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| Original file line number | Diff line number | Diff line change |
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| --- | ||
| title: Models | ||
| description: Reliable, future proof AI predictions | ||
| slug: options/models | ||
| --- | ||
|
|
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| This page provides information about the different models used by the Prediction | ||
| Guard API. | ||
|
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||
| ## Hermes-2-Pro-Llama-3-8B | ||
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| A general use model that maintains excellent general task and conversation | ||
| capabilities while excelling at JSON Structured Outputs and improving on several | ||
| other metrics. | ||
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| **Type**: Chat | ||
| **Use Case**: Instruction Following or Chat-Like Applications | ||
| **Promp Format**: [ChatML](/options/prompts#chatml) | ||
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| https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B | ||
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| Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of | ||
| an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly | ||
| introduced Function Calling and JSON Mode dataset developed in-house. | ||
|
|
||
| This new version of Hermes maintains its excellent general task and conversation | ||
| capabilities - but also excels at Function Calling, JSON Structured Outputs, | ||
| and has improved on several other metrics as well, scoring a 90% on our function | ||
| calling evaluation built in partnership with Fireworks.AI, and an 84% on our | ||
| structured JSON Output evaluation. | ||
|
|
||
| Hermes Pro takes advantage of a special system prompt and multi-turn function | ||
| calling structure with a new chatml role in order to make function calling | ||
| reliable and easy to parse. | ||
|
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| ## Nous-Hermes-Llama2-13B | ||
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| A general use model that combines advanced analytics capabilities with a vast 13 | ||
| billion parameter count, enabling it to perform in-depth data analysis and | ||
| support complex decision-making processes. This model is designed to process | ||
| large volumes of data, uncover hidden patterns, and provide actionable insights. | ||
|
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| **Type**: Text Generation | ||
| **Use Case**: Generating Output in Response to Arbitrary Instructions | ||
| **Promp Format**: [Alpaca](/options/prompts#alpaca) | ||
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| https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b | ||
|
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| Nous-Hermes-Llama2-13b is a state-of-the-art language model fine-tuned on over | ||
| 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium | ||
| and Emozilla leading the fine tuning process and dataset curation, Redmond AI | ||
| sponsoring the compute, and several other contributors. | ||
|
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||
| This Hermes model uses the exact same dataset as Hermes on Llama-1. This is to | ||
| ensure consistency between the old Hermes and new, for anyone who wanted to keep | ||
| Hermes as similar to the old one, just more capable. | ||
|
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| This model stands out for its long responses, lower hallucination rate, and | ||
| absence of OpenAI censorship mechanisms. The fine-tuning process was performed | ||
| with a 4096 sequence length on an 8x a100 80GB DGX machine. | ||
|
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| ## Hermes-2-Pro-Mistral-7B | ||
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| A general use model that offers advanced natural language understanding and | ||
| generation capabilities, empowering applications with high-performance | ||
| text-processing functionalities across diverse domains and languages. The model | ||
| excels in delivering accurate and contextually relevant responses, making it ideal | ||
| for a wide range of applications, including chatbots, language translation, | ||
| content creation, and more. | ||
|
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||
| **Type**: Chat | ||
| **Use Case**: Instruction Following or Chat-Like Applications | ||
| **Promp Format**: [ChatML](/options/prompts#chatml) | ||
|
|
||
| https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B | ||
|
|
||
| Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of | ||
| an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly | ||
| introduced Function Calling and JSON Mode dataset developed in-house. | ||
|
|
||
| This new version of Hermes maintains its excellent general task and conversation | ||
| capabilities - but also excels at Function Calling, JSON Structured Outputs, and | ||
| has improved on several other metrics as well, scoring a 90% on our function | ||
| calling evaluation built in partnership with Fireworks.AI, and an 84% on our | ||
| structured JSON Output evaluation. | ||
|
|
||
| Hermes Pro takes advantage of a special system prompt and multi-turn function | ||
| calling structure with a new chatml role in order to make function calling | ||
| reliable and easy to parse. Learn more about prompting below. | ||
|
|
||
| ## Neural-Chat-7B | ||
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| A revolutionary AI model for perfoming digital conversations. | ||
|
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| **Type**: Chat | ||
| **Use Case**: Instruction Following or Chat-Like Applications | ||
