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AI 模型 2119
- dragon-multiturn-context-encoder↗
NVIDIA
We introduce Dragon-multiturn, a retriever specifically designed for the conversational QA scenario. It can handle conversational query which combine dialogue history with the current query. It is built on top of the Dragon retriever. The details of Dragon-multiturn can be found in here. **Please note that Dragon-multiturn is a dual encoder consisting of a query encoder and a context encoder. This repository is only for the context encoder of Dragon-multiturn for getting the context embeddings, and you also need the query encoder to get query embeddings, which can be found here. Both query encoder and context encoder share the same tokenizer.** .tg {border:none;border-collapse:collapse;border-spacing:0;} .tg td{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;overflow:hidden; padding:10px 5px;word-break:normal;} .tg th{border-style:solid;border-width:1px;font-family:Arial, sans-serif;font-size:14px;font-weight:normal; overflow:hidden;padding:10px 5px;word-break:normal;} .tg .tg-c3ow{border-color:inherit;text-align:center;vertical-align:center} .tg .tg-0pky{border-color:inherit;text-align:left;vertical-align:center} top-1 top-5 top-1 top-5 top-1 top-5 top-1 top-5 top-5* top-20* top-5* top-20*
- Sakura-Fara1.5-9B-GGUF↗
webmp3
Fara1.5-9B is Microsoft's computer-use agent for web browsing (Qwen3.5-9B base, multimodal: it reads browser screenshots and emits structured tool calls). This repository holds **three GGUF files of different size** made from the BF16 weights with our measured mixed-codec method. Pick one on the *Files* page; the table below says what each file is. Independent community quantization, **not** an official Microsoft release. Part of the **Sakura Mini** line. Same method, same texts, same machine for every file; lower KL divergence means closer to the BF16 original: Why: the bit budget per tensor is allocated from measured sensitivity (see *How it was made*), not from fixed rules. Full table below.
- wav2vec2-large-xlsr-53-finnish↗
jonatasgrosman
Fine-tuned facebook/wav2vec2-large-xlsr-53 on Finnish 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. This model has been fine-tuned thanks to the GPU credits generously given by the OVHcloud :) The script used for training can be found here: https://github.com/jonatasgrosman/wav2vec2-sprint
- Srijith-rkr_-_deepseek_base_1e-4_NO_cot_only_failed_samples_3_epoch-gguf↗
RichardErkhov
- Qwen2.5-Coder-0.5B-Instruct-Gensyn-Swarm-tall_tame_panther↗
0xgr3y
Our pick an **experimental (advanced) mode** at this model a continuously trained `Qwen2.5-Coder-0.5B-Instruct` fine-tuned using **Gensyn RL-Swarm** framework with **GRPO (Group Relative Policy Optimization)** and supported format **GGUF (llama.cpp)** for enhanced code generation capabilities. **Note: Current training focuses on programming challenges with adaptive weighted sampling**. **Total Dataset Size:** Streaming datasets with infinite iteration **Training Samples per Round:** 2 **Evaluation:** Real-time via Swarm Coordination with Ollama-based evaluator else Judge > "When the solvers perform well, the proposer automatically increases the difficulty to keep challenging solvers to get better over time." - CodeZero-blog
- Gemma4-12B-IT-Abliterated-GGUF↗
DuoNeural
GGUF quantizations of DuoNeural/Gemma4-12B-IT-Abliterated — an abliterated Gemma 4-12B-IT with the refusal direction surgically removed. Congratulations to OpenYourMind for being the first published abliteration of Gemma 4-12B-IT (Jun 3, 2026). Their approach uses diff-in-means on a labeled harmful/harmless set; ours uses orthogonal rank-1 projection via heretic-llm. Two independent methods on the same base — a useful comparison point for the community. We are not affiliated and did not use their data.
- Codestral-22B-v0.1-abliterated-v3↗
failspy
--- library_name: transformers license: other license_name: mnpl license_link: https://mistral.ai/licences/MNPL-0.1.md tags: - code language: - code --- This is mistralai/Codestral-22B-v0.1 with orthogonalized bfloat16 safetensor weights, generated with a refined methodology based on that which was described in the preview paper/blog post: 'Refusal in LLMs is mediated by a single direction' which I encourage you to read to understand more. TL;DR: This model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 22B model was, just with the strongest refusal directions orthogonalized out.
- embeddinggemma-300m-ONNX↗
onnx-community
**Model Page**: EmbeddingGemma * Responsible Generative AI Toolkit * EmbeddingGemma on Kaggle * EmbeddingGemma on Vertex Model Garden EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.
- LFM2.5-2.6B-GGUF-shards↗
Arki05
- `source/`: LiquidAI's BF16 GGUF (LiquidAI/LFM2.5-2.6B-GGUF), unchanged. - `types/ -hq.gguf`, `types/ -hq1.gguf`: the tensors at llama.cpp type `T`, rebuilt from the BF16 weights in two Hessian-quantized (GPTQ-family) builds: `-hq`, the higher-quality one, the block tensors; `-hq1`, the first build, every quantized tensor. Same type, format and size as llama.cpp's: plain GGUF types, for stock llama.cpp (a GGUF assembled from them holds the plain type `T`); modified versions of the original weights. - `data/`: the calibration data the types were made from (token ids and their manifest). - `manifest.json` (`qcore-store/1`), `skeleton.gguf`, `store.json`, `records/`: what qalloc reads, with every tensor's offset and sha256. The first version of this store (bartowski's BF16 GGUF and imatrix, types by llama.cpp's `llama-quantize`) is at revision `fe50613`.
- music_moods_classification↗
StanislavKo28
Music mood classification is a fundamental and versatile application in many various domains. Some possible use cases for music mood classification include: - music recommendation systems; - content organization and discovery; - radio broadcasting and programming; - music licensing and copyright management; - music analysis and research; - content tagging and metadata enrichment; - audio identification and copyright protection; - music production and creativity; - healthcare and therapy; - entertainment and gaming. The model is trained based on publicly available dataset of labeled music data — HWNAS Dataset — that contains 6930 sample 30-second audio files evenly split among 14 moods: