Create README.md

README.md

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+---
+license: gpl
+language:
+- en
+tags:
+- starcoder
+- wizardcoder
+- code
+- self-instruct
+- distillation
+---
+
+# Model Card: Redmond-Hermes-Coder 15B
+
+## Model Description
+
+Redmond-Hermes-Coder 15B 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 and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors.
+
+This model was trained with a WizardCoder base, which itself uses a StarCoder base model. 
+
+The model is truly great at code, but, it does come with a tradeoff though. While far better at code than the original Nous-Hermes built on Llama, it is worse than WizardCoder at pure code benchmarks, like HumanEval.
+
+It comes in at 39% on HumanEval, with WizardCoder at 57%. This is somewhat dissapointing to us, and we are exploring why now.
+
+However, it does seem better at non-code than WizardCoder on a variety of things, including writing tasks.
+
+## Model Training
+
+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from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), CodeAlpaca, Evol_Instruct Uncensored, GPT4-LLM, and Unnatural Instructions. 
+
+Additional data inputs came from Camel-AI's Biology/Physics/Chemistry and Math Datasets, Airoboros' (v1) GPT-4 Dataset, and more from CodeAlpaca. The total volume of data encompassed over 300,000 instructions.
+
+## Collaborators
+The model fine-tuning and the datasets were a collaboration of efforts and resources from members of Nous Research, includingTeknium, Karan4D, Huemin Art, and Redmond AI's generous compute grants. 
+  
+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, and the Nous Research Instruct Dataset was provided by Karan4D and HueminArt.  
+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.
+If anyone was left out, please open a thread in the community tab.
+
+## Prompt Format
+
+The model follows the Alpaca prompt format:
+```
+### Instruction:
+
+### Response:
+```
+
+or 
+
+```
+### Instruction:
+
+### Input:
+
+### Response:
+```  
+
+## 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 bot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot  
+
+## Future Plans
+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.
+
+## Benchmark Results
+```
+HumanEval: 39%
+```
+
+## 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.
+  
+Compute provided by our project sponsor Redmond AI, thank you!!