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Merge pull request #315 from S4MFI/Prompt-Engineering-Guide-FI-updates 

Updated list of LLMs
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@ -9,6 +9,17 @@ This section consists of a collection and summary of notable and foundational LL
| Model | Release Date | Size (B) | Checkpoints | Description |
| --- | --- | --- | --- | --- |
| [Falcon LLM](https://falconllm.tii.ae/) | Sep 2023 | 7, 40, 180 | [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b), [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b), [Falcon-180B](https://huggingface.co/tiiuae/falcon-180B) | Falcon LLM is a foundational large language model (LLM) with 180 billion parameters trained on 3500 Billion tokens. TII has now released Falcon LLM – a 180B model. |
| [Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) | Sep 2023 | 7 | - | Mistral-7B-Instruct-v0.1 is a fine-tuned version of the Mistral-7B-v0.1 generative text model. It's designed for instruction following and uses a variety of publicly available conversation datasets for training. The model is based on a transformer architecture with features like Grouped-Query Attention and Sliding-Window Attention. It doesn't have any moderation mechanisms but is a quick demonstration that the base model can be easily fine-tuned for compelling performance. |
| [Llama-2](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf) | Jul 2023 | 70, 13, 7 | - | LLaMA-2, developed by Meta AI, was released in July 2023 with models of 7, 13, and 70 billion parameters. It maintains a similar architecture to LLaMA-1 but uses 40% more training data. LLaMA-2 includes foundational models and dialog-fine-tuned models, known as LLaMA-2 Chat, and is available for many commercial uses, with some restrictions. |
| [ChatGLM2-6B](https://huggingface.co/THUDM/chatglm2-6b) | Jul 2023 | 6 | - | ChatGLM2-6B is the second-generation version of the open-source bilingual (Chinese-English) chat model ChatGLM-6B. It has improved performance, longer context capabilities, more efficient inference, and an open license for academic and commercial use. The model uses a hybrid objective function and has been trained with 1.4T bilingual tokens. It shows substantial improvements in performance on various datasets compared to its first-generation counterpart. |
| [XGen-7B-8K-Inst](https://huggingface.co/Salesforce/xgen-7b-8k-inst) | Jul 2023 | 7 | - | The XGen-7B-8K-Inst, developed by Salesforce AI Research, is a 7B parameter language model, fine-tuned for instruction following. |
| [CodeLlama](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) | Jul 2023 |7, 13, 34 | - | The Code Llama family is designed for general code synthesis and understanding. It is specifically tuned for instruction following and safer deployment. The models are auto-regressive and use an optimized transformer architecture. They are intended for commercial and research use in English and relevant programming languages. |
| [Claude-2](https://www.anthropic.com/index/claude-2) | Jul 2023 | 130 | - | Claude 2 is a foundational LLM built by Anthropic, designed to be safer and more "steerable" than its previous version. It is conversational and can be used for a variety of tasks like customer support, Q&A, and more. It can process large amounts of text and is well-suited for applications that require handling extensive data, such as documents, emails, FAQs, and chat transcripts. |
| [Nous-Hermes-13B](https://huggingface.co/NousResearch/Nous-Hermes-13b) | Jun 2023 | 13 | - | Nous-Hermes-13B is a language model fine-tuned by Nous Research on over 300,000 instructions. |
| [Baize-v2-13B](https://huggingface.co/project-baize/baize-v2-13b) | Jun 2023 | 13 | - | Baize-v2-13B is an open-source chat model developed by UCSD and Sun Yat-Sen University, fine-tuned with LoRA, and trained with supervised fine-tuning (SFT) and self-distillation with feedback (SDF). It is a 13B parameter model that has been merged with LLaMA and is designed to engage in detailed and informative conversations, adhering to a specific conversational format. Baize, named after a mythical creature in Chinese folklore known to speak human languages and possess vast knowledge, is expected to provide detailed responses and avoid engaging in unethical or sensitive topics. |
