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chore(model-gallery): ⬆️ update checksum (#4261)

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⬆️ 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>
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LocalAI [bot] 2024-11-26 09:49:05 +01:00 committed by GitHub
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commit 9b46dcf006
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gallery/index.yaml
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@ -678,8 +678,8 @@
model: Llama-Sentient-3.2-3B-Instruct.Q4_K_M.gguf
files:
- filename: Llama-Sentient-3.2-3B-Instruct.Q4_K_M.gguf
sha256: 0b1c10da004ffd61b860c9058265e9bdb7f53c7be8e87feece8896d680f5b8be
uri: huggingface://QuantFactory/Llama-Sentient-3.2-3B-Instruct-GGUF/Llama-Sentient-3.2-3B-Instruct.Q4_K_M.gguf
sha256: 3f855ce0522bfdc39fc826162ba6d89f15cc3740c5207da10e70baa3348b7812
- &qwen25
## Qwen2.5
name: "qwen2.5-14b-instruct"
@ -3496,7 +3496,7 @@
- https://huggingface.co/AIDC-AI/Marco-o1
- https://huggingface.co/QuantFactory/Marco-o1-GGUF
description: |
Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding—which are well-suited for reinforcement learning (RL)—but also places greater emphasis on open-ended resolutions. We aim to address the question: "Can the o1 model effectively generalize to broader domains where clear standards are absent and rewards are challenging to quantify?"
Marco-o1 not only focuses on disciplines with standard answers, such as mathematics, physics, and coding—which are well-suited for reinforcement learning (RL)—but also places greater emphasis on open-ended resolutions. We aim to address the question: "Can the o1 model effectively generalize to broader domains where clear standards are absent and rewards are challenging to quantify?"
overrides:
parameters:
model: Marco-o1.Q4_K_M.gguf