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AI モデル 2119

• wav2vec2-large-xlsr-japanese-hiragana↗

  vumichien

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the Common Voice and Japanese speech corpus of Saruwatari-lab, University of Tokyo JSUT. When using this model, make sure that your speech input is sampled at 16kHz. The model can be used directly (without a language model) as follows: **WER:** 24.74%, **CER:** 10.99% The Common Voice `train`, `validation` datasets and Japanese speech corpus datasets were used for training.

• Hetman-2b↗

  YSamchuk

• Qwen3.8-27B-Coder390-EfficientThink-Opus5.5-GPT6Astra-Grok4.7-DSV4Pro-K3-SFT-RLOO-MTP-DFlash2↗

  Arkha7

  A model post-trained with several rounds of SFT and RLOO on top of Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2, which is itself the official Qwen3.8-27B post-trained with SFT and SimPO. It is built to fix the original model's habit of failing to stop: it already has an answer, yet keeps re-deriving until it hits the 94K cap with no final answer. This repository provides five safetensors tiers, **BF16**, **static FP8 Block128**, **NVFP4 W4A16**, **NVFP4 W4A4**, and **NVFP4 W4A4-W8A8** (mixed precision), all fully multimodal (text + image/video) with the official BF16 MTP bundled; all load in SGLang without patches, and the FP8 package is also smoke-tested on vLLM. It also has **NInfer W4A4 / W4A4-W8A8** (`NVFP4-NInfer/`), **INT8 W8A8** (`INT8/W8A8/`, text only), and **GGUF Q2 / Q3 / Q4 LynnStyle, Q6_K, and Q8_0** (`GGUF/`, `GGUF-NInfer/`, built-in MTP). **Lineage**: official Qwen3.8 27B (177 / 444 / 83) → our SFT + SimPO110 final, i.e. the EfficientThink base (171 / 442 / 89) → K3 continuation SFT → week-2 SFT (week2dose, update-225) → first RLOO round (182 groups) → another SFT round (merge-sft-100), giving sft-base-rloo (177 / 448 / 90) → second RLOO round (172 groups) → **Coder390** (178 / 445 / 90, dynamic FP8). Numbers are GPQA / MMLU / LCB, all full suites under the same 100K protocol. **Training base**: this model starts from our own EfficientThink SFT + SimPO model (the SimPO110 final) and goes through several rounds of SFT and RLOO, which is itself a post-trained version of the official Qwen3.8-27B: Qwen3.8-27B-EfficientThink-SFT-SimPO-DFlash2.

• Qwen3-Embedding-4B-FP8-Dynamic↗

  DCC-BS

  Embeddings are the pooled last hidden state; the head is unused and left unquantised. Cosine similarity is scale-sensitive, so compare rankings rather than raw distances when validating this checkpoint. `WikipediaRetrievalMultilingual` from MTEB — retrieval over Wikipedia in 16 languages, scored by `ndcg_at_10`. Serving Qwen/Qwen3-Embedding-4B in bf16 leaves little room for KV cache on a small GPU: the weights take what the cache needs, and the context length has to be cut until it fits. Halving the weights gives that memory back — the same card serves a longer context without any other change.

• Samantha-Mythomax-l2-13b-merge-fixed-i1-GGUF↗

  mradermacher

• DFN5B-CLIP-ViT-H-14↗

  apple

  A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-5B. Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. This model was trained on 5B images that were filtered from a pool of 43B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B + 30B additional public image-text pairs). This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). These weights are directly usable in OpenCLIP (image + text). - **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. - **Dataset:** DFN-5b - **Papers:** - Data Filtering Networks: https://arxiv.org/abs/2309.17425 - **Samples Seen:** 39B

• FLUX.1-Kontext-dev-GGUF↗

  bullerwins

  `FLUX.1 Kontext [dev]` is a 12 billion parameter rectified flow transformer capable of editing images based on text instructions. For more information, please read our blog post and our technical report. You can find information about the `[pro]` version in here. 1. Change existing images based on an edit instruction. 2. Have character, style and object reference without any finetuning. 3. Robust consistency allows users to refine an image through multiple successive edits with minimal visual drift. 4. Trained using guidance distillation, making `FLUX.1 Kontext [dev]` more efficient. 5. Open weights to drive new scientific research, and empower artists to develop innovative workflows. 6. Generated outputs can be used for personal, scientific, and commercial purposes, as described in the [FLUX.1 \[dev\] Non-Commercial License](https://github.com/black-forest-labs/flux/blob/main/model_licenses/LICENSE-FLUX1-dev). We provide a reference implementation of `FLUX.1 Kontext [dev]`, as well as sampling code, in a dedicated github repository. Developers and creatives looking to build on top of `FLUX.1 Kontext [dev]` are encouraged to use this as a starting point.

• Sam-reason-S2.1-i1-GGUF↗

  mradermacher

• Qwen-Image-2.1-Turbo-GGUF↗

  Abiray

  Quantized GGUF weights for Alibaba's official **Qwen-Image-2.1-Turbo** distilled diffusion model. These checkpoints enable lightning-fast text-to-image and image-editing generation in ComfyUI at a fraction of the native VRAM requirement, taking full advantage of 4–8 step Turbo sampling. All models were converted using `ComfyUI-GGUF` tools and quantized using a patched multi-threaded `llama-quantize` build natively supporting the 297-tensor `qwen_image` architecture.

• Intern-S1-mini↗

  internlm

  We introduce **Intern-S1-mini**, a lightweight open-source multimodal reasoning model based on the same techniques as **Intern-S1**. Built upon an 8B dense language model (Qwen3) and a 0.3B Vision encoder (InternViT), Intern-S1-mini has been further pretrained on **5 trillion tokens** of multimodal data, including over **2.5 trillion scientific-domain tokens**. This enables the model to retain strong general capabilities while excelling in specialized scientific domains such as **interpreting chemical structures, understanding protein sequences, and planning compound synthesis routes**, making Intern-S1-mini to be a capable research assistant for real-world scientific applications. We evaluate the Intern-S1-mini on various benchmarks including general datasets and scientific datasets. We report the performance comparison with the recent VLMs and LLMs below. You can utilize one of the following LLM inference frameworks to create an OpenAI compatible server: