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AI Models 85003

  • MolmoPoint-GUI-8B↗

    allenai

  • camembertav2-base↗

    almanach

    CamemBERTav2 is a French language model pretrained on a large corpus of 275B tokens of French text. It is the second version of the CamemBERTa model, which is based on the DebertaV2 architecture. CamemBERTav2 is trained using the Replaced Token Detection (RTD) objective with 20% mask rate on 275B tokens on 32 H100 GPUs. The dataset used for training is a combination of French OSCAR dumps from the CulturaX Project, French scientific documents from HALvest, and the French Wikipedia. The model is a drop-in replacement for the original CamemBERTa model. Note that the new tokenizer is different from the original CamemBERTa tokenizer, so you will need to use Fast Tokenizers to use the model. It will work with `DebertaV2TokenizerFast` from `transformers` library even if the original `DebertaV2TokenizerFast` was sentencepiece-based. The new update includes: - Much larger pretraining dataset: 275B unique tokens (previously ~32B) - A newly built tokenizer based on WordPiece with 32,768 tokens, addition of the newline and tab characters, support emojis, and better handling of numbers (numbers are split into two digits tokens) - Extended context window of 1024 tokens

  • UltraCM-13B-GGUF↗

    alvarobartt

  • Qwen3.8-Flash-Next-Quark-MXFP4↗

    amd

    This checkpoint quantizes the routed **and** shared MoE experts to OCP MXFP4 and keeps everything else in bf16, including the full self-attention and the (Mamba-style) linear-attention projections, the MTP head, router gates, shared-expert gate, PLE n-gram path, vision tower, hyper-connection mixers, and `lm_head`. The model was quantized from Qwen3.8-Flash-Next using AMD-Quark. MoE expert weights and activations are quantized to OCP MXFP4. The GSM8K results were obtained using the vLLM framework, based on the Docker image `vllm/vllm-openai-rocm:v0.32.0`, with lm-eval installed from source. This Reproduction serves the model without MTP. Enabling MTP on the formal `v0.32.0` release currently requires vLLM PR #61770.

  • MiniMax-H3-Turbo-Lora↗

    Assfnjk

  • LFM2.5-2.6B-heretic↗

    atakankutalp

  • Qwen3.6-35B-A3B-UDT-MTP-GGUF↗

    AtomicChat

  • Triplex-GGUF↗

    bartowski

  • gpt2-babylm-7↗

    BrainAlign

  • needle3↗

    Cactus-Compute

    A foundation model for mobiles, wearables, robots, smart home, automotive and microcontrollers. The whole model is a single 8-29 MB file, and we trade general chat capacity to beat models 10x its size on mobile tool calls and match 2-3x bigger models on extraction. - **Tool calls**: given the functions your app exposes, Needle picks the right ones and fills every argument from what the user said. Ask for two things and you get two calls in order; ask for something no tool covers and you get an empty list, not a guess. - **Structured extraction**: declare a shape, hand over messy text, get typed fields back: an invoice, a booking, a notification, a form. The decode grammar guarantees the output parses, and extraction generalises to classification. - **Text embedding**: the same model returns a vector for a sentence, so an app can search, match and route locally. The same engine also runs Whistle, our 16.9 MB speech-to-text model in seven languages. Load `whistle.cact` beside `needle3.cact` and the one binary transcribes a clip on its own, or answers it against your tools in a single call and returns the calls and the transcript in one JSON object.