Hermes 2 Pro Mistral 7B
Outstanding accuracy in function calling and JSON structured outputs.
about
With 7 billion parameters, Hermes 2 Pro Mistral 7B, developed by Nous Research, is a top-tier language model. It shines in benchmarks like GPT4All and BigBench, showcasing significant improvements in task performance. Its function calling precision and JSON mode capabilities are particularly noteworthy.
| License | apache-2.0 |
|---|---|
| Context window(in thousands) | 32768 |
Use cases for Hermes 2 Pro Mistral 7B
- Language Translation: Quickly translates and understands multiple languages, making it great for global communication.
- Sentiment Analysis: Analyzes social media posts, customer reviews, and other public feedback to gauge public sentiment towards a product or service.
- Real-time Data Analysis: Provides immediate insights by analyzing live data, useful in areas like stock trading and live event monitoring.
| Arena Elo | 1074 |
|---|---|
| MMLU | N/A |
| MT Bench | N/A |
Hermes 2 Pro Mistral 7B scores 1,074 on the Chatbot Arena Leaderboard, ranking above Gemma 2B IT, which has a score of 989.
Llama 2 Chat 70B
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Hermes 2 Pro Mistral 7B
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Mistral 7B Instruct v0.2
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GPT-3.5 Turbo-1106
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What's Twitter saying?
- OpenAI API-Compatible Function Calling: Kyle Mistele discusses progress towards OpenAI API-compatible function calling in vllm_project using Mistral 7B instruct v0.3 and NousResearch Hermes 2 Pro models, with contributions from Hugging Face. (Source: @0xblacklight)
- Advocating for OutlinesOSS: Rémi recommends OutlinesOSS for structured JSON output, citing Andrej Baranovskij's endorsement of Hermes-2-Pro-Mistral-7B LLM and other tools like UnstructuredIO and LangChainAI Pydantic parser. (Source: @remilouf)
- Extracting JSON from Unstructured Text: Aman Arora explores methods for extracting JSON from unstructured text using open-source models and Pydantic schema, evaluating tools like Guidance, Instructor, DSPy, Guardrails-AI, and jsonformer. He also considers the Hermes-2-Pro-Mistral-7B.Q8_0.gguf model for this task. (Source: @amaarora)
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| Organization | Model Name | Tasks | Languages Supported | Context Length | Parameters | Model Tier | License |
|---|---|---|---|---|---|---|---|
| deepseek-ai | deepseek-coder-33b-instruct | text generation | English | 16384 | 33.3B | medium | deepseek |
| deepseek-ai | deepseek-coder-6.7b-instruct | text generation | English | 16384 | 6.7B | small | deepseek |
| deepseek-ai | DeepSeek-R1 | text generation | English | 128000 | 684.5B | large | deepseek |
| deepseek-ai | DeepSeek-R1-Distill-Qwen-14B | text generation | English | 43000 | 14.8B | medium | deepseek |
| fixie-ai | ultravox-v0_4 | audio text-to-text | Multilingual | 131072 | 8.1B | small | mit |
| fixie-ai | ultravox-v0_4_1-llama-3_1-70b | audio text-to-text | Multilingual | 2900 | 71.2B | large | mit |
| fixie-ai | ultravox-v0_4_1-llama-3_1-8b | audio text-to-text | Multilingual | 8000 | 8.7B | small | mit |
| fixie-ai | ultravox-v0_5-llama-3_3-70b | audio text-to-text | Multilingual | 90000 | 71.2B | large | mit |
| gemma-2b-it | text generation | English | 8192 | 2.5B | small | gemma | |
| gemma-7b-it | text generation | English | 8192 | 8.5B | small | gemma | |
| meta-llama | Llama-2-13b-chat-hf | text generation | English | 4096 | 13.0B | medium | LLAMA 2 Community License |
| meta-llama | Llama-2-7b-chat-hf | text generation | English | 4096 | 6.7B | small | LLAMA 2 Community License |
| meta-llama | Llama-3.3-70B-Instruct | text generation | Multilingual | 99000 | 70.6B | large | llama3.3 |
| meta-llama | Llama-Guard-3-1B | safety classification | Multilingual | 128000 | 1.5B | small | llama3.3 |
| meta-llama | Meta-Llama-3-70B-Instruct | text generation | English | 8192 | 70.6B | large | llama3 |
| meta-llama | Meta-Llama-3-8B-Instruct | text generation | English | 8192 | 8.0B | small | llama3 |
| meta-llama | Meta-Llama-3.1-70B-Instruct | text generation | Multilingual | 99000 | 70.6B | large | llama3.1 |
| meta-llama | Meta-Llama-3.1-8B-Instruct | text generation | Multilingual | 131072 | 8.0B | small | llama3.1 |
| mistralai | Mistral-7B-Instruct-v0.1 | text generation | English | 8192 | 7.2B | small | apache-2.0 |