| [Tulu-30B](https://huggingface.co/allenai/tulu-30b) | Jun 2023 | 30 | - | Tulu 30B is a model developed by Allen Institute for AI and is a 30B parameter LLaMa model that has been fine-tuned on a mixture of instruction datasets, including FLAN V2, CoT, Dolly, Open Assistant 1, GPT4-Alpaca, Code-Alpaca, and ShareGPT. It is designed to follow complex instructions across various NLP tasks |
| [RWKV-4-Raven-14B](https://huggingface.co/BlinkDL/rwkv-4-raven) | May 2023 | 14 | - | RWKV-4-Raven-14B is part of the RWKV-4 "Raven" series of models. These models are fine-tuned on various datasets like Alpaca, CodeAlpaca, Guanaco, GPT4All, and ShareGPT. The model is designed to be surprisingly good for its size and is developed by BlinkDL. It follows a 100% RNN architecture for its language model. |
| [Guanaco](https://huggingface.co/timdettmers/guanaco-65b-merged) | May 2023 | 7, 13, 33, 65 | - | Guanaco models are open-source chatbots fine-tuned through 4-bit QLoRA tuning of LLaMA base models on the OASST1 dataset. They are intended for research purposes. The models allow for cheap and local experimentation with high-quality chatbot systems. |
| [PaLM 2](https://arxiv.org/abs/2305.10403) | May 2023 | - | - | A Language Model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. |
| [Med-PaLM 2](https://arxiv.org/abs/2305.09617v1) | May 2023 | - | - | Towards Expert-Level Medical Question Answering with Large Language Models |
| [Gorilla](https://arxiv.org/abs/2305.15334v1) | May 2023 | 7 | [Gorilla](https://github.com/ShishirPatil/gorilla) | Gorilla: Large Language Model Connected with Massive APIs |
@ -19,12 +30,22 @@ This section consists of a collection and summary of notable and foundational LL
| [CodeGen2](https://arxiv.org/abs/2305.02309) | May 2023 | 1, 3, 7, 16 | [CodeGen2](https://github.com/salesforce/codegen2) | Code models for program synthesis. |
| [CodeT5 and CodeT5+](https://arxiv.org/abs/2305.07922) | May 2023 | 16 | [CodeT5](https://github.com/salesforce/codet5) | CodeT5 and CodeT5+ models for Code Understanding and Generation from Salesforce Research. |
| [StarCoder](https://huggingface.co/blog/starcoder) | May 2023 | 15 | [StarCoder](https://huggingface.co/bigcode/starcoder) | StarCoder: A State-of-the-Art LLM for Code |
| [MPT-7B](https://www.mosaicml.com/blog/mpt-7b) | May 2023 | 7 | [MPT-7B](https://github.com/mosaicml/llm-foundry#mpt) | MPT-7B is a GPT-style model, and the first in the MosaicML Foundation Series of models. |
| [MPT](https://www.mosaicml.com/blog/mpt-7b) | May 2023 | 7, 30 | [MPT-7B](https://github.com/mosaicml/llm-foundry#mpt) | MosaicML's MPT models are open-source, commercially licensed Large Language Models, offering customizable AI solutions with versions like MPT-30B and MPT-7B, optimized for various NLP tasks. |
| [DLite](https://medium.com/ai-squared/announcing-dlite-v2-lightweight-open-llms-that-can-run-anywhere-a852e5978c6e) | May 2023 | 0.124 - 1.5 | [DLite-v2-1.5B](https://huggingface.co/aisquared/dlite-v2-1_5b) | Lightweight instruction following models which exhibit ChatGPT-like interactivity. |
| [WizardLM](https://huggingface.co/WizardLM/WizardLM-70B-V1.0) | Apr 2023 | 70, 30, 13 | - | WizardLM is a family of large language models designed to follow complex instructions. The models performs well in coding, mathematical reasoning, and open-domain conversations. The models are license-friendly and adopt a prompt format from Vicuna for multi-turn conversations. The models are developed by the WizardLM Team, designed for various NLP tasks. |
| [FastChat-T5-3B](https://huggingface.co/lmsys/fastchat-t5-3b-v1.0) | Apr 2023 | 3 | - | FastChat-T5 is an open-source chatbot trained by fine-tuning Flan-t5-xl (3B parameters) on user-shared conversations collected from ShareGPT. It's based on an encoder-decoder transformer architecture and can autoregressively generate responses to users' inputs. |
| [GPT4All-13B-Snoozy](https://huggingface.co/nomic-ai/gpt4all-13b-snoozy) | Apr 2023 | 13 | - | GPT4All-13B-Snoozy is a GPL licensed chatbot trained over a massive curated corpus of assistant interactions including word problems, multi-turn dialogue, code, poems, songs, and stories. It has been finetuned from LLama 13B and is developed by Nomic AI. The model is designed for assistant-style interaction data and is primarily in English. |
