`nomic-embed-code` is a state-of-the-art code embedding model that excels at code retrieval tasks: - **High Performance**: Outperforms Voyage Code 3 and OpenAI Embed 3 Large on CodeSearchNet - **Multilingual Code Support**: Trained for multiple programming languages (Python, Java, Ruby, PHP, JavaScript, Go) - **Advanced Architecture**: 7B parameter code embedding model - **Fully Open-Source**: Model weights, training data, and evaluation code released - **Total Parameters**: 7B - **Training Approach**: Trained on the CoRNStack dataset with dual-consistency filtering and progressive hard negative mining - **Supported Languages**: Python, Java, Ruby, PHP, JavaScript, and Go
Original model: nomic-embed-text-v1.5 Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string. For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application.
Embedding text with `nomic-embed-text` requires task instruction prefixes at the beginning of each string. For example, the code below shows how to use the `search_query` prefix to embed user questions, e.g. in a RAG application. To see the full set of task instructions available & how they are designed to be used, visit the model card for nomic-embed-text-v1.
This model was presented in the paper Training Sparse Mixture Of Experts Text Embedding Models. Original model: nomic-embed-text-v2-moe This model can be used with the llama.cpp server and other software that supports llama.cpp embedding models.
- *pust og ventilering* ( se e-læringskurset PUST ) - *astma og epilepsi* (se Astma - og allergiforbundet og Helse norge.no ) - *ernæring og sondemating* ( se egen te maside f rambu.no )
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** The models are finally trained by MRL), so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.
The models are trained based on `Alibaba-NLP/gte-large-en-v1.5` and `Alibaba-NLP/gte-Qwen2-1.5B-instruct`. Thanks for their contributions! **We simplify usage of prompts, providing two prompts for most general tasks, one is for s2p, another one is for s2s.** The models are finally trained by MRL, so they have multiple dimensions: 512, 768, 1024, 2048, 4096, 6144 and 8192.
This is the BERT-Medium model from Google: https://github.com/google-research/bert#bert. A BERT model with 2 layers, 128 hidden unit size, and 2 attention heads.
The base model pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz. **Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data. Check out this blog for more in-detail explanation of how to fine-tune the model. Paper: DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERT
This model performs visual feature extraction. For instance, RADIO generates image embeddings that can be used by a downstream model to classify images. **Input Type(s):** Image **Input Format(s):** Red, Green, Blue (RGB) pixel values in [0, 1] range. **Input Parameters:** Two Dimensional (2D) **Other Properties Related to Input:** Image resolutions up to 2048x2028 in increments of 16 pixels **Output Type(s):** Embeddings **Output Format:** Tensor **Output Parameters:** 2D **Other Properties Related to Output:** Downstream model required to leverage image features
Adjust provider, category or input/output filters. New models are added continuously.
COST EXPLORER
Great models. Clear costs.
Cost analysis
Adjust monthly token usage to compare estimated API costs.
1 M = 1 million tokens · USD Compares base text-token rates within the same channel
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
MODEL HANDBOOK
Understand before you choose
Selection guide & FAQ
How should API prices be compared?+
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Do open weights mean free commercial use?+
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Is the top-ranked model the best for me?+
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.
