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AI Models 2119

  • plant-disease-dinov2-small↗

    rodynaemad

    Identifies **38 crop–condition classes** (26 diseases and 12 healthy classes across 14 crops) from a photo of a single leaf. Built as the open disease-scanning model for BloomTech, my AI + IoT smart-agriculture graduation project. PlantVillage contains several photos of the **same physical leaf**. If photos are split randomly, near-duplicates of a test leaf end up in training, which inflates test accuracy. The dataset authors' `leaf-map.json` links 40,490 photos to 7,524 physical leaves. I split the data so that **every photo of a given leaf lands on only one side** (stratified, leaf-grouped 80/20 split), and asserted that no leaf crosses the split. For this model the inflation from a naive split is small (99.1% vs 98.9%), but the leaf-level number is the one to trust. The remaining 13,815 photos have no leaf mapping and are split photo by photo. `onnx/model_with_features.onnx` (88 MB, fp32) and `onnx/model_with_features_quantized.onnx` (24 MB, int8) take `pixel_values` (1×3×224×224, ImageNet-normalized) and return `logits` and the 768-d `features` used for the out-of-distribution check. The fp32 export matches PyTorch exactly. The int8 copy, which powers the Space, scores **98.7% accuracy (98.1% macro F1)** on the full leaf-level test set. Example with onnxruntime-web in the Space's `index.html`.

  • bert-base-arabertv02-twitter↗

    aubmindlab

    AraBERTv0.2-Twitter-base/large are two new models for Arabic dialects and tweets, trained by continuing the pre-training using the MLM task on ~60M Arabic tweets (filtered from a collection on 100M). The two new models have had emojies added to their vocabulary in addition to common words that weren't at first present. The pre-training was done with a max sentence length of 64 only for 1 epoch. **AraBERT** is an Arabic pretrained language model based on Google's BERT architechture. AraBERT uses the same BERT-Base config. More details are available in the AraBERT Paper and in the AraBERT Meetup

  • pocket-tts-GGUF↗

    cstr

    Pocket TTS is a lightweight (~100M param) continuous-latent autoregressive TTS model from Kyutai, based on the CALM paper (arXiv:2509.06926). Unlike codebook-based TTS models, Pocket TTS emits continuous float vectors — no discrete tokens, no softmax sampling: - **FlowLM backbone** — causal transformer (1024D, 16 heads, 6 layers; 24 layers for the French preview, RoPE, GELU) operating at 12.5 Hz - **Consistency head** — SimpleMLPAdaLN (512D, 6 ResBlocks) with timestep embedding → one-step LSD decode → 32-dim continuous latent vectors - **Mimi VAE decoder** — SEANet upsample convolutions + 2-layer transformer → 24 kHz PCM - **Mimi VAE encoder** *(voice-cloning builds only)* — SEANet downsample + 2-layer transformer + speaker projection → reference conditioning - **Text tokenizer** — SentencePiece BPE (4000 vocab, embedded in GGUF) Single GGUF file — no separate codec companion needed (Mimi weights and the tokenizer are embedded). Pocket TTS is zero-shot: it clones the timbre of a short reference clip. The **voice-cloning builds** (the files *without* `novc` in the name) embed the Mimi VAE **encoder** and speaker projection needed to condition on a reference; the English-only `novc` builds omit that encoder and are ~20 MB smaller.

  • GLM-5.3-Flash-ewaste-edition-GGUF↗

    SixVolts

    This is a quantization of GLM-5.3-flash that is optimized to work better on older hardware. This quant was built using i-matrix GGUF quantizations with the same principle as the GLM-5.2 and GLM-5.3 e-waste editions: **K-quants for the routed experts, no codebook i-quants**, because on pre-AVX-512 CPUs and previous-generation datacenter GPUs (MI100 / gfx908) expert dequantization, not bandwidth, is the decode bottleneck. **Format: mainline llama.cpp `glm5-next`** (merged 2026-09-30, ggml-org/llama.cpp#27773). These files do **not** load in builds that only know the earlier `glm5next` arch from the unmerged PR #27754 (unsloth's September files are that format). Any llama.cpp from 2026-10 on loads them. The NextN/MTP head (blk.45) is in every file for when mainline's MTP support (#27917) lands. All are imatrix-quantized with unsloth's GLM-5.3-Flash importance matrix (88 chunks × 9216 tokens) from an **F32 intermediate** of the FP8 checkpoint (`fp8 × block scale` in F32; a BF16 intermediate re-rounds most weights because the scales are arbitrary floats). Tensors that mainline's quantizer keeps unquantized for this arch (hyper-connection mixers, DSA indexer, KDA gates/betas, `attn_kv_a_mqa`) are F32 in every file; `attn_q_a/q_b` and `nextn.eh_proj` are Q8_0 (mainline's floor). The MTP head's experts are Q4_K in every variant.

  • cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-base-q3c↗

    agurung

    Trained and validated on the **cobalt-train ≤2/64 frontier** (canonical clean_eval prompts): 1833 train / 112 held-out val problems the base model solved on at most 2 of 64 samples under the iid_canonical@64 hardness scan. Val evals sample at temperature 1.0 (matching the clean_eval frontier eval). Reward signal: **binary code-correctness** (1.0 if the generated program passes the problem's tests, otherwise 0.0). This checkpoint is the **`main`** revision (git branch) of the repo, with the model at the repo root — load it directly, no subfolder:

  • minimax_h3_pdmd_2nfe_comfyui↗

    Iwannapose

    ComfyUI-format conversion of the PDMD 2-NFE LoRA — the 2-step (2 NFE) student distilled from MiniMax-H3-33B with Projected Distribution Matching Distillation, rank 128, covering attention projections and both feed-forward layers of all 50 transformer blocks + 2 token-refiner blocks. (Companion repo for the 4-NFE variant: `Iwannapose/minimax_h3_pdmd_4nfe_comfyui`.) The two are numerically identical (the source fp32 file is an exact cast of the bf16 one); the bf16 file is the one to use. Both are larger than the source because the fused `qkv_proj` stores the block-diagonal zero padding explicitly. Source keys are Diffusers PEFT names (`transformer.transformer_blocks.N.attn.to_q.lora_A.weight` …); target keys follow the ComfyUI H3 layout (same scheme as the proven `minimax_h3_fl2v_turbo_8step_v1.0_768p_comfyui_bf16.safetensors`):

  • Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF↗

    Sameer3336s

  • Wan2.1-VACE-1.3B-GGUF↗

    samuelchristlie

    Wan2.1 is an open-source suite of video foundation models, compatible with consumer-grade GPUs, that excels in various video generation tasks like text-to-video, image-to-video, and video editing, even supporting visual text generation. This model is a derivative work of the original model licensed under the Apache 2.0 License, and is therefore distributed under the terms of the same license. Thanks to Patrick Gillespie for creating the ASCII text art tool used in this project https://patorjk.com/software/taag/

  • Ternary-Bonsai-2-27B-PTQ1_0-MTP-GGUF↗

    sudoingx

    One line installs it on the card you already own, RTX 20, 30, 40 and 50, and AMD Radeon in beta, with 8 GB or more: it picks the build and the model file for your VRAM, checks every SHA256 and starts it, with the chat page at `http://127.0.0.1:8899` and an OpenAI-compatible API at `/v1` (model `bonsai-2-27b`). Windows, in PowerShell (measured on native Windows on an RTX 3060 12GB and an RTX 5070 Ti 16GB; RTX 20 in beta): AMD Radeon (beta): the same two lines, on PrismML's own Vulkan build, no ROCm to install. 8 GB cards get up to 64K, 12 GB about 195K, 16 GB and up the full 262K with vision, all from the files in this repo; the cards and the numbers are in Radeon (beta).

  • gemma-4-26B-A4B-it-qat-AWQ-INT4↗

    cyankiwi

    Hugging Face | GitHub | Launch Blog | Documentation | Technical Report > [!Note] > This model card is for the new versions of the Gemma 4 family optimized with Quantization-Aware Training (QAT), which allows preserving similar quality to bfloat16 while dramatically reducing the memory requirements to load the model. > Four versions of the QAT checkpoints are available: > * **Unquantized QAT checkpoints** (Q4_0): Half-precision weights extracted from the QAT pipeline, ideal for custom downstream compilation and research. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B, and their drafter models. > * **GGUF** (Q4_0): Ready-to-deploy formats for broad ecosystem compatibility. Available for Gemma 4 E2B, E4B, 12B, 26B A4B, and 31B. > * **Mobile-optimized** (wNa8o8): A custom schema engineered explicitly for mobile hardware efficiency. It features targeted 2-bit decoding layers, optimized KV caches, and static activations to maximize VRAM savings. Available for Gemma 4 E2B and E4B. > * **Compressed Tensors** (w4a16): QAT checkpoints serialized in the compressed-tensors format for native, optimized inference with vLLM. Available for Gemma 4 E2B, E4B, 12B, and 31B. > * **Assistant Compatibility**: When using multi-token prediction (speculative decoding) with an assistant model alongside a QAT target model, the assistant model must also be a QAT checkpoint with the same precision to ensure compatibility. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.