Open-Weight Models Under Apache License 2.0

A merged (LoRA-finetuned) variant of Qwen/Qwen2.5-1.5B-Instruct, fine-tuned for Cisco-related assistant tasks. This repository contains full merged weights — not a PEFT adapter. Load it directly with transformers; no peft and no adapter merging step is required. The model is tagged endpointscompatible and can be deployed directly to for a dedicated, OpenAI-compatible /v1/chat/completions API. Direct use: [e.g. answering Cisco product, configuration, and troubleshooting questions.] Out of scope: This model is not production-grade for safety- or compliance-critical networking decisions. Verify all configuration and command output before applying it to real hardware. Do not use it as the sole…

Nautilus 独立判读侧现役判分器(LoRA on Qwen3-1.7B)。 - 三态判读 pass / fail / insufficientevidence,J1 binary acc 88.51%(生产评测,ECE 0.072) - 部署纪律(PRECOR B/C 实测 291 题,2026-10-07):只允许 bf16/fp16——fp16 对 bf16 判定一致率 1.0000,int8 0.9828 FAIL,int4 0.9416 FAIL(禁用) - 判定表:docs/metering/PRECORBCVERDICT20261007.md(nautilus-compass 主仓) - 归属:NACRE 机制·判读实例 v1(机制名+实例版本号,组织对齐账 2026-10-06)

Model · Text generation

NadevA23_Kronumos-Kairos-v2-GGUF

Bartowski

Using llama.cpp release b11259 for quantization. Don't know which to choose? Grab Q4KM (4.78GB) - usually a good mix of size and performance. Download instructions available here First, make sure you have the Hugging Face CLI installed: These quants run with llama.cpp - installable in one line via llama.app: llama-server includes a built-in chat web UI, served at http://localhost:8080 by default. These quants were made with llama.cpp release b11259 - if this model's architecture is newly supported, you'll need that release or newer to run them. They also work in: quants made using imatrix option, with a calibration corpus rendered through this model's own chat template. The corpus pairs…

NAJY 11단계 ACT 체크포인트 (1호기 DGX1 학습). 분석·채점용 업로드 — 실기 배포 후보 확정과는 별개. 받은 뒤: sha256sum model.safetensors 가 위와 같은지. 채점 도구는 같은 폴더의 multimanifest.json 에서 플래그를 읽는다 (--checkpoint; pretrainedmodel 구조 그대로이므로 stageconddiag.py 등에 바로 쓴다). 배경: trossen-ai-simulation docs/mobilebaseinvestigation.md §94.

NAJY 11단계 ACT 체크포인트 (1호기 DGX1 학습). 분석·채점용 업로드 — 실기 배포 후보 확정과는 별개. 받은 뒤: sha256sum model.safetensors 가 위와 같은지. 채점 도구는 같은 폴더의 multimanifest.json 에서 플래그를 읽는다 (--checkpoint; pretrainedmodel 구조 그대로이므로 stageconddiag.py 등에 바로 쓴다). 배경: trossen-ai-simulation docs/mobilebaseinvestigation.md §94.

NAJY 11단계 ACT 체크포인트 (1호기 DGX1 학습). 분석·채점용 업로드 — 실기 배포 후보 확정과는 별개. 받은 뒤: sha256sum model.safetensors 가 위와 같은지. 채점 도구는 같은 폴더의 multimanifest.json 에서 플래그를 읽는다 (--checkpoint; pretrainedmodel 구조 그대로이므로 stageconddiag.py 등에 바로 쓴다). 배경: trossen-ai-simulation docs/mobilebaseinvestigation.md §94.

NAJY 11단계 ACT 체크포인트 (1호기 DGX1 학습). 분석·채점용 업로드 — 실기 배포 후보 확정과는 별개. 받은 뒤: sha256sum model.safetensors 가 위와 같은지. 채점 도구는 같은 폴더의 multimanifest.json 에서 플래그를 읽는다 (--checkpoint; pretrainedmodel 구조 그대로이므로 stageconddiag.py 등에 바로 쓴다). 배경: trossen-ai-simulation docs/mobilebaseinvestigation.md §94.

Model · Text to image

nanites-keypunch-z-image-1.0

Nanites

LoRA adapter for Z-Image (Tongyi-MAI, Apache-2.0), fine-tuned on 20 public-domain reference stills of mid-century data instrumentation and paperwork (FSA/OWI, Harris & Ewing, National Photo Company, HABS/HAER surveys) in the idiom of 1930s-1960s U.S. government documentary film stills. Stark black-and-white: punched cards, tabulating machines and card sorters, strip-chart recorders, dial gauges, ledgers, dense tabular reports. Harsh light, deep blacks, film grain. Use keypunch in your prompt to invoke the idiom. See SOURCES.md in the repo for the per-file rights basis.

Model · Text generation

nanites-smoky-1b-chat

Nanites

A Llama-3.2-1B-Instruct model fine-tuned to play the role of an old resident of the Great Smoky Mountains of the 1930s in conversation. You talk to it; the old-timer answers in period voice with regional vocabulary. This is the chat-tuned sibling of the CosyVoice2 gender-pooled speech voices (Earl / male at Nanite-Labs/nanites-smoky-speech-earl, Ethel / female at Nanite-Labs/nanites-smoky-speech-ethel). The chat model provides the language behind the TTS; pair them for a text-and-speech experience. Given a modern-English question about life in the Smokies (work, family, food, music, religion, hardships, the mountains themselves), the model answers in 1-3 sentences in the voice of an old…

Model · Text generation

nanites-smoky-1b-chat-gguf

Nanites

Q4KM quant of the merged nanites-smoky-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays an old resident of the Great Smoky Mountains of the 1930s. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-smoky-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt

Model · Text generation

nanites-whaler-1b-chat

Nanites

A Llama-3.2-1B-Instruct model fine-tuned to play the role of a 19th-century American whaling first mate in conversation. You talk to it; the mate answers in period voice. This is the chat-tuned sibling of Nanite-Labs/nanites-whaler-1b, which is a text completer that continues 19th-century whaling logbook entries. The chat version is a different animal — don't mix them up. Talk to it like a sailor would talk to the mate on deck. It answers in the voice of a 19th-c. ship's mate, brief and direct, using "try-works", "by D.R.", "the Captain", "no speak of it", etc. Multi-turn is supported — the mate sees your prior Q&A in the context window and can refer to earlier exchanges. period voice) + 8…

Model · Text generation

nanites-whaler-1b-chat-gguf

Nanites

Q4KM quant of the merged nanites-whaler-1b-chat model — a Llama-3.2-1B-Instruct fine-tune that plays a 19th-century American whaling first mate. For Ollama / LM Studio / llama.cpp users who don't want the base-model + adapter merge step. Adapter (train output) lives at Nanite-Labs/nanites-whaler-1b-chat; this repo holds the merged + quantized GGUF for direct use. Alpaca-style (### Instruction: / ### Input: / ### Response:). Example prompt

Four checkpoints, not one model. This repository is a controlled experiment: two training arms × two seeds, 10,000 steps each, identical in every respect except how packed training rows are masked. The comparison is the artifact. Any single checkpoint here is a weak 230M research model and is not meant for use. Each arm lives on its own branch: Weights are fp32, exactly as trained — these are the tensors every number below was computed from. Optimizer state is not included. nanoVLM packs several unrelated samples into one 4096-token row to avoid padding waste. Upstream's packing applies a single causal mask over the whole row, so a sample attends into the earlier, unrelated samples packed…

Model · Text ranking

nb-asr-qwen06b-bokmal-nynorsk-reranker

Nasjonalbiblioteket AI Lab

This repository is under construction. No selected, validated model has been released yet. This model was created by Thea Tollersrud and Per Egil Kummervold in the NB-ASR project. The planned model recognizes equivalent Bokmål–Nynorsk text while detecting changed numbers, pronouns, entities, and participant roles. Meaning-preserving differences and reorderings are accepted. Training starts from Qwen/Qwen3-Reranker-0.6B and uses revision 619e36b85e4943f23cd85b66edcde331995e4872. Validation is automatic, without manual review. Accuracy will be conditional on the supplied positive pairs and the automatic corruption rules. See trainingstatus.json for the latest recorded job status. A complete…

This gemma3 model was trained 2x faster with Unsloth

This gemma3 model was trained 2x faster with Unsloth

weighted/imatrix quants of https://huggingface.co/prithivMLmods/NeoHorse-1-4B-Max-Heretic-Uncensored-TURBO For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/NeoHorse-1-4B-Max-Heretic-Uncensored-TURBO-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…

static quants of https://huggingface.co/OpenIntelligenceNet/NeoHorse-1-9-Uncensored 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…

