基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
From language to images, open weights to APIs. Understand capabilities and compare real costs.
Find the right engine for your next idea.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
> A Hugging Face-compatible wrapper around the **FastViT-HD** vision backbone from > FastVLM: Efficient Vision Encoding for Vision-Language Models (Apple CVPR 2025). > This repo exposes **only the image encoder** – no text tower, no projection head – so you > can plug it into any downstream pipeline that needs per-image embeddings. D is the number of patches (e.g. 16 × 16 for 1024 × 1024 input). For 1024 × 1024 input, D = 16 × 16 = 256. The `mci.py` code implementation is licensed according to Apple's LICENSE; it is a modified version of the original `mci.py` from the FastVLM repo. The underlying weights inherit the license provided by Apple in their LICENSE_MODEL; review that file before use. All other code in this repo is licensed according to Apache 2.0.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
This model is a merged LoRA fine-tune of **Qwen3.5-9B**, mimicking the behavioral distribution of **DeepSeek-V4.1-Flash**. **Example usage**: - For text only LLMs: `llama-cli -hf kevinzhou21/Qwen3.5-9B-Distill-Deepseek-V4.1-Flash-20261003 --jinja` - For multimodal models: `llama-mtmd-cli -hf kevinzhou21/Qwen3.5-9B-Distill-Deepseek-V4.1-Flash-20261003 --jinja`
This model is a merged LoRA fine-tune of **Qwen3.5-9B**, mimicking the behavioral distribution of **DeepSeek-V4.1-Flash**. **Example usage**: - For text only LLMs: `llama-cli -hf kevinzhou21/Qwen3.5-9B-Distill-Deepseek-V4.1-Flash-20261004 --jinja` - For multimodal models: `llama-mtmd-cli -hf kevinzhou21/Qwen3.5-9B-Distill-Deepseek-V4.1-Flash-20261004 --jinja`
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
The Model keXjos/Qwen3.8-9B-mlx-4Bit was converted to MLX format from empero-ai/Qwen3.8-9B using mlx-lm version **0.31.2**.
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
基础档案已收录,详细介绍后续补充。可先查看厂商、分类与来源。
Basic profile indexed · More details to follow
Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.