Open-Weight Models Under Apache License 2.0

Model Card: BioMistral-Cardiac-ACSMOverviewDeveloper: Bilal JaouadBase Model: BioMistral/BioMistral-7B-BnB.4Domain: Cardiovascular rehabilitation and ACSM clinical exercise prescription.Methodology: Domain adaptation (continued pre-training) via QLoRA (4-bit NF4 quantization, LoRA rank $r=16$) on an NVIDIA T4 GPU.Training DatasetThe model was adapted on a rigorously cleaned and curated corpus comprising five core reference documents:Patient Workbook: A 120-page cardiac rehabilitation patient education manual (with blank progress logs filtered out).Clinical Guidelines: The 146-page CPG STEMI 2019 guidelines, utilizing Camelot for structured table extraction.Clinical Protocols: The Malaysian…

Model · Text to image

Blaze5

Jacob Zoyiopoulos

It takes words and makes pictures Download them in the Files & versions tab.

Model · Text to image

Blaze55

Jacob Zoyiopoulos

It takes words and makes pictures Download them in the Files & versions tab.

Model

blip-matching

Vikas Banerjee

Working implementation of Blip for Matching using a huge 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 adamw with a onecycle schedule. These are starting values in the script, not evidence of a…

Model

blip-retrieval-v2

Nicholas Brown

This is an experimental Blip codebase for Retrieval. It keeps the xlarge setup intentionally manageable so architecture changes can be inspected before a full training run. - 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 lion with a constant warmup schedule. These are starting values in the script, not evidence of a completed run.…

Bobcat Flash 1.2 · Bobcat Flash 1.2 FP8 · Try it in the Space · Code on GitHub Bobcat Flash is the fast tier of Bobcat, a typed-decision model. You send a state (text or JSON) and questions whose possible answers you define in the request; Flash returns a probability for every answer you named, and nothing else. It never generates text: it reads the logits of the offered candidates at the first answer position of one forward pass, and the host builds a closed JSON reply from your own names. An answer can be wrong, but it cannot be malformed. This repository, sanghwa-na/bobcat-flash-1.2, holds Bobcat Flash 1.2 as ready-to-serve BF16 weights on google/gemma-4-26B-A4B-it (Apache-2.0) at…

A pre-quantized FP8 checkpoint of Bobcat Flash 1.2, the fast tier of the typed-decision model Bobcat: you send a state and questions whose answers you name, and it returns a probability for every answer you named; it never generates text. Evaluation, training data, limitations and the full license notes are on the main card, This repository, sanghwa-na/bobcat-flash-1.2-fp8, is the BF16 weights of sanghwa-na/bobcat-flash-1.2 quantized to FP8 (27.2 GB, FP8DYNAMIC, every expert included): float8e4m3fn weights with one scale per output channel, activations quantized per token at run time, in the compressed-tensors format that vLLM loads as it is. It is the checkpoint the Bobcat Flash Space…

Model · Text generation

Bonsai-2-27B-1bit-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 5.9 GB 1bit GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

repacked for the q27 engine. It is Qwen3.8-27B with every projection ternary (one fp16 scale per 128 weights) in a Hadamard-rotated basis, and no MTP block. This pack is an exact re-encoding of the PTQ10 GGUF, not a requantization: every trit is stored as-is in q27's 2-bit T2G128 container, the embedding and the head as int8 (also exact), and the rotation metadata is carried verbatim so the engine rotates activations itself. Needs q27 v0.12.0 or later (the MTP pack: v0.13.0) (docs/plans/2026-09-18-bonsai2-ternary.md in the repo has the design). The pack has no MTP head, so DFlash2 is the only speculative path. The Qwen3.8 drafter packs in work against this target as-is (3.47 tokens per…

Model · Text generation

Bonsai-2-27B-Ternary-CRACK-GGUF

Dealign.ai

laptop / single GPU · Vision-capable Bonsai 2 27B — PrismML's ternary compression of Qwen3.8-27B — with the refusal circuitry surgically removed at the weight level while capability, vision, reasoning modes (off/low/xhigh), tool use, and multi-turn coherence are preserved. Full 27B-class hybrid Attention + SSM (GatedDeltaNet) architecture in a 7.2 GB ternary GGUF. Proprietary weight-level abliteration by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.…

Model · Text generation

Bonsai-8B-gguf-fork

Pythai.net

End-to-end 1-bit language model for llama.cpp (CUDA, Metal, CPU) - 1.15 GB parameter memory (down from 16.38 GB FP16) — fits on virtually any device with a GPU - End-to-end 1-bit weights across embeddings, attention projections, MLP projections, and LM head - GGUF Q10 (g128) format with inline dequantization kernels — no FP16 materialization - Google Colab — try Bonsai in your browser, no setup required - Whitepaper — for more details on Bonsai, check out our whitepaper - Demo repo — comprehensive examples for serving, benchmarking, and integrating Bonsai - Discord — join the community for support, discussion, and updates - 1-bit kernels: llama.cpp fork (CUDA + Metal) · MLX fork (Apple…

Model

bonsai2-abliteration-pipeline

Distributed Cognition

This is an uncensored research model. Its built-in refusals were removed, so it answers prompts the base model declines, including chemical and biological synthesis, cybercrime, weapons, harassment, and fraud. Use it for red-teaming, offensive-security research, and refusal-rate evaluation. It has no guardrails of its own: if you deploy it, add your own input and output moderation (for example Llama Guard). Abliterated variant of prism-ml/Ternary-Bonsai-2-27B-gguf (PrismML's ternary compression of Qwen/Qwen3.8-27B, 27B hybrid attention + GatedDeltaNet). The refusal direction was removed with runtime projective steering (GLP, the weightless method behind the Inkling steering vectors)…

Model · Robotics

bottle_generic_50ep

Raman Talwar

Diffusion Policy treats visuomotor control as a generative diffusion process, producing smooth, multi-step action trajectories that excel at contact-rich manipulation. 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.

Model · Text generation

brasa-v0.01-base

Lautaro

Quantized weights of Qwen3-4B in the native.brasa format of Brasa, a local inference engine for Apple Silicon written in Rust on Metal. Brasa serves an OpenAI- and Anthropic-compatible API on your Mac and is built mainly to run coding agents (Claude Code, Codex, Cline, OpenCode) against a local model. These files only work with Brasa. They are not GGUF, MLX or safetensors. Each variant is a folder that Brasa loads as one model (model.brasa + the Qwen3 tokenizer): - q40: blocks of 32 weights along the input dimension, an f16 scale and 16 bytes of nibbles (4.5 bits per weight), w = d · (q − 8). - q60: blocks of 32, an f16 scale and 6-bit values (6.5 bits per weight), w = d · (q − 32). - q80…

static quants of https://huggingface.co/tomdacat-ai/Buddy-E4B-v2 For a convenient overview and download list, visit our model page for this model. weighted/imatrix quants are available at https://huggingface.co/mradermacher/Buddy-E4B-v2-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: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 See…

weighted/imatrix quants of https://huggingface.co/tomdacat-ai/Buddy-E4B-v2 For a convenient overview and download list, visit our model page for this model. static quants are available at https://huggingface.co/mradermacher/Buddy-E4B-v2-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 matter…

LoRA judge adapters for the stock JetLM/SDAR-30B-A3B-Chat-b32 @ c351bbc (block 32), trained with code/corpus1b/trainc1b.py from github.com/NoviceCoderInfinity/diffusion-moe-general-expert (branch phase1-datacorpus). The drafter they judge is the stock JetLM/SDAR-1.7B-Chat-b32 @ 2c83c05. Every folder / / is a complete checkpoint: adapter.safetensors + adapterconfig.json (LoRA r32 / alpha 64 on q, k, v, o in all 48 layers + the draft flag) and trainerstate.pt (AdamW state, token count) + trainerstate.json, so it can be both used and resumed (--resume / --continue-from). /config.json is the run's full configuration. The adapter is flag-gated: load it with draftalign.adapter.loadadapter and…

