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AI モデル 2119
- Qwen-Image-2.1-DiT-GGUF↗
pottokao
> **Not affiliated with, or endorsed by, Alibaba / Qwen.** Quantized redistribution of the > `Qwen/Qwen-Image-2.1` diffusion transformer under the > **Qwen Research License** (copy included as `LICENSE`). **Non-commercial use only.** > [!IMPORTANT] > **Loads in stock city96/ComfyUI-GGUF — no patch, no extra nodes.** > These files carry `general.architecture = qwen_image`, so `UnetLoaderGGUF` recognises them directly > (GGUFs without that field fail with `Unknown model architecture!`). > > **Text encoder:** our Qwen-Image-2.1 Text Encoder GGUF > works with this DiT, but it needs one extra node first — > ComfyUI-GGUF-Qwen3VL-TE (stock ComfyUI-GGUF > doesn't load Qwen3-VL text encoders yet). With a **safetensors** text encoder (bf16 / int8 / FP8 from the same > repo family) nothing extra is needed. The sample / showcase images in this repo were generated with the technique behind **ComfyUI-QwenImage-PhotoStyles** — a small ComfyUI node (17 photographic styles; one short prompt becomes a full styled prompt via the PE-T2I rewriter). If you're interested in how the images are made, the node and its recipe are there.
- wav2vec2-large-xlsr-53-russian↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Russian using the train and validation splits of Common Voice 6.1 and CSS10. When using this model, make sure that your speech input is sampled at 16kHz.
- Qwen3.5-27B-Samantha-Uncensored-i1-GGUF↗
mradermacher
- cobalt-seeded-rl-base-ramp25-stoppen-gen4k-ep2-ncp5-n3nc-vs16-s3↗
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:
- Mistral-Medium-3.5-128B↗
Mistral AI
Mistral Medium 3.5 is our first flagship merged model. It is a dense 128B model with a 256k context window, handling instruction-following, reasoning, and coding in a single set of weights. Mistral Medium 3.5 replaces its predecessor Mistral Medium 3.1 and Magistral in Le Chat. It also replaces Devstral 2 in our coding agent Vibe. Concretely, expect better performance for instruct, reasoning and coding tasks in a new unified model in comparison with our previous released models. Reasoning effort is configurable per request, so the same model can answer a quick chat reply or work through a complex agentic run. We trained the vision encoder from scratch to handle variable image sizes and aspect ratios. Find more information on our blog.
- SummOrchestra-Qwen3-8B-GRPO-BRLP-SAMSUM-i1-GGUF↗
mradermacher
- sethuiyer_-_Dr.Samantha-8B-gguf↗
RichardErkhov
- MiniMax-H3-comfy-native-fl2va↗
joeygambino
The `fl2va` half of the comfy-native set. If you want `ref2va`, it is over here — same formats, same method, different job. **Stock ComfyUI 0.32+ loads these directly.** No ComfyUI-GGUF, no custom loader node, no dequantise-on-the-fly. ComfyUI carries its own quantisation system (`comfy/quant_ops.py` plus the `comfy_kitchen` kernels) and these files use it, so the kernels run in the quantised domain instead of unpacking to bf16 first. `fl2va` is the first/last-frame variant. It has **no reference rows**, so voice anchoring, the identity bank and reference images do nothing on it. In exchange it does the one thing `ref2va` cannot:
- TIPO-v2.1-1B-A200M↗
KBlueLeaf
**TIPO-v2.1-1B-A200M** is a **1B-A200M** sparse model. 991M total parameters, ~200M active per token, plus a ~50M embedding table. It is a full retrain of TIPOv2 on a **corrected dataset**, for **longer**, at **4096 context**, in **pure fp16**. TIPO is a framework for improving Text-to-Image generation by *text presampling*: a small language model expands a short user prompt into a detailed one before the diffusion model ever sees it. Pre-sampling a smaller distribution by narrowing the range indicated by a brief prompt to a more specific description which matches the original prompt allows the diffusion model to have more information to work with, while preserving overall diversity and fidelity. Instead of asking the user to write 200 tokens of booru tags and natural language, TIPO samples that expansion from a distribution learned over real caption data. This is **v2.1**. Same architecture and same data sources as v2, retrained from scratch after three defects were found in how v2's training examples were built.
- spear-large-speech-audio↗
marcoyang
This is the SPEAR Large dual-domain (speech + general audio) model. The model adopts a Zipformer backbone with 327M parameters consisting of 112 Zipformer stacks. It generates 1024-dimensional representations at approximately 50~Hz. This model was pre-trained on 97k hours of mixture data of English speech and general audio, among which 84k hours are speech data, and the remaining 13k hours are general audio data. It achieves high performance on SUPERB benchmark (for speech representation evaluation) and on HEAR benchmark (for audio representation evaluation). **Note**: The model is pretrained on 16kHz sampled speech/audio data. When using the model make sure that your input is also sampled at 16kHz.