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AI 모델 2119

  • gemma-3n-E2B-it-unsloth-bnb-4bit↗

    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.

  • Llama-3-Kimodo-GGML↗

    LocalAI-io

    Native GGML/GGUF text encoder used by Kimodo. This is the reusable LLM2Vec encoder only; download a matching Kimodo diffusion model separately, for example `Kimodo-SMPLX-RP-v1-GGML`. From a kimodo.cpp checkout with the Hugging Face CLI installed, install both with: The downloader installs the recommended Q8_0 variant by default. Select a different one with `--text-quantization bf16|q8_0|q6_k|q5_k|q4_k|q4_k_m`. Every variant is one monolithic weight GGUF and uses the same `tokenizer.gguf`. Kimodo reads individual tensor ranges from the monolith, so streaming one layer at a time does not require separate per-layer files.

  • Krea2-Turbo-Distill-4step-LoRA↗

    lvladikov

    > 🆕 **New: a 3-step LoRA** takes Krea 2 Turbo from 8 steps down to 3, at three quarters of this LoRA's denoising time, with more fine detail (fine texture 1.15× the 8-step teacher's on its 22 test prompts). This 4-step LoRA stays the closest to Turbo's own 8-step pictures. **Half the steps · ~1.6× faster end to end · 45% of the gap to the 8-step teacher closed · texture at 1.03× the teacher's, verified clean · teacher preferred on only 6 of 45 judged renders · 12 trained resolutions · 78,000 training samples · 21 days on one RTX 3090.** A LoRA for **Krea 2 Turbo** that reduces the minimum usable step count from **8 to 4** — Turbo's own model and sigmas, half the denoising passes, and the fine texture that 4-step Turbo loses put back.

  • NVIDIA-Nemotron-3.5-Bolt-74B-A9B-Uncensored-GGUF↗

    AtomicChat

    NVIDIA's refusal behaviour projected out of the released weights, baked into Atomic Dynamic GGUF builds and measured against the original. On held-out JailbreakBench prompts refusals fall from 97.5% to 0%, and with thinking on from 30 of 30 to none (2 of 30 on the 22 and 29 GB files). MMLU stays where it was: 81.35% before, 81.75% after (2000 questions, McNemar p = 0.29). Tool calls and long context are unaffected. You can now run the Uncensored Nemotron 3.5 Bolt in Atomic Chat , locally on your own machine. Made from the full-precision release, not from NVIDIA's 24 GB file, and measured on one stand against the other public Bolt abliterations: at 22 GB ours is the closest to the original and the only one that neither refuses with thinking off nor breaks into repeated text ( table ). The refusal is written into the residual stream by every kind of block: the edit covers the Mamba-2 outputs, the attention outputs, the shared experts and the latent projection through which all routed experts write. Same Atomic Dynamic layouts and importance matrix as our Nemotron 3.5 Bolt quants ; each file is the size of the original build with the same name. *28 Oct 2026: re-split from 2 GB shards: `AD-IQ2_XS` and `AD-IQ1_M_S` are now a single file each, `AD-Q4_K_M` two files cut at 45 GB. The weights are unchanged (tensor-for-tensor identical).*

  • CodeNinja-1.0-OpenChat-7B↗

    beowolx

    CodeNinja is an enhanced version of the renowned model openchat/openchat-3.5-1210. It having been fine-tuned through Supervised Fine Tuning on two expansive datasets, encompassing over 400,000 coding instructions. Designed to be an indispensable tool for coders, CodeNinja aims to integrate seamlessly into your daily coding routine. CodeNinja maintains the same prompt structure as OpenChat 3.5. Effective utilization requires adherence to this format: The simplest way to engage with CodeNinja is via the quantized versions on LM Studio. Ensure you select the "OpenChat" preset, which incorporates the necessary prompt format. The preset is also available in this gist.

  • Qwen3.8-Flash-Next-MLX-Serve-iQ-MLX-3.3bpw↗

    ddalcu

    mlx-serve pack of Qwen/Qwen3.8-Flash-Next, the Qwen4 preview architecture (`model_type: qwen4_exp`), quantized to 3.3 bits per weight with imatrix calibration so it fits a 64 GB Mac with room for 64k of context. 52 GB resident. Includes the MTP head and the vision tower (image and video input). Measured against the bf16 checkpoint on 1200 held-out next-token positions (agent, code, prose, math; documents the calibration never saw). Top-1 is how often the pack's greedy token equals bf16's; mass is the bf16 probability of the pack's pick, with bf16's own pick as the ceiling. Same positions, same server, cold prefills. The 3.3 bpw pack keeps 99% of the 4-8 pack's probability mass at 21 GB less.

