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AI 模型 2119
- RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-GGUF↗
deadbydawn101
> **The most comprehensive open-source security agent model — in GGUF.** Runs in Ollama, LM Studio, llama.cpp, vLLM, and any GGUF runtime. 51/51 LoRA tensors merged. Identical to the MLX version. **Both versions are identical** — same 51/51 LoRA tensors, same 745K+ training data, same 12 training rounds. **People are NOT getting the most out of local LLMs.** A 35B MoE at Q4_K_M gives dramatically better output than a 7B model at the SAME speed — because only 3B params activate per token.
- GLM-5.3-UNCENSORED-EXL3-3.0bpw↗
Infatoshi
EXL3 quantization of dealignai/GLM-5.3-UNCENSORED-FP8, itself a weight-edited (no fine-tune) variant of zai-org/GLM-5.3. The edit is documented in `CRACK_SURGERY.json` (copied unchanged from the source repo). This repo is not affiliated with dealignai or Z.ai. tau2-bench (airline, retail), Pass^1. Agent temperature 1.0, top_p 0.95; user simulator and judges GPT-4.1 at temperature 0. The reference is stock GLM-5.3 (not the uncensored edit) served at FP8 by Z.ai via OpenRouter, run through the same harness. The difference therefore mixes the dealign weight edit and this quantization. Per trial: airline baseline 0.740 / 0.680, quant 0.600 / 0.680; retail baseline 0.465 / 0.544, quant 0.482. ± is one binomial standard error. Neither difference is statistically significant at these sample sizes; treat the airline point estimate as a possible small regression rather than a measured one.
- sentence-t5-base↗
sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model works well for sentence similarity tasks, but doesn't perform that well for semantic search tasks. This model was converted from the Tensorflow model st5-base-1 to PyTorch. When using this model, have a look at the publication: Sentence-T5: Scalable sentence encoders from pre-trained text-to-text models. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-base model. The weights are stored in FP16.
- SmolLM3-3B-trellis-3inst-4bpw-kernel↗
xv0y5ncu
**GLQ (4.0 bits/weight)** quantization of `HuggingFaceTB/SmolLM3-3B` using a **trellis-coded codebook (QTIP TCQ) + randomized Hadamard transform (RHT) + LDLQ**. Weights stay compressed in memory and are decoded on the fly by a fused CUDA kernel. > **Where GLQ shines:** the size-vs-accuracy win is strongest at **2–4 bits/weight**. For even more headroom, combine it with the **E8 KV cache** (≈4× smaller KV cache → longer context in the same VRAM). > **Requires `glq >= 0.7.0`.** This checkpoint stores the trellis in the kernel > (MMA-fragment) layout that the lookup-free 3INST CUDA kernels consume > (`trellis_layout: "kernel"` in `config.json`); older glq versions cannot decode it.
- qwen-image-2.1-qma-camera-angles↗
gribbliest
Give it an image and a camera position. It renders the same subject, scene and style from the new angle. Orbit all the way around, raise or lower the camera, or pull in for a close-up. This is an edit LoRA for Qwen Image 2.1, so it always needs a source image. Directions are relative to the original camera, not the subject. "Right side view" means the camera swings 90 degrees to its own right around the subject. "Back view" takes it all the way around behind. Rather than typing prompts, you can drag a camera around to build them with the QMA Multi-Angle LoRA Node by CoreyCorza.
- SGuard-JailbreakFilter-2B-v1↗
SamsungSDS-Research
- colSmol-256M↗
vidore
ColSmolVLM is a model based on a novel model architecture and training strategy based on Vision Language Models (VLMs) to efficiently index documents from their visual features. It is a SmolVLM extension that generates ColBERT- style multi-vector representations of text and images. It was introduced in the paper ColPali: Efficient Document Retrieval with Vision Language Models and first released in this repository This version is trained with the commit b983e40 of the Colpali repository. (main branch from the repo) Our training dataset of 127,460 query-page pairs is comprised of train sets of openly available academic datasets (63%) and a synthetic dataset made up of pages from web-crawled PDF documents and augmented with VLM-generated (Claude-3 Sonnet) pseudo-questions (37%). Our training set is fully English by design, enabling us to study zero-shot generalization to non-English languages. We explicitly verify no multi-page PDF document is used both *ViDoRe* and in the train set to prevent evaluation contamination. A validation set is created with 2% of the samples to tune hyperparameters.
- Spark-X2.5-cmf↗
infosave
Spark-X2.5-4B and Spark-X2.5-1.7B (hybrid attention: three 512-token sliding-window layers per full layer, 1M-token context, thinking mode, tool calling, 200+ languages) packaged as single CMF files. They run with `cortiq`, a Rust inference engine with no Python and no ML framework: NVIDIA, AMD and Intel GPUs through Vulkan/DX12, Apple silicon through Metal, and the CPU. Use cortiq 0.8.12 or later; prebuilt binaries for Linux, macOS and Windows are on the releases page. The engine picks the GPU and its settings itself. `--no-think` asks for a direct answer; without it the model reasons in a ` ` block first, so raise `--max-tokens` (default 256). All files are quantized from the bf16 checkpoints. Perplexity: the first 4096 tokens of wikitext-2 test with the BOS token in front; the unquantized models score 10.61 (4B) and 14.49 (1.7B; transformers in float32 gives the same 14.49). Spark is sensitive to 4-bit attention and down projections, so only gate/up are 4-bit in the compact files.
- GLM-5.3-Flash-NVFP4↗
tattrongvu
NVFP4 (4-bit float, group size 16) quantization of `zai-org/GLM-5.3-Flash`, covering the routed experts of **every** MoE layer — including layer 45, the multi-token-prediction (MTP) layer — in a **single, uniform** scheme. **No calibration data is used in this quantization step**, and none is needed: the scheme quantizes each group of 16 weights with a scale derived from those 16 values (`memoryless_minmax`), so there is no forward pass for calibration data to inform. The checkpoint contains no activation-scale tensors and declares `input_activations: null`. `num_hidden_layers: 45` plus `num_nextn_predict_layers: 1` puts the MTP layer at index **45**, and it is not a small head: 288 routed experts, ~7.25 B parameters, its own DSA attention and `eh_proj`/`enorm`/`hnorm` plumbing. Keeping it in the same scheme as every other expert layer means the checkpoint has one expert layout throughout — 37,152 packed expert tensors (43 × 288 × 3).
- gemma-3n-E2B-it-litert-preview-GGUF↗
unsloth
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3n models are designed for efficient execution on low-resource devices. They are capable of multimodal input, handling text, image, video, and audio input, and generating text outputs, with open weights for pre-trained and instruction-tuned variants. These models were trained with data in over 140 spoken languages. Gemma 3n models use selective parameter activation technology to reduce resource requirements. This technique allows the models to operate at an effective size of 2B and 4B parameters, which is lower than the total number of parameters they contain. For more information on Gemma 3n's efficient parameter management technology, see the Gemma 3n page. With instruction-tuned models, you need to use chat templates to process our inputs first. Then, you can pass it to the pipeline.