From 75ba2daba1e7ac12c91489d6c425df5f86ded598 Mon Sep 17 00:00:00 2001 From: "LocalAI [bot]" <139863280+localai-bot@users.noreply.github.com> Date: Wed, 24 Jun 2026 23:18:04 +0200 Subject: [PATCH] chore(model-gallery): :arrow_up: update checksum (#10495) :arrow_up: Checksum updates in gallery/index.yaml Signed-off-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: mudler <2420543+mudler@users.noreply.github.com> --- gallery/index.yaml | 21 ++------------------- 1 file changed, 2 insertions(+), 19 deletions(-) diff --git a/gallery/index.yaml b/gallery/index.yaml index e26f2a1f5..52f23a771 100644 --- a/gallery/index.yaml +++ b/gallery/index.yaml @@ -3,24 +3,7 @@ url: "github:mudler/LocalAI/gallery/virtual.yaml@master" urls: - https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF - description: | - Try LFM • Docs • LEAP • Discord - - # LFM2.5-1.2B-Instruct - - LFM2.5 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. - - ## 🗒️ Model Details - - LFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features: - - ... + description: "Try LFM • Docs • LEAP • Discord\n\n# LFM2.5-1.2B-Instruct\n\nLFM2.5 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.\n\n - **Best-in-class performance**: A 1.2B model rivaling much larger models, bringing high-quality AI to your pocket.\n - **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.\n - **Scaled training**: Extended pre-training from 10T to 28T tokens and large-scale multi-stage reinforcement learning.\n\nFind more information about LFM2.5 in our blog post.\n\n## \U0001F5D2️ Model Details\n\nLFM2.5-1.2B-Instruct is a general-purpose text-only model with the following features:\n\n...\n" license: "other" tags: - llm @@ -842,8 +825,8 @@ use_tokenizer_template: true files: - filename: llama-cpp/models/Qwopus3.6-27B-Coder-MTP-GGUF/Qwopus3.6-27B-Coder-MTP-Q4_K_M.gguf - sha256: b2898667ed7b2388f0ab7691393833ae777f247492bbe62fdb4b2bd3e3cf3f79 uri: https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF/resolve/main/Qwopus3.6-27B-Coder-MTP-Q4_K_M.gguf + sha256: b2b9180093496da2e00439e3fa23227c591355901bfa579bc6897bbc01b755ef - filename: llama-cpp/mmproj/Qwopus3.6-27B-Coder-MTP-GGUF/mmproj-F32.gguf sha256: 32f7ea0600c07272547da401d460f8abbd980f3a57b69d6df87be0e2505e0b9c uri: https://huggingface.co/Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF/resolve/main/mmproj-F32.gguf