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AI Models 2119

• fashion-clip↗

  patrickjohncyh

  Disclaimer: The model card adapts the model card from here. UPDATE (10/03/23): We have updated the model! We found that laion/CLIP-ViT-B-32-laion2B-s34B-b79K checkpoint (thanks Bin!) worked better than original OpenAI CLIP on Fashion. We thus fine-tune a newer (and better!) version of FashionCLIP (henceforth FashionCLIP 2.0), while keeping the architecture the same. We postulate that the perofrmance gains afforded by `laion/CLIP-ViT-B-32-laion2B-s34B-b79K` are due to the increased training data (5x OpenAI CLIP data). Our thesis, however, remains the same -- fine-tuning `laion/CLIP` on our fashion dataset improved zero-shot perofrmance across our benchmarks. See the below table comparing weighted macro F1 score across models. FashionCLIP is a CLIP-based model developed to produce general product representations for fashion concepts. Leveraging the pre-trained checkpoint (ViT-B/32) released by OpenAI, we train FashionCLIP on a large, high-quality novel fashion dataset to study whether domain specific fine-tuning of CLIP-like models is sufficient to produce product representations that are zero-shot transferable to entirely new datasets and tasks. FashionCLIP was not developed for model deplyoment - to do so, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.

• sambanovasystems_-_SambaLingo-Russian-Chat-gguf↗

  RichardErkhov

• ModernGBERT_134M↗

  LSX-UniWue

  ModernGBERT 134M is a German ModernBERT language model with 134 million parameters and a native context length of up to 8,192 tokens. This model follows the same BERT-style architecture and training procedure as the ModernBERT codebase. ModernGBERT 134M has been pre-trained on 470 billion tokens from the German portion of RedPajama V2, a subset of the same training dataset used for our LLäMmlein decoder family. You can use ModernGBERT with the `transformers` library from version v4.48.0 onwards. (Optional: install `flash-attn` to achieve highest efficiency.) Since ModernGBERT 134M is a Masked Language Model (MLM), you can load it via `AutoModelForMaskedLM`. For downstream tasks such as classification, retrieval, or QA, fine-tune the model by following standard BERT fine-tuning recipes.

• Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-Distilled-MLX-oQ4-MTP↗

  stamsam

• GLM-Z1-9B-0414-GGUF↗

  unsloth

  The GLM family welcomes a new generation of open-source models, the **GLM-4-32B-0414** series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival larger models like GPT-4o and DeepSeek-V3-0324 (671B). **GLM-Z1-32B-0414** is a reasoning model with **deep thinking capabilities**. This was developed based on GLM-4-32B-0414 through cold start and extended reinforcement learning, as well as further training of the model on tasks involving mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities. **GLM-Z1-Rumination-32B-0414** is a deep reasoning model with **rumination capabilities** (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learni

• Yi-34B↗

  01-ai

  If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️ > 💡 TL;DR > > The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama. 🔥 2024-07-29 : The Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English.

• Signal-3.8-Flash-Next-GGUF↗

  agentionai

  AgentionAI Signal is a minimally invasive fine-tune of Qwen3.8-Flash-Next for lower latency and better token efficiency. On MATH-500 it spends **26% fewer reasoning tokens** than the base model with **half as many hesitation markers**, and scores higher (99.2% against 97.5%). As a coding agent on Terminal-Bench 2.0 it finishes the same tasks in **25% less wall time**. With speculative decoding it is faster still: the multi-token-prediction drafter agrees with Signal 90% of the time against 69% for the base model, so more tokens are accepted per forward pass. Signal gets there by being direct, not by cutting corners. Reasoning models spend a large share of their tokens on hesitation and re-checking rather than on new steps. Signal keeps every step that does work and drops the ones that only describe the process, and in answers it drops preambles, sign-offs, excess formatting and narration while keeping the substance. The 177B mixture-of-experts backbone, the vision encoder and the multi-token-prediction draft head all work exactly as in the base model, so it is a drop-in replacement. Signal is trained by self-distillation, on Qwen3.8-Flash-Next's own answers generated under an instruction to be direct that the released model no longer needs. No external data and no other model's outputs went into it, so it keeps the base model's knowledge and voice intact.

• DanTagGen-delta-rev2↗

  KBlueLeaf

  DanTagGen(Danbooru Tag Generator) is inspired from p1atdev's dart project. But with different arch, dataset, format and different training strategy. - alpha: pretrain on 2M dataset, smaller batch size. Limited ability - beta: pretrain on 5.3M dataset, larger batch size. More stable, better ability with only a few information provided. - delta: pretrain on 7.2M dataset, larger batch size. Slightly underfit but better diversity. quality tag introduced. - rev2: resumed from delta, same dataset, 2 more epoch. This version of DTG is trained from scratch with 400M param LLaMA arch.(In my personal preference I will call it NanoLLaMA) Since it is llama arch. Theoritically it should be able to be used in any LLaMA inference interface.

• sam-3d-body-dinov3↗

  facebook

• Qwen3.6-27B-A3B-CoderX-MTP-GGUF↗

  ManniX-ITA

  GGUF quantizations of **CoderX**, a long-horizon code prune of Qwen3.6-35B-A3B: the MoE is reduced from **256 experts to 184** (72 dropped per layer, ~35B→27B, still A3B active). Same expert budget as the sibling Qwen3.6-27B-A3B-Coder, but a different **selection** — same competence map, different keep-set. **Served at top-8** — the base model's native routing, no override needed. No fine-tuning, no distillation. CoderX is **not** a strict upgrade over Coder. It is the long-horizon sibling: far stronger on multi-step competitive-programming work, a tie on short multi-language completion, and its non-code profile is not yet measured. Pick by workload.