Added Code Llama

loleg · Aug 28, 2023 · 85a61d3 · 85a61d3
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@@ -38,6 +38,7 @@ These LLMs (Large Language Models) are all licensed for commercial use (e.g., Ap
 | XGen-7B | 2023/06 | [XGen-7B-8K-Base](https://huggingface.co/Salesforce/xgen-7b-8k-base) | [Long Sequence Modeling with XGen: A 7B LLM Trained on 8K Input Sequence Length](https://blog.salesforceairesearch.com/xgen/) | 7 | [8192](https://huggingface.co/Salesforce/xgen-7b-8k-base/blob/main/config.json) | [Apache 2.0](https://github.com/salesforce/xgen/blob/main/LICENSE) | 
 | CodeGen2.5 | 2023/07 | [CodeGen2.5-7B-multi](https://huggingface.co/Salesforce/codegen25-7b-multi) | [CodeGen2.5: Small, but mighty](https://blog.salesforceairesearch.com/codegen25/) | 7 | [2048](https://huggingface.co/Salesforce/codegen25-7b-multi/blob/main/config.json) | [Apache 2.0](https://huggingface.co/Salesforce/codegen25-7b-multi/blob/main/README.md) | 
 | DeciCoder-1B | 2023/08 | [DeciCoder-1B](https://huggingface.co/Deci/DeciCoder-1b#how-to-use) | [Introducing DeciCoder: The New Gold Standard in Efficient and Accurate Code Generation](https://deci.ai/blog/decicoder-efficient-and-accurate-code-generation-llm/) | 1.1 | [2048](https://huggingface.co/Deci/DeciCoder-1b#model-architecture) | Apache 2.0 |  [DeciCoder Demo](https://huggingface.co/spaces/Deci/DeciCoder-Demo)|
+| Code Llama  | 2023 | [Inference Code for CodeLlama models]([https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://github.com/facebookresearch/codellama)) | [Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)     | 7 - 34       | [4096](https://scontent-zrh1-1.xx.fbcdn.net/v/t39.2365-6/369856151_1754812304950972_1159666448927483931_n.pdf?_nc_cat=107&ccb=1-7&_nc_sid=3c67a6&_nc_ohc=wURKmnWKaloAX-ib5XW&_nc_ht=scontent-zrh1-1.xx&oh=00_AfAN1GB2K_XwIz54PqXTr-dhilI3CfCwdQoaLMyaYEEECg&oe=64F0A68F)  | [Custom](https://github.com/facebookresearch/llama/blob/main/LICENSE) Free if you have under 700M users and you cannot use LLaMA outputs to train other LLMs besides LLaMA and its derivatives   | [HuggingChat](https://huggingface.co/blog/codellama) | 
 
 
  ## Open LLM datasets for pre-training