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README.md
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- llama
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- self-instruct
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- distillation
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- llama
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- self-instruct
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- distillation
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---
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# Model Card: Nous-Hermes-13b
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## Model Description
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Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was developed and fine-tuned by Teknium, in collaboration with Nous Research, Redmond AI, and several other contributors. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks.
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This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.
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## Model Training
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The model was trained almost entirely on synthetic GPT-4 outputs. This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), CodeAlpaca, Evol_Instruct Uncensored, GPT4-LLM, and Unnatural Instructions.
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Additional data inputs came from Camel-AI's Biology/Physics/Chemistry and Math Datasets, Airoboros' GPT-4 Dataset, and more from CodeAlpaca. The total volume of data encompassed over 300,000 instructions.
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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, Nous Research, Huemin Art, and Redmond AI.
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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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Special mention goes to @winglian, @erhartford, and @main_horse for assisting in some of the training issues.
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Among the contributors of datasets, GPTeacher was made available by Teknium, Wizard LM by nlpxucan, and the Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
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The GPT4-LLM and Unnatural Instructions were provided by Microsoft, Airoboros dataset by jondurbin, Camel-AI datasets are from Camel-AI, and 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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### Response:
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```
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or
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```
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### Instruction:
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### Input:
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### Response:
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```
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## Future Plans
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The model is currently being uploaded in FP16 format, and there are plans to convert the model to GGML and GPTQ 4bit quantizations. The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. We will try to get in discussions to get the model included in the GPT4All.
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## Benchmark Results
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Benchmark results are coming soon.
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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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Compute provided by our project sponsor Redmond AI, thank you!!
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