--- license: other language: - en pipeline_tag: text-generation inference: false tags: - transformers - gguf - imatrix - OpenOrcaxOpenChat-Preview2-13B --- Quantizations of https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B ### Inference Clients/UIs * [llama.cpp](https://github.com/ggerganov/llama.cpp) * [KoboldCPP](https://github.com/LostRuins/koboldcpp) * [ollama](https://github.com/ollama/ollama) * [text-generation-webui](https://github.com/oobabooga/text-generation-webui) * [GPT4All](https://github.com/nomic-ai/gpt4all) * [jan](https://github.com/janhq/jan) --- # From original readme We have used our own [OpenOrca dataset](https://huggingface.co/datasets/Open-Orca/OpenOrca) to fine-tune Llama2-13B using [OpenChat](https://huggingface.co/openchat) packing. This dataset is our attempt to reproduce the dataset generated for Microsoft Research's [Orca Paper](https://arxiv.org/abs/2306.02707). This second preview release is trained on a curated filtered subset of most of our GPT-4 augmented data. This release highlights that our dataset and training methods have surpassed performance parity with the Orca paper. We measured this with BigBench-Hard and AGIEval results with the same methods as used in the Orca paper, finding **~103%** of original Orca's performance on average. As well, this is done with <1/10th the compute requirement and using <20% of the dataset size from the original Orca paper. We have run extensive evaluations internally and expect this model to **place number 1** on both the HuggingFaceH4 Open LLM Leaderboard and the GPT4ALL Leaderboard for 13B models. "One" of [OpenChat](https://huggingface.co/openchat) has joined our team, and we'd like to provide special thanks for their training of this model! We have utilized OpenChat [MultiPack algorithm](https://github.com/imoneoi/multipack_sampler) which achieves 99.85% bin-packing efficiency on our dataset. This has significantly reduced training time, with efficiency improvement of 3-10X over traditional methods. Want to visualize our full (pre-filtering) dataset? Check out our [Nomic Atlas Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2). [Atlas Nomic Dataset Map](https://atlas.nomic.ai/map/c1b88b47-2d9b-47e0-9002-b80766792582/2560fd25-52fe-42f1-a58f-ff5eccc890d2) We are in-process with training more models, so keep a look out on our org for releases coming soon with exciting partners. We will also give sneak-peak announcements on our Discord, which you can find here: https://AlignmentLab.ai # Prompt Template We use our own prompt template which we call "`OpenChat Llama2 V1`". The model is heavily conditioned to work using this format only and will likely encounter issues such as run-on output which emulates a chat between a user and assistant if this format is not properly followed. Examples: ``` # Single-turn `OpenChat Llama2 V1` tokenize("You are OpenOrcaChat.<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:") # [1, 887, 526, 4673, 2816, 1113, 1451, 271, 29889, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901] # Multi-turn `OpenChat Llama2 V1` tokenize("You are OpenOrcaChat.<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:") # [1, 887, 526, 4673, 2816, 1113, 1451, 271, 29889, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901] ``` For UIs with Prefix and Suffix fields, these will likely work: Prefix (include a space after colon): ``` User: ``` Suffix (space after colon): ``` <|end_of_turn|>\nAssistant: ``` **Oobabooga's text-generation-webui instructions can be found [further down the page](https://huggingface.co/Open-Orca/OpenOrcaxOpenChat-Preview2-13B#serving-with-oobabooga--text-generation-webui).**