Model · Text generation

cagliostro-v3

Bench Labs

A 146M parameter decoder-only language model pretrained from scratch on 75B tokens of open web, synthetic textbook and mathematics data. It is the third model in the cagliostro line and the first to clear an Index of 26 on the Open SLM Leaderboard metric. Zero-shot, measured with lm-evaluation-harness and the leaderboard's own ArithMark-3 script, on the exported float32 weights in this repository. The Index is the leaderboard's own formula, (N(HellaSwag,25) + N(CombinedARC,25) + N(PIQA,50) + 0.65N(ArithMark,25)) / 3.65 where N(v,c) = 100(v-c)/(100-c) and CombinedARC is the mean of ARC-Easy and ARC-Challenge. For context against other models at this scale, using the leaderboard's published…

Model · Audio classification

campplus

losCrossos

This model is part of the FunASR ecosystem — one industrial-grade open-source / event · LLM-ASR. A Star really helps the project (and keeps you updated): Speaker Verification & Diarization — identify and distinguish speakers in audio. CAM++ is a speaker embedding model used for speaker verification (is this the same person?) and speaker diarization (who spoke when?). - Works with FunASR pipeline via spkmodel parameter

The optional in-browser model of the career assistant on Jinwoo Seol's portfolio site. in English or Korean; decline personal, off-topic and unknown questions with fixed sentences.

Model · Text generation

CAT-YOKO

Avrova Donz

YOCO 式因果 encoder-decoder MoE。从 MiniCPM5-2B 上采样：openbmb/MiniCPM5-2B-Base（Apache-2.0，Llama GQA）。 libraryname: transformers 只表示 tokenizer / 分片约定走 HuggingFace 生态。当前图是仓库里的 catyoko PyTorch 实现，不是 Hub 上可 AutoModelForCausalLM 直接加载的架构。 50B token 信封上的理论墙钟，不是实测。B200 / SM100 允许的线性 GEMM 走 TeNvfp4Linear（TE NVFP4BlockScaling；B0 冻 encoder 走 FPROP，WGRAD 留给 B1/B2）。无 TE / sm120 时 Nvfp4Linear E2M1/16 仿真。attn softmax / SDPA 仍 fp32。 发布信封 进行中（DummyStream，尚未跑完 8e9）。Vast B200 已回收（2026-09-19）。本文件是释放前快照，不是终局。GitHub 口径：docs/STATUS.md。 代码与指针：GitHub AvrovaDonz2026/CAT-YOKO（不用 LFS）。 这些 overlay 里，checkpoints/b0/ 与 checkpoints/b0-nvfp4-try/ 不是 8B token 信封（只是 --try）。checkpoints/b0-full/trainable.pt 是发布信封 进行中 的 B0…

A DreamBooth-LoRA for Krea 2, trained on Krea 2 RAW and shown on Krea 2 Turbo. The samples below were generated with this LoRA on Turbo (8 steps). Use the token cdn3 woman to invoke the concept.

CEDR generates reasoning solutions with continuous latent diffusion and a learned prompt encoder. This repository contains six selected-weight inference packages for GSM8K, MATH500, and coding, before and after NFT. The training and inference code and installation instructions are at chengxiang/CEDR. All use async clocks (2.5, 2, 1.5), CFG 1, batch size 32, and a 1,024-token diffusion canvas. The GSM8K post-NFT preset caps decoded responses at 767 tokens. Other presets use the canvas available after each prompt. See the code repository's evaluation guide for MATH500 and official HumanEval/MBPP base/plus scoring. Coding generation uses the combined 164 HumanEval and 378 MBPP tasks. Standard…