| [Koala-13B](https://bair.berkeley.edu/blog/2023/04/03/koala/) | Apr 2023 | 13 | - | Koala-13B is a chatbot created by Berkeley AI Research (BAIR). It is fine-tuned on Meta's LLaMA and focuses on dialogue data scraped from the web. The model aims to balance performance and cost, providing a lighter, open-source alternative to models like ChatGPT. It has been trained on interaction data that includes conversations with highly capable closed-source models such as ChatGPT. |
| [OpenAssistant (Llama family)](https://huggingface.co/OpenAssistant/oasst-sft-6-llama-30b-xor) | Apr 2023 | 30 | - | OpenAssistant-LLaMA-30B, released on 15th April 2023, is a language model from OpenAssistant's seventh phase of work on the Llama 30B model. Trained on a 30 billion-word dataset, it supports CPU + GPU inference using GGML format and aims to provide an open-source alternative for instruction following tasks​ |
| [Dolly](https://www.databricks.com/blog/2023/04/12/dolly-first-open-commercially-viable-instruction-tuned-llm) | April 2023 | 3, 7, 12 | [Dolly](https://huggingface.co/databricks/dolly-v2-12b) | An instruction-following LLM, fine-tuned on a human-generated instruction dataset licensed for research and commercial use. |
| [StableLM](https://github.com/Stability-AI/StableLM#stablelm-alpha) | April 2023 | 3, 7 | [StableLM-Alpha](https://github.com/Stability-AI/StableLM#stablelm-alpha) | Stability AI's StableLM series of language models |
| [Pythia](https://arxiv.org/abs/2304.01373) | April 2023 | 0.070 - 12 | [Pythia](https://github.com/eleutherai/pythia) | A suite of 16 LLMs all trained on public data seen in the exact same order and ranging in size from 70M to 12B parameters. |
| [Open Assistant (Pythia Family)](https://open-assistant.io/) | March 2023 | 12 | [Open Assistant](https://huggingface.co/OpenAssistant) | OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so. |
| [ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) | Mar 2023 | 6 | - | ChatGLM-6B, is an open-source, Chinese-English bilingual dialogue model based on the General Language Model (GLM) architecture with 6.2 billion parameters. Despite its small size causing some factual or mathematical logic issues, it's adept for Chinese question-answering, summarization, and conversational tasks due to its training on over 1 trillion English and Chinese tokens |
| [GPT-3.5-turbo](https://openai.com/blog/chatgpt) | Mar 2023 | 175 | - | GPT-3.5-Turbo is OpenAI's advanced language model optimized for chat but also works well for traditional completion tasks. It offers better performance across all aspects compared to GPT-3 and is 10 times cheaper per token. |
| [Vicuna](https://huggingface.co/lmsys/vicuna-33b-v1.3) | Mar 2023 | 33, 13, 7 | - | Vicuna is a family of auto-regressive language models based on the transformer architecture. It's fine-tuned from LLaMA and primarily intended for research on large language models and chatbots. It's developed by LMSYS and has a non-commercial license. |
| [Alpaca-13B](https://crfm.stanford.edu/2023/03/13/alpaca.html) | Mar 2023 | 13 | - | Alpaca is an instruction-following language model fine-tuned from Meta's LLaMA 7B. It's designed for academic research to address issues like misinformation and toxicity. Alpaca is trained on 52K instruction-following demonstrations and aims to be a more accessible option for academic study. It's not intended for commercial use due to licensing and safety concerns. |
| [Claude-1](https://www.anthropic.com/index/introducing-claude) | Mar 2023 | 137 | - | Claude is foundational a large language model (LLM) built by Anthropic. It is designed to be a helpful, honest, and harmless AI assistant. It can perform a wide variety of conversational and text processing tasks and is accessible through a chat interface and API. |
| [Cerebras-GPT](https://arxiv.org/abs/2304.03208) | March 2023 | 0.111 - 13 | [Cerebras-GPT](https://huggingface.co/cerebras) | Cerebras-GPT: Open Compute-Optimal Language Models Trained on the Cerebras Wafer-Scale Cluster |
| [BloombergGPT](https://arxiv.org/abs/2303.17564v1)| March 2023 | 50 | - | BloombergGPT: A Large Language Model for Finance|
| [PanGu-Σ](https://arxiv.org/abs/2303.10845v1) | March 2023 | 1085 | - | PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing |