Merge branch 'master' into feat/stablediffusion-ggml

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Ettore Di Giacinto 2024-12-03 19:27:07 +01:00 committed by GitHub
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3 changed files with 57 additions and 2 deletions

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@ -8,7 +8,7 @@ DETECT_LIBS?=true
# llama.cpp versions
GOLLAMA_REPO?=https://github.com/go-skynet/go-llama.cpp
GOLLAMA_VERSION?=2b57a8ae43e4699d3dc5d1496a1ccd42922993be
CPPLLAMA_VERSION?=5e1ed95583ca552a98d8528b73e1ff81249c2bf9
CPPLLAMA_VERSION?=8648c521010620c2daccfa1d26015c668ba2c717
# whisper.cpp version
WHISPER_REPO?=https://github.com/ggerganov/whisper.cpp

@ -1 +1 @@
Subproject commit 28fce6b04c414523280c53ee02f9f3a94d9d23da
Subproject commit be85052efea3a0aaef45ecb0126d390c1bbac760

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@ -1725,6 +1725,31 @@
- filename: Teleut-7b.Q4_K_M.gguf
sha256: 844a633ea01d793c638e99f2e07413606b3812b759e9264fbaf69c8d94eaa093
uri: huggingface://QuantFactory/Teleut-7b-GGUF/Teleut-7b.Q4_K_M.gguf
- !!merge <<: *qwen25
name: "qwen2.5-7b-homercreative-mix"
urls:
- https://huggingface.co/ZeroXClem/Qwen2.5-7B-HomerCreative-Mix
- https://huggingface.co/QuantFactory/Qwen2.5-7B-HomerCreative-Mix-GGUF
description: |
ZeroXClem/Qwen2.5-7B-HomerCreative-Mix is an advanced language model meticulously crafted by merging four pre-trained models using the powerful mergekit framework. This fusion leverages the Model Stock merge method to combine the creative prowess of Qandora, the instructive capabilities of Qwen-Instruct-Fusion, the sophisticated blending of HomerSlerp1, and the foundational conversational strengths of Homer-v0.5-Qwen2.5-7B. The resulting model excels in creative text generation, contextual understanding, and dynamic conversational interactions.
🚀 Merged Models
This model merge incorporates the following:
bunnycore/Qandora-2.5-7B-Creative: Specializes in creative text generation, enhancing the model's ability to produce imaginative and diverse content.
bunnycore/Qwen2.5-7B-Instruct-Fusion: Focuses on instruction-following capabilities, improving the model's performance in understanding and executing user commands.
allknowingroger/HomerSlerp1-7B: Utilizes spherical linear interpolation (SLERP) to blend model weights smoothly, ensuring a harmonious integration of different model attributes.
newsbang/Homer-v0.5-Qwen2.5-7B: Acts as the foundational conversational model, providing robust language comprehension and generation capabilities.
overrides:
parameters:
model: Qwen2.5-7B-HomerCreative-Mix.Q4_K_M.gguf
files:
- filename: Qwen2.5-7B-HomerCreative-Mix.Q4_K_M.gguf
sha256: fc3fdb41e068646592f89a8ae62d7b330f2bd4e97bf615aef2977930977c8ba5
uri: huggingface://QuantFactory/Qwen2.5-7B-HomerCreative-Mix-GGUF/Qwen2.5-7B-HomerCreative-Mix.Q4_K_M.gguf
- &archfunct
license: apache-2.0
tags:
@ -3340,6 +3365,20 @@
- filename: Skywork-o1-Open-Llama-3.1-8B.Q4_K_M.gguf
sha256: ef6a203ba585aab14f5d2ec463917a45b3ac571abd89c39e9a96a5e395ea8eea
uri: huggingface://QuantFactory/Skywork-o1-Open-Llama-3.1-8B-GGUF/Skywork-o1-Open-Llama-3.1-8B.Q4_K_M.gguf