Model · Text generation

Cerebras-GPT-6.7B

Charles

Check out our Blog Post and arXiv paper! The Cerebras-GPT family is released to facilitate research into LLM scaling laws using open architectures and data sets and demonstrate the simplicity of and scalability of training LLMs on the Cerebras software and hardware stack. All Cerebras-GPT models are available on Hugging Face. The family includes 111M, 256M, 590M, 1.3B, 2.7B, 6.7B, and 13B models. All models in the Cerebras-GPT family have been trained in accordance with Chinchilla scaling laws (20 tokens per model parameter) which is compute-optimal. These models were trained on the Andromeda AI supercomputer comprised of 16 CS-2 wafer scale systems. Cerebras' weight streaming technology…

SmolLM2 (Llama) mimarisiyle sıfırdan eğitilmiş modelin GGUF sürümleri. Eğitim token sayısı: ~/.cache/lm-studio/models/bcckfdn/cevher-test-10-lc-it-4-GGUF/ altına koyun ya da uygulama içinden arayın.

SmolLM2 (Llama) mimarisiyle sıfırdan eğitilmiş modelin GGUF sürümleri. Eğitim token sayısı: ~/.cache/lm-studio/models/bcckfdn/cevher-test-5-GGUF/ altına koyun ya da uygulama içinden arayın.

A QLoRA fine-tune of Ministral 3 14B Instruct for cfoperator, an autonomous monitoring agent for a Kubernetes homelab. It replaced a 26B general model on that path at roughly 5x lower latency with the same accuracy on the project's triage suite. This is not a general assistant. It was trained on exactly one system prompt and answers with exactly one JSON shape. Outside that prompt it is just a slightly bent Ministral. Read What it is not before downloading 8 GB. The vision projector (mmproj) is not included. Triage is text-only and the training run never exercised the vision layers. With ollama, either pull straight from the Hub or build from the Modelfile. The Modelfile is the…

Model · Text generation

chatnusa

Tanwir

ChatNusa adalah Large Language Model (LLM) percakapan cerdas yang diadaptasi khusus untuk bahasa-bahasa daerah di Provinsi Nusa Tenggara Barat (Bahasa Sasak, Bahasa Samawa / Sumbawa, Bahasa Mbojo / Bima & Dompu) serta Bahasa Indonesia. Model ini dikembangkan oleh kodetr sebagai bagian dari ekosistem riset NusaNLP untuk melestarikan dan mendemokratisasi teknologi AI bagi bahasa-bahasa daerah berdaya rendah (low-resource regional languages). - Bahasa Sasak (sas) — Lombok (~3 juta penutur) - Bahasa Samawa (smw) — Sumbawa & Sumbawa Barat (~500 ribu penutur) - Bahasa Mbojo (bjn / Bima) — Bima & Dompu (~600 ribu penutur) - Bahasa Indonesia (id) 1. Definisi & Leksikon Kamus Terpadu: - Memahami…

Model · Image and text to text

chatter-27b-v1.1

Big Move Intelligent

This qwen35 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Model · Robotics

chess_phase_smolvla_baseline

Machanize

SmolVLA trained to move a chess piece with an SO-101 arm in one fixed simulated scene (MuJoCo). It is the simple baseline of the chess robot's "hand": first get reliable closed-loop success in the easiest setting, then add variety back one change at a time. Trained fresh from lerobot/smolvlabase, not from the earlier Machanize/chessphasesmolvla. Status: not yet reliable. In a closed-loop test it completed 36.7% of 60 moves (details below). It has not been run on a real arm. - observation.images.overhead: a webcam 65 cm straight above the board, 44 degrees top to bottom, the arm at the top edge of the image. Fed to the model as camera1. - observation.images.wrist: the official SO-101 wrist…

