zenyx-V3-Base

Anamitra Sarkar

Zenyx V3 is an efficient 1.5B-parameter Mixture-of-Experts (MoE) foundation model built for low-latency inference and high throughput. It is written from scratch in JAX/Flax and trained on TPU v5e-8. active per token, with a Sinkhorn transport-based gate. latent subspace, cutting HBM bandwidth and memory footprint. stability at scale. YaRN and RoPE scaling factors are precomputed so context can be extended at inference time beyond the trained length. All tasks are evaluated with the standard base-model protocol: the model scores the log-likelihood of every candidate continuation and the highest-scoring one is taken as the answer. Nothing is generated and no output parsing is involved, so…

This model is a fine-tuned version of francesca9805/ppt-wc-zipf-newlex-zho-before-100mb-packed-bfdisoseed455. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of francesca9805/zho-hans-10mb-ppt-Dp-100mb-packed-bfdisoseed10. It has been trained using TRL. This model was trained with SFT.

This model is a fine-tuned version of francesca9805/zho-hans-10mb-ppt-Dp-10mb-packed-bfdisoseed3407. It has been trained using TRL. This model was trained with SFT.

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 · Text generation

zoey

Dakuwon Moody

Status: experimental test completed; continuous background mode is disabled. The test did not change the serving checkpoint, app, or model weight files in this repository. This update documents the experiment only. A separate controller completed two successive coach → learner cycles without chat input, then stopped at its configured limit. It supplied a planning request and a follow-up, retaining the actual first coach answer as conversation history. The second lesson drafted with the checkpoint learned from the first. The student used the existing Map5/SEE memory and linear reranker, plus the frozen FP32 GPT2-medium chooser. The external coach was Qwen/Qwen2.5-32B-Instruct, revision…

Model · Text generation

Zrald-AI-model-quant-qwen-3.8-27b

Gerald Bustilla

High-efficiency, hardware-tested GGUF releases of Qwen3.8-27B (27 Billion Parameters, Dense Architecture). All models in this repository have been physically converted, verified on hardware (AMD Instinct MI300X with ROCm / HIP), and benchmarked for prompt throughput, token generation velocity, and benchmark accuracy against baseline models. This repository provides three specialized model tiers: - zraldv1-ac (Accuracy-Priority Tier): 17.08 GiB (18.3 GB). Near-lossless retention (99.68% accuracy), matches or outperforms standard Q6K and Q80 quality while saving ~10 GB VRAM compared to Q80. - zraldv1-ba (Balanced Sweet-Spot): 14.46 GiB (15.5 GB). Optimal balance (99.12% accuracy), fits…

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-ac

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Accuracy Priority Tier. This repository contains zraldv1-ac.gguf (17.08 GiB / 18.34 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-ba

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Balanced Sweet-Spot Tier. This repository contains zraldv1-ba.gguf (14.46 GiB / 15.52 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.

Model · Text generation

Zrald-AI-qwen-3.8-27b-zraldv1-cs

Gerald Bustilla

Official high-efficiency GGUF release of Qwen3.8-27B optimized for Compressed Size Tier. This repository contains zraldv1-cs.gguf (10.18 GiB / 10.93 GB), physically benchmarked on AMD Instinct MI300X hardware. For cross-comparison tables against standard Q80, Q6K, Q5K, Q4K, and Q2K models, visit the master repository: - Base model by the Qwen Team (Alibaba) under Apache 2.0. - Runtime by Georgi Gerganov and the llama.cpp community.