| mistralai | Mistral-7B-Instruct-v0.2 | text generation | English | 32768 | 7.2B | small | apache-2.0 |
| mistralai | Mixtral-8x7B-Instruct-v0.1 | text generation | Multilingual | 32768 | 46.7B | medium | apache-2.0 |
| openai | gpt-3.5-turbo | text generation | Multilingual | 4096 | 0 | large | openai |
| openai | gpt-3.5-turbo-0125 | text generation | Multilingual | 4096 | 0 | large | openai |
| openai | gpt-3.5-turbo-1106 | text generation | Multilingual | 4096 | 0 | large | openai |
| openai | gpt-3.5-turbo-16k | text generation | Multilingual | 16000 | 0 | large | openai |
| openai | gpt-4 | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-0125-preview | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-0314 | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-0613 | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-1106-preview | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-32k | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-32k-0314 | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-32k-0613 | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4-turbo-preview | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4o | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | gpt-4o-mini | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | o1-mini | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | o1-preview | text generation | Multilingual | 128000 | 0 | large | openai |
| openai | o3-mini | text generation | Multilingual | 200000 | 0 | large | openai |
| xai-org | grok-2 | text generation | Multilingual | 131072 | 0 | large | xai |
| xai-org | grok-2-latest | text generation | Multilingual | 131072 | 0 | large | xai |
| xai-org | grok-beta | text generation | Multilingual | 131072 | 0 | large | xai |
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faqs
What is Hermes 2 Pro - Mistral 7B?
Hermes 2 Pro - Mistral 7B is an advanced version of the Nous Hermes 2 model, enhanced and retrained with an updated OpenHermes 2.5 Dataset and a new Function Calling and JSON Mode dataset. This model is designed for general task and conversation capabilities, excelling in Function Calling, JSON Structured Outputs, and scoring high in evaluations for these features.
How does Hermes 2 Pro improve on function calling and JSON structured outputs?
Hermes 2 Pro incorporates a special system prompt and multi-turn function calling structure, alongside a chatml role, to facilitate reliable and easy parsing of function calls and JSON structured outputs. This model scored a 90% on function calling evaluation and an 84% on structured JSON Output evaluation, indicating significant improvements in these areas.
Where can I learn more about the function calling system for Hermes 2 Pro?
For detailed information on the function calling system utilized by Hermes 2 Pro, visit the GitHub repository at NousResearch Hermes Function Calling.
Can I use Hermes 2 Pro for text generation in languages other than English?
Hermes 2 Pro is primarily designed and optimized for English. While it may have capabilities in other languages, its performance is best with English text generation and processing tasks.
How do I format prompts for Hermes 2 Pro?
Hermes 2 Pro uses ChatML as the prompt format, which allows for a structured system for engaging in multi-turn chat dialogue. For general prompts, use the provided template format, and for function calling, follow the specific system prompts and structures provided in the documentation. More details can be found on the model's page.
Where can I find example inference code for Hermes 2 Pro?
Example code for using Hermes 2 Pro with Hugging Face Transformers library is available on the model's Hugging Face page. Additionally, for function calling, refer to the Hermes Function Calling GitHub repository for comprehensive guides and examples.
What are the system requirements to use Hermes 2 Pro in inference?
To use Hermes 2 Pro in inference, especially in 4bit mode, it requires around 5GB of VRAM. Ensure your setup meets this requirement for optimal performance.
Where can I download quantized versions of Hermes 2 Pro?
Quantized versions of Hermes 2 Pro can be found on Hugging Face at NousResearch Hermes-2-Pro-Mistral-7B-GGUF.
How can I cite Hermes 2 Pro - Mistral 7B in my work?
To cite Hermes 2 Pro - Mistral 7B, use the following format: @misc{Hermes-2-Pro-Mistral-7B, url={https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B}, title={Hermes-2-Pro-Mistral-7B}, author={"interstellarninja", "Teknium", "theemozilla", "karan4d", "huemin_art"} }.