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AI 模型 85003
- musicgen-stereo-small↗
facebook
- tts_transformer-en-ljspeech↗
facebook
- gemini-3.8-flash-thinking-high↗
Google
Gemini 3.8 Flash 是我们最智能的 Flash 模型，专为长远软件工程、自主代理和复杂企业工作流程设计——所有这些都具备 Flash 的速度和成本效益。
- multiberts-seed_0-step_800k↗
Google
- Huihui-Qwen3-VL-8B-Instruct-abliterated↗
huihui-ai
This is an uncensored version of Qwen/Qwen3-VL-8B-Instruct created with abliteration (see remove-refusals-with-transformers to know more about it). The abliterated model will no longer say "I can’t describe or analyze this image." **Please update to the latest version of Ollama-v0.12.7.** You can use huihui_ai/qwen3-vl-abliterated:8b-instruct directly,
- wav2vec2-xls-r-300m-hebrew↗
imvladikon
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the private datasets in 2 stages - firstly was fine-tuned on a small dataset with good samples Then the obtained model was fine-tuned on a large dataset with the small good dataset, with various samples from different sources, and with an unlabeled dataset that was weakly labeled using a previously trained model. The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 100.0 - mixed_precision_training: Native AMP The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - gradient_accumulation_steps: 4 - total_train_batch_size: 64 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 1000 - num_epochs: 60.0 - mixed_precision_training: Native AMP
- Qwen3.5-9B-MXFP8-FP8KV-FP8Attn-CT-AutoRound↗
INCModel3
- Qwen3.8-27B-MXFP4↗
Inferact
- Sage-A1.5↗
InternScience
> [!Note] > This repository contains model weights and configuration files for Sage-A1.5 in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. **Sage‑A1.5** is a 35B Mixture‑of‑Experts agentic model based on **Qwen3.6** and **Agents-A1** from Shanghai AI Laboratory, built to advance long-horizon scientific problem solving through adaptive capability composition. Building upon **A1**, **Sage‑A1.5** learns not only to solve individual tasks, but also to organize work and revise its approach during execution, highlighting three key capabilities: **Agentic Orchestration, Verification, and Open Scientific Discovery**. First, agentic orchestration extends single-agent execution through task delegation and parallel collaboration among subagents with isolated contexts, enabling complex tasks to be decomposed and addressed concurrently while reducing interference across concurrent subagents. Second, agentic verification strengthens long-horizon reliability through progress checking, evidence validation, and reflective strategy revision, allowing **Sage‑A1.5** to recognize unproductive search paths and pursue alternative approaches. Third, open scientific discovery extends agentic capabilities beyond answering predefined questions to conducting exploratory research, connecting scientific inquiry with interactive analysis and inspectable research artifacts. We hope **Sage‑A1.5** provides the community with an accessible foundation for open scientific discovery, offering a practical path from long-horizon task execution to collaborative, verifiable, and inspectable scientific exploration.
- Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTPLX-4bit↗
itrejomx