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bge-large-en-v1.5-f16
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-q8-0
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-q4-k-m
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 1024-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-large-en-v1.5-transformers
BGE-large-en-v1.5 is a 335M English embedding model for semantic search
and retrieval. This safetensors build uses the Transformers backend's
SentenceTransformer loader, producing 1024-dimensional embeddings with
a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-f16
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-q8-0
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-base-en-v1.5-q4-k-m
BGE-base-en-v1.5 is a 109M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 768-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-f16
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This F16 GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-q8-0
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This Q8_0 GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
bge-small-en-v1.5-q4-k-m
BGE-small-en-v1.5 is a 33M English embedding model for semantic search
and retrieval. This Q4_K_M GGUF build produces 384-dimensional embeddings
with a 512-token context. For retrieval, prefix queries with
"Represent this sentence for searching relevant passages: "; leave passages unprefixed.
Repository: localaiLicense: mit
Attention: Trust Remote Code is required for this model
wemm-embedding-2b
WeMM-Embedding-2B is Tencent's Apache-2.0 multilingual embedding model
built on Qwen3.5. This entry serves the original bfloat16 safetensors with
LocalAI's Transformers backend and produces 2,048-dimensional normalized
embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-4b
WeMM-Embedding-4B is Tencent's mid-sized Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 2,560-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
Attention: Trust Remote Code is required for this model
wemm-embedding-9b
WeMM-Embedding-9B is Tencent's largest Apache-2.0 multilingual embedding
model built on Qwen3.5. This entry serves the original bfloat16 safetensors
with LocalAI's Transformers backend and produces 4,096-dimensional
normalized embeddings for text retrieval, semantic search, and RAG.
The upstream model can also embed images and videos. LocalAI currently
exposes text input through its embeddings API for this backend.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-q4
Mixedbread's mxbai-embed-large-v1 is a 335M-parameter English BERT
embedding model for retrieval, semantic search, and RAG. It produces
1,024-dimensional embeddings and supports sequences up to 512 tokens.
Prefix retrieval queries with `Represent this sentence for searching
relevant passages: `. This entry uses the balanced Q4_K_M GGUF.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-q8
Mixedbread's mxbai-embed-large-v1 in the higher-fidelity Q8_0 GGUF
format. This 335M-parameter English BERT model produces
1,024-dimensional embeddings for retrieval, semantic search, and RAG.
Repository: localaiLicense: apache-2.0
mxbai-embed-large-v1-f16
Mixedbread's mxbai-embed-large-v1 in the official full-precision F16
GGUF format. This 335M-parameter English BERT model produces
1,024-dimensional embeddings for retrieval, semantic search, and RAG.
Repository: localaiLicense: apache-2.0
nemotron-3-embed-1b-q4
Nemotron-3-Embed-1B is NVIDIA's multilingual text embedding model for
retrieval, semantic search, and RAG. This compact Q4_K_M GGUF produces
2,048-dimensional normalized embeddings and supports 36 languages. Prefix
retrieval queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
nemotron-3-embed-8b-q4
Nemotron-3-Embed-8B is NVIDIA's larger multilingual text embedding model
for retrieval, semantic search, and RAG. This Q4_K_M GGUF balances retrieval
quality with local resource use and supports 36 languages. Prefix retrieval
queries with `query: ` and documents with `passage: `.
Repository: localaiLicense: openmdw-1.1
inkling
# Inkling
BF16 |
NVFP4 |
Playground |
Tinker Cookbook |
Acceptable Use
## 1. General Information
Inkling is a general-purpose multimodal model that accepts text, image and audio inputs and generates text outputs. It is intended for use in English and other languages, and across multiple coding languages. The model is designed to be used by developers building AI-powered applications, including agentic and tool-use systems, coding assistants, chatbots, and retrieval-augmented generation systems, and is suitable for general-purpose conversational use, instruction-following, and other natural language and multimodal tasks. It is released with open weights to support research, fine-tuning and integration into third-party products by downstream developers.
**Languages:** English, with general multilingual capabilities across other languages.
## 2. Getting Started
Try Inkling on the Tinker Playground or access via API using the Tinker Cookbook.
Inkling supports local deployment using the following open-source libraries:
* SGLang (recipe, PR)
* vLLM (recipe, PR)
* TokenSpeed (recipe, PR)
* Unsloth (recipe, PR)
* Huggingface (recipe, PR)
...
Repository: localaiLicense: apache-2.0
qwen3-vl-embedding-8b
**Model Name:** Qwen3-VL-Embedding-8B
**Base Model:** Qwen/Qwen3-VL-8B-Instruct
**Description:**
The **Qwen3-VL-Embedding** and **Qwen3-VL-Reranker** model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities.
**Key Features:**
- Model Type: MultiModal Embedding
- Supported Languages: 30+ Languages
- Supported Input Modalities: Text, images, screenshots, videos, and arbitrary multimodal combinations (e.g., text + image, text + video)
- Number of Parameters: 8B
- Context Length: 32k
- Embedding Dimension: Up to 4096, supports user-defined output dimensions ranging from 64 to 4096
**Downloads:**
- [GGUF Files](https://huggingface.co/Qwen/Qwen3-VL-Embedding-8B) (e.g., `Qwen3-VL-Embedding-8B-Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_embedding import Qwen3VLEmbedder model = Qwen3VLEmbedder(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
qwen3-vl-embedding-2b
**Model Name:** Qwen3-VL-Embedding-2B
**Base Model:** Qwen/Qwen3-VL-2B-Instruct
**Description:**
The **Qwen3-VL-Embedding** and **Qwen3-VL-Reranker** model series are the latest additions to the Qwen family, built upon the recently open-sourced and powerful Qwen3-VL foundation model. Specifically designed for multimodal information retrieval and cross-modal understanding, this suite accepts diverse inputs including text, images, screenshots, and videos, as well as inputs containing a mixture of these modalities.
**Key Features:**
- Model Type: MultiModal Embedding
- Supported Languages: 30+ Languages
- Supported Input Modalities: Text, images, screenshots, videos, and arbitrary multimodal combinations (e.g., text + image, text + video)
- Number of Parameters: 2B
- Context Length: 32k
- Embedding Dimension: Up to 2048, supports user-defined output dimensions ranging from 64 to 2048
**Downloads:**
- [GGUF Files](https://huggingface.co/Qwen/Qwen3-VL-Embedding-2B) (e.g., `Qwen3-VL-Embedding-2B-Q8_0.gguf`).
**Usage:**
- Requires `transformers`, `qwen-vl-utils`, and `torch`.
- Example: `from scripts.qwen3_vl_embedding import Qwen3VLEmbedder model = Qwen3VLEmbedder(...)`
**Citation:**
@article{qwen3vlembedding, ...}
This description emphasizes its capabilities, efficiency, and versatility for multimodal search tasks.
Repository: localaiLicense: apache-2.0
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