Open-Weight Models

Model · Text generation

Longleaf-35B-A3B-Abliterated

Red Hills Media

The abliterated version of Longleaf, our local model for coding, repository edits, debugging, and structured tools. - 35B total / 3B active parameters. - MLX 4-bit weights. - 262,144-token configured context. EvalPlus 0.3.1, 164 tasks, pass@1. Thinking off, greedy sampling, 2,048-token output cap, 262,144-token configured context. Generation used our M1 Ultra 231 / 198 / 195 tokens, respectively. CLI sample: five Terminal-Bench 2 tasks on ARM64, thinking off. Per task: 300 seconds, 24 model calls, 32 tool calls, 2,048 output tokens per call. Apache 2.0. This release contains modified model weights, based on Qwen3.6-35B-A3B, and the Abliteration changes refusal behavior, not output…

Model · Text generation

LoopPolicy-27B-LoRA

EnvLoop

1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0) that answers typed decision questions for a personal agent with long-term memory, with calibrated probabilities. Code, pre-registration, results and technical report: - Unpacked from the frozen training output qwen-adapter.tar.gz, SHA-256 6b07a7d36eba59b9cfd9cf440bdc5783f4fc12d4d726cfa5beef2462860d8d81 (per-file hashes in release-manifest.json). - Frozen as the final candidate after two hill-climbing rounds; passed the one-shot pre-registered HIDDEN gate; then compared once with five other models on a public TEST set. LoopPolicy is the Hugging Face release name of the PersonaLoop research artifacts: a calibrated personal-agent policy built through…

This repository contains the actual final checkpoint files for three HUGE-Bench 5-epoch experiments, plus complete evaluation results for the pi0.5-base reproduction and our final VLA using the fine-tuned Qwen3-VL-2B backbone. Each evaluated model has all 993 videos and trajectories. Results are organized under the respective experiments/pi05hugebench/ and experiments/lowaltitude3dllm/ directories. Training, evaluation, model conversion, and environment setup code: The separate pi0.5 experiment with current-state input is paused and has no final checkpoint in this repository. Base weights and intermediate training checkpoints are outside this upload's scope. The two StarVLA.pt files are…

weighted/imatrix quants of https://huggingface.co/Cyclone-Labs/Lucent-Witch-31B For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Lucent-Witch-31B-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 thoughts on the…

static quants of https://huggingface.co/noeme/Lura-1.3-500m 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 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).

LoRA adapter 已融合进基座，权重是 bf16 全精度，没有经过任何量化。 MLX 和 transformers 都能直接加载——它是标准 safetensors 格式，所以一套权重两种用法。 一句话：要在它身上"再加工"，就必须用这个版本；只是想用它，4bit 更省。 命令行也行（mlxlm.generate 自己会套模板）： 和 4bit 版完全一致——模型输出裸 JSON： 完整接入代码（含多步循环、JSON 兜底解析）见主仓库的 1. 不套 chat template 会复读。 直接 generate(model, tok, prompt="递归是什么？") 会得到类似这样的东西： 这不是模型坏了，是你喂的是裸补全而不是对话。用 tok.applychattemplate(...)， 或者干脆用 mlxlm.generate 命令行。 2. enablethinking 记得显式传 False。 模板里有 开关，不传的话没有默认值，输出可能带游离的 。 想要思考内容就传 True，它会单独出现（不混在回答里）。 3. 加载比 4bit 慢、占内存多——这是正常的。 实测对比（MacBook Pro M4 / 16GB）： ① 无工具时算术会算错 → 在 system 里要求"一律调 calculate"； ② 通用 system 下身份可能答错 → system 里写清身份； - 基座：MiniCPM5-2B（面壁智能 OpenBMB） - 许可：Apache 2.0 个人项目，欢迎提 issue。有用的话 Bilibili 关注一下

Model

MAI-UI-2B-Q4_0-GGUF

Yongsheng Yan

This model was converted to GGUF format from Tongyi-MAI/MAI-UI-2B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Install llama.cpp through brew (works on Mac and Linux) Invoke the llama.cpp server or the CLI. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with LLAMACURL=1 flag along with other hardware-specific flags (for ex: LLAMACUDA=1 for Nvidia GPUs on Linux).

A character-level causal Transformer trained on the literary corpus of Goswami Tulsidas MANAS is a small, experimental character-level causal Transformer language model trained on a Devanagari literary corpus focused on the works of Goswami Tulsidas — the 16th-century poet-saint and author of the Ramcharitmanas. Unlike modern large language models that operate on subword tokens (BPE, SentencePiece), MANAS processes text one Unicode character at a time. The model was built entirely from scratch in PyTorch, without any pretrained weights or transfer learning, as an educational experiment into whether a small Transformer can learn the statistical patterns of classical Awadhi poetry from raw…

Model · Text generation

MANN-GPT

Wuff Mann

因果 MANN · 全局记忆 v2 · 动态思考网络（DTN） MANN-GPT v6 is a memory-augmented autoregressive language-model architecture built around causal MANN layers, a formally qualified Global Memory v2 path, and a Dynamic Thinking Network (DTN). This repository is the frozen architecture and code archive for the first formally qualified bilingual continuation line. MANN-GPT v6 是一种记忆增强型自回归语言模型架构，其核心由 因果 MANN 层（causal MANN layers）、已完成正式资格验证的 Global Memory v2 全局记忆路径，以及 动态思考网络（Dynamic Thinking Network, DTN） 构成。本仓库保存首条完成正式资格验证的中英双语继续训练线路所对应的冻结架构与代码归档。 The canonical Phase-1 checkpoint passed value qualification, Global-Memory causal-identity retention, exact Q2 preservation, fixed-D3 retention, strict autoregressive…

Model

MERNIK

Wepiqxx

MERNIK ("the one who measures") is the evolution of the ASHQ1 battlefield zoo: fewer utility duels, more verdicts. The method (priority queue) is built; MERNIK is how we prove anything about it. Think of it as a finetune of ASHQ1: same base weights (queue, pins, tied groups), retrained objective (measure-first protocol, three-column verdicts) — and far more capabilities on top (slow capability ring, KLD-aware teachers, relief ceilings, the zoo bench, norms shields). But the legend is not forgotten: every scar in the ledger traces back to it. Allocator signals never certify. Capability scores never steer. Mixing them is how the PPL disease happened. - PPL = canary. Cheap, lies by sharpening.…