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--- |
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language: |
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- en |
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tags: |
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- llama-2 |
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- self-instruct |
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- distillation |
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- synthetic instruction |
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license: |
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- mit |
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--- |
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# Model Card: Nous-Hermes-Llama2-7b |
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Compute provided by our project sponsor Redmond AI, thank you! Follow RedmondAI on Twitter @RedmondAI. |
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## Model Description |
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Nous-Hermes-Llama2-7b 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 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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## Model Training |
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The model was trained almost entirely on synthetic GPT-4 outputs. Curating high quality GPT-4 datasets enables incredibly high quality in knowledge, task completion, and style. |
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This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below |
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## Collaborators |
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The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Emozilla, Huemin Art, and Redmond AI. |
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Special mention goes to @winglian for assisting in some of the training issues. |
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Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly. |
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Among the contributors of datasets: |
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- GPTeacher was made available by Teknium |
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- Wizard LM by nlpxucan |
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- Nous Research Instruct Dataset was provided by Karan4D and HueminArt. |
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- GPT4-LLM and Unnatural Instructions were provided by Microsoft |
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- Airoboros dataset by jondurbin |
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- Camel-AI's domain expert datasets are from Camel-AI |
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- CodeAlpaca dataset by Sahil 2801. |
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If anyone was left out, please open a thread in the community tab. |
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## Prompt Format |
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The model follows the Alpaca prompt format: |
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``` |
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### Instruction: |
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<prompt> |
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### Response: |
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<leave a newline blank for model to respond> |
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``` |
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or |
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``` |
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### Instruction: |
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<prompt> |
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### Input: |
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<additional context> |
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### Response: |
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<leave a newline blank for model to respond> |
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``` |
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## Benchmark Results |
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Coming soon |
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## Resources for Applied Use Cases: |
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For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord |
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For an example of a roleplaying discord chatbot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot |
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LM Studio is a good choice for a chat interface that supports GGML versions (to come) |
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## Future Plans |
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We plan to continue to iterate on both more high quality data, and new data filtering techniques to eliminate lower quality data going forward. |
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## Model Usage |
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The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions. |
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