Update README.md

README.md

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from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), and several others, detailed further below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