chore(model gallery): add qwen3-embedding-8b

gallery/index.yaml

@@ -1095,6 +1095,36 @@
     - filename: Qwen3-Embedding-4B-Q4_K_M.gguf
       sha256: aaeddb737110a166dbc7155753bb60d8c3ba9a93e69938c18bf3fdd7f23f0381
       uri: huggingface://Qwen/Qwen3-Embedding-4B-GGUF/Qwen3-Embedding-4B-Q4_K_M.gguf
+- !!merge <<: *qwen3
+  name: "qwen3-embedding-8b"
+  tags:
+    - qwen3
+    - embedding
+    - gguf
+    - gpu
+    - cpu
+  urls:
+    - https://huggingface.co/Qwen/Qwen3-Embedding-8B-GGUF
+  description: |
+    The Qwen3 Embedding series model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
+    **Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios.
+    **Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
+    **Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
+    **Qwen3-Embedding-8B-GGUF** has the following features:
+    - Model Type: Text Embedding
+    - Supported Languages: 100+ Languages
+    - Number of Paramaters: 8B
+    - Context Length: 32k
+    - Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 32 to 4096
+    - Quantization: q4_K_M, q5_0, q5_K_M, q6_K, q8_0, f16
+  overrides:
+    embeddings: true
+    parameters:
+      model: Qwen3-Embedding-8B-Q4_K_M.gguf
+  files:
+    - filename: Qwen3-Embedding-8B-Q4_K_M.gguf
+      sha256: 758749433c7954543f308a2bf850e4238c57aeb64834ee36ca6b3b57d33a147c
+      uri: huggingface://Qwen/Qwen3-Embedding-8B-GGUF/Qwen3-Embedding-8B-Q4_K_M.gguf
 - &gemma3
   url: "github:mudler/LocalAI/gallery/gemma.yaml@master"
   name: "gemma-3-27b-it"
@@ -1114,7 +1144,7 @@
     - gemma3
     - gemma-3
   overrides:
     #mmproj: gemma-3-27b-it-mmproj-f16.gguf
     parameters:
       model: gemma-3-27b-it-Q4_K_M.gguf
   files:
@@ -1132,7 +1162,7 @@
   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
     parameters:
       model: gemma-3-12b-it-Q4_K_M.gguf
   files:
@@ -1150,7 +1180,7 @@
   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
     parameters:
       model: gemma-3-4b-it-Q4_K_M.gguf
   files:
@@ -4242,7 +4272,7 @@
       sha256: b34dde1835752949d6b960528269af93c92fec91c61ea0534fcc73f96c1ed8b2
       uri: https://huggingface.co/ggml-org/ultravox-v0_5-llama-3_2-1b-GGUF/resolve/main/mmproj-ultravox-v0_5-llama-3_2-1b-f16.gguf
 - &qwen25
   name: "qwen2.5-14b-instruct" ## Qwen2.5
   icon: https://avatars.githubusercontent.com/u/141221163
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master"
   license: apache-2.0
@@ -6203,7 +6233,7 @@
       sha256: 0fec82625f74a9a340837de7af287b1d9042e5aeb70cda2621426db99958b0af
       uri: huggingface://bartowski/Chuluun-Qwen2.5-72B-v0.08-GGUF/Chuluun-Qwen2.5-72B-v0.08-Q4_K_M.gguf
 - &smollm
   url: "github:mudler/LocalAI/gallery/chatml.yaml@master" ## SmolLM
   name: "smollm-1.7b-instruct"
   icon: https://huggingface.co/datasets/HuggingFaceTB/images/resolve/main/banner_smol.png
   tags: