10 actions in 10 pieces of software · ranked by words, meaning and measured trust · 586 ms
modelrecord44,979 downloads/month
- sentence-similarity
Task: sentence-similarity
- sentence-similarity
modelrecord
- ibm-granite/granite-embedding-small-english-r2
Granite-embedding-small-english-r2 is a 47M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings.
- ibm-granite/granite-embedding-small-english-r2
modelrecord
- ibm-granite/granite-embedding-278m-multilingual
Granite-Embedding-278M-Multilingual is a 278M parameter model from the Granite Embeddings suite that can be used to generate high quality text embeddings
- ibm-granite/granite-embedding-278m-multilingual
modelrecord13,210 downloads/month
- feature-extraction
Task: feature-extraction
- feature-extraction
modelrecord10,832 downloads/month
- text-generation
Task: text-generation
- text-generation
modelrecord9,193 downloads/month
- text-ranking
Task: text-ranking
- text-ranking
modelrecord
- zsxkib/embedding-gemma-300m
Turn any text into 768-dimensional vectors for search, classification, and AI apps 🧠✨
- zsxkib/embedding-gemma-300m
modelrecord
- ibm-granite/granite-3.3-8b-instruct
Granite-3.3-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for improved reasoning and instruction-following capabilities.
- ibm-granite/granite-3.3-8b-instruct
modelrecord
- ibm-granite/granite-3.2-8b-instruct
Granite-3.2-8B-Instruct is a 8-billion parameter 128K context length language model fine-tuned for reasoning and instruction-following capabilities.
- ibm-granite/granite-3.2-8b-instruct
modelrecord
- ibm-granite/granite-3.0-8b-instruct
Granite-3.0-8B-Instruct is a lightweight and open-source 8B parameter model is designed to excel in instruction following tasks such as summarization, problem-solving, text translation, reasoning, code tasks, function-calling, and more.
- ibm-granite/granite-3.0-8b-instruct
Labels are the instruments' verdict and are never changed by ranking. Agents and robots get this same answer as JSON at /api/v1/find?q=…&answer=1 and through the MCP server.