游戏 UGC 评论「语义单元抽取」模型。基于 Qwen/Qwen3-4B，LoRA（r16 / alpha32 / dropout0.05）训练后合并进底座，fp16 权重，可直接用 transformers 加载，无 peft 依赖。 - 任务：从一条评论文本中抽取语义单元（mention / span / occurrence），只做抽取，不做分类 - 硬件：实测峰值显存 8.82 GB，T4/T10（16GB，Turing）可直接部署，attention 走 sdpa，无需 FlashAttention2 生成参数已固化在 generationconfig.json：贪心解码（dosample=False）+ maxnewtokens=2048，请勿改动，否则与评测结果不一致。 ok=False 表示输出 JSON 解析失败或存在非法 span（mention 不在正文 / occurrence 越界），此时 semanticunits 为已通过校验的子集；线上建议对 ok=False 记录走兜底（重试或降级）。 - JSON 成功率 98.99%，非法 span 0 - SDK 与评测输出平价验证：20/20 DEV 样本单元集合完全一致 1. dtype 必须 fp16：Turing 架构无 bf16 支持；Ampere+（A10/A100/4090）可改用 bf16 3. 时延：A100 mean 4.8s / p50 3.8s 每条；Turing 预计 8~15s，随单元数线性增长 4. 输入 text 建议 ≤ 1024 token（训练 maxseq…

This model and dataset are part of an automated AI innovation stream. Latest Discovery: NexusUlaweng Innovation: Advanced Blockchain integration in AI systems for future efficiency.

static quants of https://huggingface.co/dancingfrog/Nezou-27B-CyberAbliterated For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Nezou-27B-CyberAbliterated-i1-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…

weighted/imatrix quants of https://huggingface.co/dancingfrog/Nezou-27B-CyberAbliterated For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Nezou-27B-CyberAbliterated-GGUF This is a vision model - mmproj files (if any) will be in the static repository. 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…

weighted/imatrix quants of https://huggingface.co/Nock-AI/nock-coder-1.5b For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/nock-coder-1.5b-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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

weighted/imatrix quants of https://huggingface.co/OS-Software/North-Mini-Code-1.0-Uncensored-Heretic For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/North-Mini-Code-1.0-Uncensored-Heretic-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

oac-ops-health-v2

SZL Holdings

This package is a deterministic Python standard-library kernel and a JSON table of coefficients fitted on generated transport counters. It is not a transformer checkpoint or adapter; it is not a clinical model or result service. It accepts eight bounded operational fields and proposes ALERT, NOALERT, or ABSTAIN for operator attention. Every authority flag is false: it cannot acknowledge a transport message, control a device, interpret a result, release a result, or make a care decision. Do not submit patient information, laboratory results, specimens, orders, or device identifiers. The four executable/data files in this repository are byte copies of ops-health/v2/ops-health/ at the…

Model

Occamy-1.0-oQ6e-mtp

Qinghua

本仓库是什么：把官方 occamy-1.0（35B-A3B MoE + 视觉编码器）与官方 occamy-1.0-MTP 草稿头合并进同一份权重（mtpnumhiddenlayers: 1），再用 oMLX 的 oQ6e 量化导出，专供 Apple Silicon 上的 oMLX 推理。非官方发布，未经 Accio-Lab 验证。 - 实测（本机真实使用日志，113 次请求，Mac Studio M4 Max 64GB）：草稿接受率 77.95%（48,501/62,218），分深度 d1 79.4% / d2 76.3% / d3 77.3%；中位 2.83 tokens/cycle；中位解码 110.5 tok/s（p10 78.0 / p90 146.9）。工具调用类结构化输出接受率 85.7%，自由文本 75.8%。 - 量化配方：oQ6e = oMLX 通用动态量化，6-bit 基准（专家层 6-bit/group 64；语言部分 ≈6.6 bpw，整文件含视觉塔 ≈6.7 bpw）；嵌入、注意力/GDN 投影、共享专家提升到 8-bit；全模型 norms、MoE 路由、MTP 融合层与整个视觉塔保持未量化；imatrix 校准（oqecodemultilingual，128×512，专家覆盖 100%）。 - 体积：30.28 GB（28.20 GiB），6 个分片；64GB 统一内存可运行（实测占用约 29 GB）。 Upstream ships the MTP draft head as a separate artifact that must be…

OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility…

OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility…

OD-1 is an open System One decision model: give it a state (text or JSON) and typed questions (choice, noul = yes/no, score = ordinal) and it returns every answer from one forward pass, with full probability distributions including an explicit NOTANSWERABLE outcome. The request/response shape follows the Jev / TypeSafe /v1/systemone wire format. Backbone: Qwen/Qwen3.5-0.8B (Apache-2.0) with typed answer heads read at marker tokens, early-exit heads (layers 6/12/18), and a CLM-style contrastive shortlist head for questions with more than 24 options. Trained with SFT + distillation from Qwen3.5-27B-FP8 (never a compared system), RLCD-style proper-score calibration, an abstention utility…

One street photo in, a structured Japanese observation record out. OddEye is the street-reading model of michiyomi. It is built on Qwen3.8-27B and compressed to 4-bit (NVFP4, about 20 GB), so a single GPU — even an RTX 5090 — can run it. It keeps what it sees (observations) and what it infers (interpretations, each with a confidence label) in separate fields. The record has 13 top-level fields, including objects, text on signs, relations, surfaces, standard road facets (lanes, sidewalks, lighting…), interpretations and a scene summary. All numbers use the recommended prompt (v5.2c), greedy decoding and the same photos for every model. "Generic NVFP4" is a publicly distributed 4-bit version…

Engineered & Architected by Suryaansh Prithvijit Singh (@Ultron28 / @Ultron09) The ULTRON Financial Conformer (1.189B) is a large-scale foundation model specifically designed to ingest raw minute-level OHLCV candlestick time series across liquid US equities and generate multi-horizon continuous returns and directional probability forecasts. Unlike conventional time-series transformers (e.g. PatchTST, Chronos, TimesFM) that treat financial candlesticks as 1D scalar sequences, this architecture treats the microstructure as a continuous multi-scale physical manifold. It combines: 1. 6-Scale Temporal Inception Tokenizer: Continuous 1D convolutions spanning $[3\text{m}, 7\text{m}, 15\text{m}…

Model · Image to text

ohmyshot-ppformulanet-plus-l-coreml

Morax Cheng

PaddlePaddle's PP-FormulaNetplus-L, which reads a picture of a formula as LaTeX, converted to Core ML ML programs with 8-bit weights for OhMyShot, a screenshot app for macOS, which downloads them as a model pack to copy the formula in a selection as LaTeX. The weights are PaddlePaddle's, quantized; see LICENSE.txt for the license and what was changed. Decoding starts from token 2 at position 0 and ends when the decoder gives 2. Run on the CPU (.cpuOnly). Made with Tools/model-packs/formulapack.py from this upstream revision: - https://huggingface.co/PaddlePaddle/PP-FormulaNetplus-Lsafetensors/tree/6cc004a8e91ea3f3819b05317f40b290cbe99c2b Checked against the OhMyShot formula corpus…

The model weights of the LoRA / QLoRA runs done on the L40S server during the Warsaw Model Trainers Hackathon (25-26.09.2026): PEFT adapters, Trainer checkpoints (with optimizer state), lora.gguf adapters for llama.cpp and trainingargs.bin. These are adapters, not full models. Every run uses the base google/gemma-4-12B-it-qat-q40-unquantized (the local path in adapterconfig.json points to a server copy of it). Layout: l40s-results/ /..., the same paths as oldwb/l40s-results/ in the GitHub repo Endote/WarsawModelTrainersHackathon, which holds the logs, metrics, data statistics and write-ups (README.md, EXPERIMENTS.md, BENCHMARKSRESEARCH.md). Restore into a checkout of that repo: python…

基于 MikeLee000/olmo3-190m-zh-full 完成一轮持续预训练（Continued Pretraining，CPT）的课程实验模型，约 187M 参数。 不是 SFT 或指令对话模型。 使用普通文本作为提示进行续写，不使用 applychattemplate，不应将其作为可靠的问答助手。 - 架构：Olmo3ForCausalLM，12 层，hidden size 768，intermediate size 3072，词表 48,000。 - 数据：cmz1024/llm101-olmo3-zh-demo-data 仓库中的 continue/tokenizedv31.bin。 - 全参数持续预训练；不使用 LoRA；序列长度 2,048；BF16。 - 1 epoch，共 11,918 个 optimizer steps。 - Batch size 16，gradient accumulation 4，有效 batch size 64。 - Learning rate 2e-4，warmup ratio 0.1，cosine scheduler。 - Transformers 5.9.0，PyTorch 2.5.1。 - 最终 validation loss：1.9467759132385254。这是特定验证集上的语言模型损失，不是准确率或通用问答质量评分。 发布文件来自 /checkpoints/olmo3zhcontinue/final，已核对与 checkpoint-11918/model.safetensors 的 SHA-256 一致：…