  • VieNeu-TTS-v3-Nano↗

    pnnbao-ump

    > [!WARNING] > **Preview release — still under active testing.** VieNeu-TTS v3 Nano is an experimental, lightweight model. It has known shortcomings (see *Limitations* below), its quality is **noticeably lower than v3 Turbo**, and weights, voices and the API may still change between revisions. **VieNeu-TTS v3 Turbo remains the default and recommended model.** Reach for Nano only when Turbo is too slow on your hardware or when you are deploying on a phone / edge device. **VieNeu-TTS v3 Nano** is a **48M-parameter flow-matching TTS model** for Vietnamese, built for **edge devices and weak CPUs**: old laptops, mini PCs, ARM single-board computers and Android phones. It ships as a handful of small **ONNX** graphs (≈280 MB at runtime), runs **torch-free on ONNX Runtime**, loads in about 3 seconds and synthesizes at roughly **3× the speed of v3 Turbo** on the same CPU. It is a different architecture from v3 Turbo (a non-autoregressive flow model instead of an LLM-style autoregressive backbone), which is where the speed comes from — and also where the quality gap comes from. It supports **6 built-in preset voices**, inline **emotion cues** (`[cười]`, `[thở dài]`, `[hắng giọng]`) and outputs **24 kHz** audio.

  • revanimated↗

    stablediffusionapi

    Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs payload = json.dumps({ "key": "", "model_id": "revanimated", "prompt": "actual 8K portrait photo of gareth person, portrait, happy colors, bright eyes, clear eyes, warm smile, smooth soft skin, big dreamy eyes, beautiful intricate colored hair, symmetrical, anime wide eyes, soft lighting, detailed face, by makoto shinkai, stanley artgerm lau, wlop, rossdraws, concept art, digital painting, looking into camera", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None })

  • TokForge-DreamShaper-LCM-GGUF-q4↗

    darkmaniac7

    A **Q4_0-quantized** single self-contained **GGUF** for `stable-diffusion.cpp`, packaging **DreamShaper-7** (SD1.5, realistic finetune) with the **LCM-LoRA** fused into the UNet for fast, guidance-free **few-step** sampling. This is the **6 GB-tier fast image route** for the TokForge apps, the smaller sibling of `TokForge-DreamShaper-LCM-GGUF` (f16, ~2.1 GB). It renders the same coherent people and hands (no SD-Turbo body-horror) at the same few-step LCM speed class, but at a smaller download + RAM footprint so it fits the 6 GB device tier where the f16 build is gated out. This GGUF was quantized with **stable-diffusion.cpp's own `-M convert --type q4_0`**, whose `tensor_should_be_converted` rule **protects embeddings and norms**; it keeps every tensor whose name contains `embedding` (and all `.bias` / `.scale` / norm tensors) at **F16**. So the CLIP text encoder's `token_embedding.weight` stays **F16**.

  • TenEros_Max_optimized_learned↗

    LokkenJP

    Two accepted weight-only releases are available for the H3 Beta7 transformer from TenStrip/10Eros-Max. Both retain the source's embedded eight-step Turbo. **HQ (18.69 GB) remains the recommended choice for quality**, using learned W6 g16 and INT8 ConvRot weights. **Compact (14.65 GB)** adds W4 g16 to reduce the weight footprint for setups with less available memory. Both passed mechanical validation and operator ComfyUI acceptance on **2026-10-28**. For compact, the operator reports slightly lower quality than HQ while holding up well, with lower observed memory use. These are qualitative observations without measured VRAM savings or a controlled quality score. The older Beta6 compact remains a separate source release. Beta7 has the standard H3 tensor layout without compression-gate tensors. The upstream author describes its merge as MLP-focused, preserving base attention/reference behavior, and recommends Euler/simple or ER-SDE/SGM_uniform for its Turbo sampling. See the upstream source notes for its recipe, workflow settings and known limitations.