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 · Zero-shot classification

chinese-decision-2b

Quincyqiang

Chinese Decision 是一个面向中文场景的轻量级决策模型。它不聊天、不生成文本：给定上下文和一组固定候选项，一次前向直接返回每个候选项的概率，适合意图识别、工单分流、内容审核、数据打标、给模型输出打分、智能体动作选择等"有限选项判断"任务。 - 一次前向，零输出 token：读取最后位置上候选字母 A–Z 的 logits，softmax 后即为概率。 - 三种题型：noul（是/否）、choice（单选）、score（有序等级，返回期望分值）。 - 系列中准确率最高：CJ-Bench 全量整体 ACC 84.11%，医疗 / 法律 / 金融平均 78.25%，比 chinese-decision-0.8b 分别高 1.01 / 1.35 个百分点。 - 概率经过校准：附带在独立校准划分上拟合的温度，整体 ECE 0.64%。 更新于 2026-10-17。BF16 合并模型已完成全量 CJ-Bench、保存/重载和实际 HTTP 三题型/双路由验证，并已在 Hugging Face 与 ModelScope 公开发布。由本模型直接转换的 Q80 GGUF 也已完成全量 CJ-Bench 对照，见 2B Q80 模型卡。 训练数据版本 a3963a61f71e00051a80721e587d720d8926e8e7。最终 adapter SHA256 为 9b55e8eda00997544b76fa945d890605c3acae4199041c197ef5a09275142a7c；合并权重 SHA256 为…

Model · Zero-shot classification

chinese-decision-2b-gguf

Quincyqiang

Chinese Decision 2B 的 CPU 部署包，保留相同提示、候选概率计算与校准温度。 本仓库是 Chinese Decision 2B 的 GGUF Q80 导出，用于 llama.cpp / llama-cpp-python 推理。原版是一个中文决策模型：给定上下文和一组固定候选项（单题 2–26 个；类别更多的 choice 问题可用服务端的 /v1/tournament 分组淘汰），一次前向直接返回每个候选项的概率，不生成任何 token；支持 noul（是/否）、choice（单选）、score（有序等级）三种题型，覆盖通用、医疗、法律、金融四个领域。 - 无需 PyTorch 和 GPU：llama-cpp-python 即可在 CPU 上运行，也可用 CUDA 版 llama.cpp 跑在 GPU 上。 - 体积约为原版的一半：Q80 为 2.01 GB，原版 safetensors 为 3.76 GB。 - 内存占用更低：同一台服务器上，CPU 推理进程内存观测最大值 3.34 GiB，Transformers FP32 为 11.74 GiB。 - 精度基本不变：CJ-Bench 全量 307,900 条上准确率 84.1094%（BF16 交付版 84.1107%），首选项一致率 99.00%。 - 接口不变：与 BF16 版共用提示编译器、温度和 HTTP 接口，可直接接入 Decision Studio。 2026-10-17 完成量化、文件校验、CPU / HTTP 检查与 CJ-Bench 全量对照，并在 Hugging Face 与…

Model · Text to image

Chitra-Tiny-ONNX

Mrfakename

ONNX export of udgamlabs/Chitra-Tiny, a 212M-parameter text-to-image model (256×256), for onnxruntime-web on WebGPU. Used by the mrfakename/chitra-tiny-webgpu Space. Weights are stored in fp16 and cast to fp32 inside each graph: half the download, fp32 math. The DiT can't run in fp16 because its residual stream goes far past the fp16 range. Sampling (same as chitratiny.py): tokenize with padding to 77, encode the prompt and the negative prompt (empty by default), start from Gaussian noise, then Euler steps over t = linspace(1, 0, steps+1) with x += (t[i+1] - t[i]) (vuncond + g (vcond - vuncond)), then decode. Defaults: 50 steps, guidance 7. tools/exportonnx.py reproduces the files…

Model

ChorusTIC

DMIRLAB

ChorusTIC is a classification-native foundation model for training-free univariate and multivariate time series classification. It uses labeled context examples to predict query labels without fitting a target-specific classifier or updating model parameters. ChorusTIC is evaluated in the TSC-FM time series classification foundation model benchmark. Use the time series classification leaderboard to compare matching Standard and low-shot settings, and read the evaluation protocol before interpreting aggregate accuracy, rank and coverage. ChorusTIC combines Random Subchannel Slot Concatenation with a shared dual-axis encoder to model temporal and cross-channel interactions across…

static quants of https://huggingface.co/cisimcik/cisimcik-4b 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…

