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AI モデル 84759
- Qwen3.5-0.8B-Base↗
阿里巴巴 · Qwen
> [!Note] > This repository contains model weights and configuration files for the pre-trained only model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, etc. > > The intended use cases are fine-tuning, in-context learning experiments, and other research or development purposes, not direct interaction. > However, the control tokens, e.g., ` ` and ` ` were trained to allow efficient LoRA-style PEFT with the official chat template, mitigating the need to finetune embeddings, a significant optimization given Qwen3.5's larger vocabulary. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. - **Unified Vision-Language Foundation**: Early fusion training on multimodal tokens achieves cross-generational parity with Qwen3 and outperforms Qwen3-VL models across reasoning, coding, agents, and visual understanding benchmarks.
- R3-rerank-0.6b↗
腾讯
- ralph-crowns↗
RalphLabsAI
- DistilQwen3-1.7B-uncensored↗
reaperdoesntknow
- allknowingroger_-_CalmExperiment-7B-slerp-gguf↗
RichardErkhov
- BSC-LT_-_salamandra-2b-instruct-gguf↗
RichardErkhov
- vicgalle_-_Configurable-Hermes-3-Llama-3.1-8B-gguf↗
RichardErkhov
- OLMo-2-2.7B-Exp-Unlearning↗
sbordt
- Dolphin-2.2-Yi-34B-GGUF↗
second-state
- multi-qa-MiniLM-L6-cos-v1↗
sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 384 dimensional dense vector space and was designed for **semantic search**. It has been trained on 215M (question, answer) pairs from diverse sources. For an introduction to semantic search, have a look at: SBERT.net - Semantic Search Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the correct pooling-operation on-top of the contextualized word embeddings.