- !!merge <<: *llama31
name: "sparse-llama-3.1-8b-2of4"
urls:
- https://huggingface.co/QuantFactory/Sparse-Llama-3.1-8B-2of4-GGUF
- https://huggingface.co/QuantFactory/Sparse-Llama-3.1-8B-2of4-GGUF
description: |
This is the 2:4 sparse version of Llama-3.1-8B. On the OpenLLM benchmark (version 1), it achieves an average score of 62.16, compared to 63.19 for the dense model—demonstrating a 98.37% accuracy recovery. On the Mosaic Eval Gauntlet benchmark (version v0.3), it achieves an average score of 53.85, versus 55.34 for the dense model—representing a 97.3% accuracy recovery.
overrides:
parameters:
model: Sparse-Llama-3.1-8B-2of4.Q4_K_M.gguf
files:
- filename: Sparse-Llama-3.1-8B-2of4.Q4_K_M.gguf
sha256: c481e7089ffaedd5ae8c74dccc7fb45f6509640b661fa086ae979f6fefc3fdba
uri: huggingface://QuantFactory/Sparse-Llama-3.1-8B-2of4-GGUF/Sparse-Llama-3.1-8B-2of4.Q4_K_M.gguf
- &deepseek
## Deepseek
url: "github:mudler/LocalAI/gallery/deepseek.yaml@master"
@ -4905,6 +4944,22 @@
- filename: Volare.i1-Q4_K_M.gguf
sha256: fa8fb9d4cb19fcb44be8d53561c9e2840f45aed738de545983ebb158ebba461b
uri: huggingface://mradermacher/Volare-i1-GGUF/Volare.i1-Q4_K_M.gguf
- !!merge <<: *gemma
name: "bggpt-gemma-2-2.6b-it-v1.0"
icon: https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/p6d0YFHjWCQ3S12jWqO1m.png
urls:
- https://huggingface.co/QuantFactory/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF
- https://huggingface.co/QuantFactory/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF
description: |
INSAIT introduces BgGPT-Gemma-2-2.6B-IT-v1.0, a state-of-the-art Bulgarian language model based on google/gemma-2-2b and google/gemma-2-2b-it. BgGPT-Gemma-2-2.6B-IT-v1.0 is free to use and distributed under the Gemma Terms of Use. This model was created by INSAIT, part of Sofia University St. Kliment Ohridski, in Sofia, Bulgaria.
The model was built on top of Googles Gemma 2 2B open models. It was continuously pre-trained on around 100 billion tokens (85 billion in Bulgarian) using the Branch-and-Merge strategy INSAIT presented at EMNLP24, allowing the model to gain outstanding Bulgarian cultural and linguistic capabilities while retaining its English performance. During the pre-training stage, we use various datasets, including Bulgarian web crawl data, freely available datasets such as Wikipedia, a range of specialized Bulgarian datasets sourced by the INSAIT Institute, and machine translations of popular English datasets. The model was then instruction-fine-tuned on a newly constructed Bulgarian instruction dataset created using real-world conversations. For more information check our blogpost.
overrides:
parameters:
model: BgGPT-Gemma-2-2.6B-IT-v1.0.Q4_K_M.gguf
files:
- filename: BgGPT-Gemma-2-2.6B-IT-v1.0.Q4_K_M.gguf
sha256: 1e92fe80ccad80e97076ee26b002c2280f075dfe2507d534b46a4391a077f319
uri: huggingface://QuantFactory/BgGPT-Gemma-2-2.6B-IT-v1.0-GGUF/BgGPT-Gemma-2-2.6B-IT-v1.0.Q4_K_M.gguf
- &llama3
url: "github:mudler/LocalAI/gallery/llama3-instruct.yaml@master"
icon: https://cdn-uploads.huggingface.co/production/uploads/642cc1c253e76b4c2286c58e/aJJxKus1wP5N-euvHEUq7.png