VKORE-325M-NPU-Base-ONNX
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View details ↗DISCOVER & COMPARE
Explore AI models 85001
VulnLLM-R-7B-imatrix-GGUF
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View details ↗zephyr-7b-alpha-imatrix-GGUF
liodon-ai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。
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View details ↗phobert-base-v2-vinli
lionellongto2015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。
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View details ↗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。
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View details ↗Jeva-4B
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View details ↗LFM 40B
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View details ↗LFM2-1.2B
LiquidAILFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6B parameters. They provide the following key features to create AI-powered edge applications: * **Fast training & inference** – LFM2 achieves 3x faster training compared to its previous generation. It also benefits from 2x faster decode and prefill speed on CPU compared to Qwen3. * **Best performance** – LFM2 outperforms similarly-sized models across multiple benchmark categories, including knowledge, mathematics, instruction following, and multilingual capabilities. * **New architecture** – LFM2 is a new hybrid Liquid model with multiplicative gates and short convolutions. * **Flexible deployment** – LFM2 runs efficiently on CPU, GPU, and NPU hardware for flexible deployment on smartphones, laptops, or vehicles.
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View details ↗LFM2-2.6B
LiquidAILFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. We're releasing the weights of four post-trained checkpoints with 350M, 700M, 1.2B, and 2.6B parameters. They provide the following key features to create AI-powered edge applications: Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
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View details ↗LFM2-24B-A2B
LiquidAI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。
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View details ↗LFM2.5-1.2B-Instruct
LiquidAILFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. - **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket. - **Fast edge inference**: 239 tok/s decode on AMD CPU, 82 tok/s on mobile NPU. Runs under 1GB of memory with day-one support for llama.cpp, MLX, and vLLM. - **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning. Find more information about LFM2.5 in our blog post.
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View details ↗LFM2.5-1.2B-Thinking
LiquidAILFM2.5 is a new family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. We recommend using it for agentic tasks, data extraction, and RAG. It is not recommended for knowledge-intensive tasks and programming. 1. **Function definition**: We recommend providing the list of tools as a JSON object in the system prompt. You can also use the `tokenizer.apply_chat_template()` function with tools. 2. **Function call**: By default, LFM2.5 writes Pythonic function calls (a Python list between ` ` and ` ` special tokens), as the assistant answer. You can override this behavior by asking the model to output JSON function calls in the system prompt. 3. **Function execution**: The function call is executed, and the result is returned as a "tool" role. 4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text.
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View details ↗LFM2.5-2.6B
LiquidAILFM2.5-2.6B is part of LFM2.5, a family of hybrid models designed for **on-device deployment**. It builds on the LFM2 architecture with a 128K context window and agentic post-training. - **Best-in-class agent**: Competitive with models 4x larger on tool use, instruction following, and multi-step agentic tasks. - **Agentic reinforcement learning**: Trained inside the most popular agentic harnesses to improve compatibility. - **Efficient inference**: 220 tok/s on an Apple M5 Max and 113 tok/s on an AMD Ryzen CPU, in under 2.5 GB of memory. Find more information about LFM2.5-2.6B in our blog post.
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View details ↗LFM2.5-8B-A1B
LiquidAILFM2.5 is a new family of hybrid models designed for on-device deployment. It builds on the LFM2 architecture with extended pre-training and reinforcement learning. - **On-device personal assistant**: Designed to power real-life applications, chaining tool calls, and following complex instructions on all devices. - **Compressed performance**: Competitive with much larger dense and MoE models on instruction following and agentic tasks. - **Unmatched throughput**: Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang. Find more information about LFM2.5-8B-A1B in our blog post.
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View details ↗LFM2.5-VL-1.6B
LiquidAILFM2.5‑VL-1.6B is Liquid AI's refreshed version of the first vision-language model, LFM2-VL-1.6B, built on an updated backbone LFM2.5-1.2B-Base and tuned for stronger real-world performance. Find more about LFM2.5 family of models in our blog post. * **Enhanced instruction following** on vision and language tasks. * **Improved multilingual vision understanding** in Arabic, Chinese, French, German, Japanese, Korean, and Spanish. * **Robust understanding of visual content** with improved results on multi-image inputs, high-resolution images, and OCR. 🎥⚡️ You can try LFM2.5-VL-1.6B running locally in your browser with our real-time video stream captioning WebGPU demo 🎥⚡️
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View details ↗LFM2-8B-A1B
LiquidAILFM2 is a new generation of hybrid models developed by Liquid AI, specifically designed for edge AI and on-device deployment. It sets a new standard in terms of quality, speed, and memory efficiency. We're releasing the weights of our first MoE based on LFM2, with 8.3B total parameters and 1.5B active parameters. Due to their small size, **we recommend fine-tuning LFM2 models on narrow use cases** to maximize performance. They are particularly suited for agentic tasks, data extraction, RAG, creative writing, and multi-turn conversations. However, we do not recommend using them for tasks that are knowledge-intensive or require programming skills.
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View details ↗LFM2.5-2.6B
liquidLFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...
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$0.35 / M input tokensOpenRouter · liquid/lfm-2.5-2.6b
View details ↗LFM2.5-2.6B (free)
liquidLFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...
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$0 / M input tokensOpenRouter · liquid/lfm-2.5-2.6b:free
View details ↗LFM2.5-24B-A2B
liquidLFM2.5-24B-A2B is a mixture-of-experts reasoning model from Liquid AI with 24B total and 2.45B active parameters. It always reasons before answering and is built for agents that call tools. Liquid...
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$0.05 / M input tokensOpenRouter · liquid/lfm-2.5-24b-a2b
View details ↗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。
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View details ↗sn38-v4-1-10
liquid9212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。
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View details ↗sn38-v4-1-11
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View details ↗sn38-v4-1-12
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View details ↗sn38-v4-1-9
liquid9212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。
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