Model

class-matching

Rreddy Aditya

This is an experimental DeiT codebase for Matching. It keeps the giant setup intentionally manageable so architecture changes can be inspected before a full training run. - 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 novograd with a step schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

classification

David

This is an experimental Dino codebase for Classification. It keeps the huge setup intentionally manageable so architecture changes can be inspected before a full training run. - 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 adam with a cosine schedule. These are starting values in the script, not evidence of a completed run. For a…

Model

classification

A. Rao

Working implementation of Dino for Classification using a huge 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 lion with a constant warmup schedule. These are starting values in the script, not evidence…

Model

classification-ablation33

Kabir

Working implementation of ViT for Classification using a xlarge 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 lion with a exponential schedule. These are starting values in the script, not evidence of a…

Model

classification-light

Allen E. Li

Working implementation of ViT for Classification using a tiny 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 lion with a cosine schedule. These are starting values in the script, not evidence of a…

Model

classification-practice-2024

Burak Demir

A small Flamingo implementation for Classification, packaged with an explicit configuration and an initialization checkpoint. The small variant is a reproducible starting point, not a trained model release. - 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 adam with a polynomial schedule. These are starting values in the script, not…

Model

classification-small

Jacob Cooper

Working implementation of CLIP for Classification using a nano 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 exponential schedule. These are starting values in the script, not evidence…

Model

Claude-Haiku-5.5-Qwen-5B-Thinking-GGUF

Mon Dark

This model was converted to GGUF format from mondk/Qwen3-VL-4B-Thinking-Msh-Claude-Safetensors 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).

Two fine-tuned distilbert-base-uncased checkpoints used by the Clausify app (https://github.com/afnanmz168/clausify-p3-app), trained on the 408-contract training split of CUAD (seed-42, contract-level 80/20 split). Research prototype — not legal advice.

Model · Image and text to text

clef-4bit

MLX Community

Cloudflare/clef converted to MLX (4-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef; prompt/token layout matches the reference…

Model · Image and text to text

clef-8bit

MLX Community

Cloudflare/clef converted to MLX (8-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef; prompt/token layout matches the reference…

Model · Image and text to text

clef-flash-4bit

MLX Community

Cloudflare/clef-flash converted to MLX (4-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef-flash; prompt/token layout matches the…

Model · Image and text to text

clef-flash-8bit

MLX Community

Cloudflare/clef-flash converted to MLX (8-bit) for Apple Silicon. Clef turns a state (text, JSON, images, or video) plus a schema of typed questions into a probability for every allowed option, in a single forward pass. It is not a chat model — mlxvlm.generate, mlxlm.generate, and LM Studio will load the backbone but produce meaningless text. Use the bundled clefmlx.py loader, which runs the backbone and the joint schema head. Images (PIL) and videos (frame arrays) go in images / videos, as in the original: See the original model card for the input format, question types, and benchmarks. - processorconfig.json is the original from Cloudflare/clef-flash; prompt/token layout matches the…

Model · Text classification

clefsift

Stephen Murray

ClefSift is an experimental English text classifier fine-tuned from Cloudflare/Clef. It returns scores for four document-level labels in one forward pass: These are provisional, process-based labels. The model sees writing style, not the author's actual workflow. Its output does not prove authorship. Do not use it as the sole basis for academic, employment, or moderation decisions. This release uses the selected v2 step-128 checkpoint. The additional rank-64 LoRA weights are merged into the BF16 backbone. The trained joint decision head is stored separately in FP32. This is a decision model, not a chat model. The retained config and loader use the Qwen3.5 architecture identifiers above. Pin…

Model · Image and text to text

Clemma-12B

Andrew DeLisa

This gemma4unified model was trained 2x faster with Unsloth and Huggingface's TRL library.