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AI 模型 84757
- Forced_BJ↗
onamissiononamission
- gemma-3n-E2B-it-ONNX↗
onnx-community
- kev-4b-ONNX↗
onnx-community
- InternVL2_5-38B-MPO-AWQ↗
OpenGVLab
- InternVL2_5-8B↗
OpenGVLab
We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 2.5 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.
- InternVL2-Llama3-76B↗
OpenGVLab
- Ornith-1.5-122B-FP8↗
ornith-ai
Chirp Chirp! 🐦 We are introducing Ornith-1.5, a major step toward building foundation models through end-to-end self-improvement. Ornith-1.5 extends Ornith-1.0 (which was developed on top of Qwen3.5 and Gemma4 with additional continued pretraining, mid-training, and post-training) by expanding the self-improvement loop from scaffold and rollout optimization to jointly optimizing task generation, scaffold construction, and solution rollouts. Rather than relying on a fixed set of human-curated tasks and manually designed harnesses, Ornith-1.5 continuously generates new training tasks, discovers effective strategies for solving them, and improves the policy through reinforcement learning. For more details on the task, harness, and rollout reward design, please refer to our blog. This model card documents **Ornith-1.5-122B**, the most capable and efficient.member of the Ornith-1.5 family — a 122B MOE model designed for efficient single-GPU deployment, and edge-deployable on mobile devices via its quantized Ornith-1.5-122B-Mobile variant.
- Nanbeige4.2-3B-GGUF↗
owao
See the original model card for details on how to use with llama.cpp and ollama (you'll need to build from their fork cause their changes didn't land in llama.cpp main yet). > Some of llama.cpp maintainers even have to work during their vacations just to have someone else copy their work without giving any credits. > it would have been much better if all projects that depend on @ggerganov's and the ggml team work would have upstreamed the contributions directly so anyone in the ecosystem could benefit, and avoid vendor lock-in and the duplicated efforts everywhere... consuming llama.cpp and reporting issues and upstream any change directly there. It is quite frustrating to see that the Open source scene is really getting derailed lately by this kind of bad attitude.
- Nemotron-3.5-Lightning-30B-A3B-mixed-INT4-INT8↗
primitive-ai
- Qwen3-4B-2507-abliterated-GGUF↗
prithivMLmods
> The Huihui-Qwen3-4B-Instruct-2507-abliterated model is an uncensored, proof-of-concept version of the Qwen3-4B-Instruct-2507 large language model, created using a novel abliteration method designed to remove refusal responses without using TransformerLens. This approach offers a faster and more effective way to bypass the model's standard refusal behaviors, resulting in a less filtered and more raw output experience, though it lacks rigorous safety filtering and may generate sensitive or controversial content. The model is quantized (4-bit) for efficient use, can be used directly in Hugging Face’s transformers library, and is intended primarily for research or experimental use rather than production due to the reduced content restrictions and associated risks. Users are advised to carefully monitor outputs and ensure ethical and legal compliance when deploying this model. (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)