328 models available
MicrosoftPhi 3 Medium 14BThe Phi-3-Medium-128K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Medium version in two variants 4k and 128K which is the context length (in tokens) that it can support.
AlibabaQwen 2.5 VL 7Blicense: apache-2.0 language: - en pipeline_tag: image-text-to-text tags: - multimodal library_name: transformers
Instinct AISolar 7BSolar 7B is Upstage's efficient language model built on a depth-upscaled architecture. Offers strong instruction following and reasoning performance optimized for single-GPU inference.
DefogSQLCoder 7BThe model weights were updated at 7 AM UTC on Feb 7, 2024. The new model weights lead to a much more performant model – particularly for joins.
AlibabaQwen 2.5 14BQwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
Tsinghua/ZhipuCodeGeeX 4 9BWe introduce CodeGeeX4-ALL-9B, the open-source version of the latest CodeGeeX4 model series. It is a multilingual code generation model continually trained on the GLM-4-9B, significantly enhancing its code generation capabilities. Using a single CodeGeeX4-ALL-9B model, it can support comprehensive functions such as code completion and generation, code interpreter, web search, function call, repository-level code Q&A, covering various scenarios of software development. CodeGeeX4-ALL-9B has achieved highly competitive performance on public benchmarks, such as BigCodeBench and NaturalCodeBench.
MistralMistral Nemo 12BThe Mistral-Nemo-Instruct-2407 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-Nemo-Base-2407. Trained jointly by Mistral AI and NVIDIA, it significantly outperforms existing models smaller or similar in size.