| **Promp Format**: [Neural Chat](/options/prompts#neural-chat) | ||
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| https://huggingface.co/Intel/neural-chat-7b-v3-3 | ||
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| This model is a fine-tuned 7B parameter LLM on the Intel Gaudi 2 processor from | ||
| the Intel/neural-chat-7b-v3-1 on the meta-math/MetaMathQA dataset. The model was | ||
| aligned using the Direct Performance Optimization (DPO) method with | ||
| Intel/orca_dpo_pairs. The Intel/neural-chat-7b-v3-1 was originally fine-tuned | ||
| from mistralai/Mistral-7B-v-0.1. For more information, refer to the blog | ||
|
|
||
| [The Practice of Supervised Fine-tuning and Direct Preference Optimization on Intel Gaudi2](https://medium.com/@NeuralCompressor/the-practice-of-supervised-finetuning-and-direct-preference-optimization-on-habana-gaudi2-a1197d8a3cd3). | ||
|
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||
| ## llama-3-sqlcoder-8b | ||
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| A state of the art AI model for generating SQL queries from natural language. | ||
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| **Type**: SQL Query Generation | ||
| **Use Case**: Generating SQL Queries | ||
| **Promp Format**: [Llama-3-SQLCoder](/options/prompts#llama-3-sqlcoder) | ||
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| https://huggingface.co/defog/llama-3-sqlcoder-8b | ||
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| A capable language model for text to SQL generation for Postgres, Redshift and | ||
| Snowflake that is on-par with the most capable generalist frontier models. | ||
|
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| ## deepseek-coder-6.7b-instruct | ||
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| DeepSeek Coder is a capable coding model trained on two trillion code and natural | ||
| language tokens. | ||
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| **Type**: Code Generation | ||
| **Use Case**: Generating Computer Code or Answering Tech Questions | ||
| **Promp Format**: [Deepseek](/options/prompts#deepseek) | ||
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| https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct | ||
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| Deepseek Coder is composed of a series of code language models, each trained | ||
| from scratch on 2T tokens, with a composition of 87% code and 13% natural | ||
| language in both English and Chinese. We provide various sizes of the code model, | ||
| ranging from 1B to 33B versions. Each model is pre-trained on project-level code | ||
| corpus by employing a window size of 16K and a extra fill-in-the-blank task, to | ||
| support project-level code completion and infilling. For coding capabilities, | ||
| Deepseek Coder achieves state-of-the-art performance among open-source code models | ||
| on multiple programming languages and various benchmarks. | ||
|
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| ## bridgetower-large-itm-mlm-itc | ||
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| BridgeTower is a multimodal model for creating joint embeddings between images | ||
| and text. | ||
|
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| _**Note: This Model is required to be used with the `/embeddings` endpoint. Most of the | ||
| SDKs will not ask you to provide model because it's using this one.**_ | ||
|
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| **Type**: Embedding Generation | ||
| **Use Case**: Used for Generating Text and Image Embedding | ||
|
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| https://huggingface.co/BridgeTower/bridgetower-large-itm-mlm-itc | ||
|
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| BridgeTower introduces multiple bridge layers that build a connection between | ||
| the top layers of uni-modal encoders and each layer of the cross-modal encoder. | ||
| This enables effective bottom-up cross-modal alignment and fusion between visual | ||
| and textual representations of different semantic levels of pre-trained uni-modal | ||
| encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower | ||
| achieves state-of-the-art performance on various downstream vision-language tasks. | ||
| In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of | ||
| 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with | ||
| the same pre-training data and almost negligible additional parameters and | ||
| computational costs. Notably, when further scaling the model, BridgeTower | ||
| achieves an accuracy of 81.15%, surpassing models that are pre-trained on | ||
| orders-of-magnitude larger datasets. | ||
|
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| ## llava-1.5-7b-hf | ||
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| LLaVa is a multimodal model that supports vision and language models combined. | ||
|
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| _**This Model is required to be used with the `/chat/completions` vision endpoint. | ||
| Most of the SDKs will not ask you to provide model because it's using this one.**_ | ||
|
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| **Type**: Vision Text Generation | ||
| **Use Case**: Used for Generating Text from Text and Image Inputs | ||
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| https://huggingface.co/llava-hf/llava-1.5-7b-hf | ||
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| LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on | ||
| GPT-generated multimodal instruction-following data. It is an auto-regressive | ||
| language model, based on the transformer architecture. |