Nous Hermes 2 Mistral 7B
Boost efficiency in AI tasks with a model that combines speed and affordability.
Nous Hermes 2 Mistral 7B, licensed under Apache 2.0, is a language model with a notable context window. It's designed for basic tasks, excelling in rapid data processing, though it has limitations with complex dialogues and advanced applications.
| License | apache-2.0 |
|---|---|
| Context window(in thousands) | 32768 |
Use cases for Nous Hermes 2 Mistral 7B
- General-purpose language processing: Nous Hermes 2 Mistral 7B is great for general tasks, following instructions effectively and generating coherent text.
- Role-playing and creative writing: Its performance and creativity make it perfect for immersive role-playing scenarios and creative writing applications.
- Token generation: The model can generate up to 30 tokens per second on a 6700xt GPU, making it ideal for applications needing quick responses.
| Arena Elo | 1010 |
|---|---|
| MMLU | 55.4 |
| MT Bench | 6.84 |
Nous Hermes 2 Mistral 7B shows average quality in human-like response quality and translation benchmarks, with strong reasoning and knowledge metrics.
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| Throughput(output tokens per second) | 93 |
|---|---|
| Latency(seconds to first tokens chunk received) | 0.6 |
| Total Response Time(seconds to output 100 tokens) | 1.5 |
This model offers fast throughput, suitable for high-volume applications. However, its higher latency might affect real-time interactions. The quick total response time ensures a smooth user experience for most applications, though latency could be an issue for those needing instant responses.
The cost of running the model with Telnyx Inference is $0.0002 per 1,000 tokens. For instance, analyzing 1,000,000 customer chats, assuming each chat is 1,000 tokens long, would cost $200.
What's Twitter saying?
- Creative testing: Fabian Stelzer shares his experience testing various LLMs for a project at Glif, praising Nous Hermes 2 and Mistral 8x-7B SFT as better than GPT-4 for creative uses. (Source: testing dozens llms)
- Rapid improvement: Awni Hannun notes that the browser-based MLX and Nous Hermes Mistral demo is rapidly improving, especially in remembering context. This demo can run locally if enough RAM is available. (Source: mlx + nous hermes mistral demo)
- Prompting best-practices: Philipp Schmid discusses few-shot prompts with open LLMs like Nous-Hermes-2-Mistral-8x7B-DPO, observing that open LLMs perform better with all information included in the first user message. (Source: few-shot prompts with open llms)
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What is Nous-Hermes-2-Mistral-7b-DPO?
Nous-Hermes-2-Mistral-7b-DPO is an advanced version of the Nous Hermes 2 artificial intelligence model, enhanced for superior performance through Direct Preference Optimization (DPO). It excels in various benchmarks like AGIEval, BigBench Reasoning, GPT4All, and TruthfulQA, making it a highly competitive option in the AI landscape.
How does Nous-Hermes-2-Mistral-7b-DPO differ from other models?
This model stands out due to its use of Direct Preference Optimization (DPO) for learning from human feedback, its efficient and cost-effective design, and its multi-language support. It has shown improvements over models like Mistral Instruct v0.1 across multiple benchmarks.
What applications can use Nous-Hermes-2-Mistral-7b-DPO?
Nous-Hermes-2-Mistral-7b-DPO is versatile and suitable for a wide range of applications including text generation, text summarization, instruction following, task automation, data analysis, and code generation. It's an invaluable tool for developers and businesses looking to enhance their services or products with cutting-edge AI capabilities.
How efficient is Nous-Hermes-2-Mistral-7b-DPO?
The model is designed for high efficiency, with a latency of 0.61 seconds and a throughput of 93.91 tokens per second. This efficiency, coupled with its cost-effectiveness, makes it an attractive option for those looking to integrate advanced AI into their solutions.
Can Nous-Hermes-2-Mistral-7b-DPO support multiple languages?
Yes, unlike some other models, Nous-Hermes-2-Mistral-7b-DPO supports multiple languages, adding to its versatility and making it suitable for global applications across various industries.
Is there a version of Nous-Hermes-2-Mistral-7b-DPO that allows for Sparse Fine-Tuning (SFT)?
Yes, there is an SFT version available for Nous-Hermes-2-Mistral-7b-DPO. This version is designed for users who prefer Sparse Fine-Tuning, providing flexibility to choose the best approach for their specific needs.
Where can I use Nous-Hermes-2-Mistral-7b-DPO for my project?
You can integrate Nous-Hermes-2-Mistral-7b-DPO into your connectivity apps through platforms like Telnyx. This allows developers to leverage the model's capabilities in text processing, automation, and more, directly within their applications. For more information on integrating Nous-Hermes-2-Mistral-7b-DPO with Telnyx, visit Telnyx's Developer Documentation.
What kind of training data was used for Nous-Hermes-2-Mistral-7b-DPO?
The model was trained on a large dataset consisting of over 1 million instructions and chats, which includes synthetic data and high-quality datasets from various sources. This extensive and diverse training data contributes to the model's broad knowledge base and understanding.
How does Direct Preference Optimization (DPO) enhance Nous-Hermes-2-Mistral-7b-DPO?
DPO is a technique that allows the model to learn from human feedback and adapt its responses to better align with user preferences. This method significantly enhances the model's ability to understand and respond to complex instructions or queries, providing more accurate and relevant outputs.