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_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2-24B-A2B **MLX quantization**: provided by LM Studio team using mlx_lm 🙏 Special thanks to the Apple Machine Learning Research team for creating MLX.
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2-24B-A2B **MLX quantization**: provided by LM Studio team using mlx_lm 🙏 Special thanks to the Apple Machine Learning Research team for creating MLX.
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*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator**: LiquidAI **Original model**: LFM2.5-1.2B-Instruct **GGUF quantization**: provided by LM Studio team using `llama.cpp` release b7662 🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2.5-1.2B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm 4-bit quantized version of LFM2.5-1.2B-Instruct using MLX, optimized for Apple Silicon.
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2.5-1.2B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm 6-bit quantized version of LFM2.5-1.2B-Instruct using MLX, optimized for Apple Silicon.
_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2.5-1.2B-Instruct **MLX quantization**: provided by LM Studio team using mlx_lm 8-bit quantized version of LFM2.5-1.2B-Instruct using MLX, optimized for Apple Silicon.
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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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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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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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。
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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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_👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord_. **Model creator**: LiquidAI **Original model**: LFM2.5-2.6B **Source revision**: `dca1825886789bd40b94368f53b1d9ada4c94598` **MLX quantization**: provided by LM Studio team using mlx_lm 8-bit affine quantized version of LFM2.5-2.6B using a group size of 64, optimized for Apple Silicon.
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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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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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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。
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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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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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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。
*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** meta-llama **Original model**: Llama-3.2-1B-Instruct **GGUF quantization:** provided by bartowski based on `llama.cpp` release b3821 Llama 3.2 is optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.
*👾 LM Studio Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on Discord*. **Model creator:** meta-llama **Original model**: Llama-3.2-3B-Instruct **GGUF quantization:** provided by bartowski based on `llama.cpp` release b3821 Llama 3.2 is optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks.
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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.
