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22 changes: 22 additions & 0 deletions gallery/index.yaml
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- gemma3
- gemma-3
overrides:
#mmproj: gemma-3-27b-it-mmproj-f16.gguf

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parameters:
model: gemma-3-27b-it-Q4_K_M.gguf
files:
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description: |
google/gemma-3-12b-it is an open-source, state-of-the-art, lightweight, multimodal model built from the same research and technology used to create the Gemini models. It is capable of handling text and image input and generating text output. It has a large context window of 128K tokens and supports over 140 languages. The 12B variant has been fine-tuned using the instruction-tuning approach. Gemma 3 models are suitable for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes them deployable in environments with limited resources such as laptops, desktops, or your own cloud infrastructure.
overrides:
#mmproj: gemma-3-12b-it-mmproj-f16.gguf

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parameters:
model: gemma-3-12b-it-Q4_K_M.gguf
files:
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description: |
Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. Gemma-3-4b-it is a 4 billion parameter model.
overrides:
#mmproj: gemma-3-4b-it-mmproj-f16.gguf

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parameters:
model: gemma-3-4b-it-Q4_K_M.gguf
files:
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sha256: 2756551de7d8ff7093c2c5eec1cd00f1868bc128433af53f5a8d434091d4eb5a
uri: huggingface://Triangle104/Nano_Imp_1B-Q8_0-GGUF/nano_imp_1b-q8_0.gguf
- &qwen25
name: "qwen2.5-14b-instruct" ## Qwen2.5

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icon: https://avatars.githubusercontent.com/u/141221163
url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
license: apache-2.0
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- filename: Qwen3-Grand-Horror-Light-1.7B.Q4_K_M.gguf
sha256: cbbb0c5f6874130a8ae253377fdc7ad25fa2c1e9bb45f1aaad88db853ef985dc
uri: huggingface://mradermacher/Qwen3-Grand-Horror-Light-1.7B-GGUF/Qwen3-Grand-Horror-Light-1.7B.Q4_K_M.gguf
- !!merge <<: *qwen25
name: "rent-qwen-7b-i1"
urls:
- https://huggingface.co/mradermacher/RENT-Qwen-7B-i1-GGUF
description: |
**Model Name:** RENT-Qwen-7B
**Base Model:** Qwen2.5-7B-Instruct
**Training Approach:** Unsupervised Reinforcement Learning via Entropy Minimization (RENT) — no external rewards or labeled data required
**Dataset:** AIME 2024 (math problem-solving benchmark)
**Key Achievement:** Outperforms the base model on AIME, achieving a mean score of **0.232 ± 0.003** (vs. 0.110 ± 0.004 for base), demonstrating strong reasoning capabilities without supervision.
**Use Case:** Ideal for reasoning tasks, especially where labeled data is scarce. Best suited for evaluation on math and logical reasoning benchmarks.
**Note:** This is a fine-tuned variant of Qwen2.5-7B-Instruct, trained using an innovative unsupervised RL method. The model is not quantized — the original full-precision weights are available in the base repository.

👉 **Base Model:** [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
👉 **Training Paper & Code:** [RENT GitHub Repo](https://github.com/satrams/rent-rl) | [arXiv Paper](https://arxiv.org/abs/2505.22660)
overrides:
parameters:
model: RENT-Qwen-7B.i1-Q4_K_M.gguf
files:
- filename: RENT-Qwen-7B.i1-Q4_K_M.gguf
sha256: bc0bd3cc994e90297072066bcc3e61e9352bfc28e7cffc09013b6134fb5bb5e2
uri: huggingface://mradermacher/RENT-Qwen-7B-i1-GGUF/RENT-Qwen-7B.i1-Q4_K_M.gguf
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