DPO checkpoints from a study of why DPO makes Olmo 3 7B Instruct overconfident on multiple-choice questions. Every model here is trained from allenai/Olmo-3-7B-Instruct-SFT on Ai2's Dolci-Instruct-DPO preference pairs with Ai2's open-instruct code. - Code, results and the write-up ("Why Does DPO Make Olmo 3 Overconfident?"): These are research checkpoints for reproducing the study's measurements. They were not tuned or safety-tested for use. "Agreement-weighted" DPO multiplies each pair's loss by sigmoid(-5 x the SFT model's preference margin), rescaled to average 1, so pairs the SFT model already gets right count less. Each folder is a full model (model.safetensors, config, tokenizer, chat…

static quants of https://huggingface.co/prithivMLmods/OneDecision-VisionGuard-4B-SFT 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…

static quants of https://huggingface.co/prithivMLmods/OneDecision-VisionGuard-9B-SFT 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…

Model

onwx

Ryu Gyo

LLM を AMD Ryzen AI の NPU（XDNA2）だけで 動かすアプリです。行列計算から DeltaNet・アテンション・LM ヘッドまで、 1 トークンの計算をすべて NPU の上で行います（CPU は単語の表引きと次の単語の選択だけ）。NPU 向けのプログラムは OpenAI 互換 API サーバーとブラウザのチャット画面、タスクトレイのアプリ（モデルのダウンロード・切り替え・設定）を備えています。 AMD Ryzen AI の NPU で LLM を動かす代表的なアプリ FastFlowLM（FLM）1.0.8 と、 同じ測り方で比べました（Qwen3.5 9B、Ryzen AI Max+ 395、NPU は performance モード）。プロンプトは FLM の flm bench と同じ文章（長さ 128〜32K トークン）で、プロンプトを読み終わるまでの時間（TTFT）と、そのあと 32 トークンの生成を測っています。 点線は、設定の「CPU も使ってプロンプトの読み込みを速くする」をオンにした値です（既定はオフ）。 生成速度はどちらも MTP なし（温度 0 で 1 トークンずつ）の値です。FLM の Qwen3.5 には MTP がないので、条件はそろっています。 onwx の MTP（温度 0 のときの先読み）を使った値は、生成のグラフに破線で示しました。 - 512 トークンの回答をプロンプトから書き終えるまでの実効速度（512 ÷ 合計時間）は、128〜32K のどの長さでも onwx が上です（MTP なしでも）。…

Model · Token classification

open-text-detector-qwen36-35b-a3b-oct10-gguf

Alice

GGUF files of the Open Text Detector token classifier, the model that scores every paper on opentextdetector.com. It detects AI-written and AI-edited text in English research papers, at the level of single tokens and sentences. These files are the LoRA adapters of woog/open-text-detector-qwen36-35b-a3b-oct10 (final-moe, Oct 10, 2026) merged into Qwen/Qwen3.6-35B-A3B (revision 995ad96), converted with llama.cpp (commit 3e390af18d) and quantized. This is not a chat model. Text generation is not what it was trained for. The GGUF gives the final hidden state of every token; the included token head (tokenhead.safetensors, one linear layer from 2,048 to 2 outputs) turns each hidden state into a…

This is a relativesplinepi05 policy trained with LeRobot. This policy has been trained and pushed to the Hub using LeRobot. See the full LeRobot 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 cheat-sheet — quick reference for the lerobot- commands. The short version to run and train this policy: Replace the remaining placeholders with your own values: --robot.port and…

static quants of https://huggingface.co/alanhuangya/OpenJev-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…

OpenJevX is an open-weight, non-autoregressive System One decision model specialized from Laya, which uses answerdotai/ModernBERT-large plus a dynamic typed-decision head. It accepts runtime-defined choice, score, and noul questions and returns calibrated probabilities in one forward pass. It is compatible with the TypeSafe Jev /v1/systemone request shape through the OpenJevX server. - CUDA p50 latency per five-question case: 22.8 ms The benchmark uses the untouched 400-case, 2,000-decision test split from LocalLLaMA/typed-decisions. Training uses only its 1,200-case train split. OpenJevX is a derivative of Laya by ConvAI Innovations and ModernBERT by Answer.AI and LightOn. Laya and…

Model · Text classification

OpenThai-SystemOne-Ollama

iApp Technology

OpenThai-SystemOne is an open Thai + English System One decision model (0.8B, Apache-2.0). It does not generate text: given a state (text or JSON) and typed questions it returns probabilities: choice between named options, noul (yes/no) and score on an ordered scale. This repo is its Ollama build for Ollama's System One API (POST /v1/systemone, Ollama ≥ 0.35), the same API Ollama serves Nimble and Tev1 with. Everything runs on your machine; no API key. The curl example below uses the hf.co name; with the ollama.com pull, use "model": "iapp/openthai-systemone". (Probabilities rounded. The whole request is part of every question's prompt, so the same question can score slightly differently…