--- datasets: - ehartford/dolphin - shahules786/orca-chat - togethercomputer/RedPajama-Data-1T - atom-in-the-universe/fanfics-10k-50k inference: false language: - en license: llama2 model_creator: OpenAssistant model_link: https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319 model_name: Llama2 13B Orca 8K 3319 model_type: llama pipeline_tag: text-generation quantized_by: TheBloke tags: - sft widget: - text: <|system|>You are an AI assistant. You will be given a task. You must generate a detailed and long answer.<|prompter|>What is a meme, and what's the history behind this word?<|assistant|> - text: <|system|>You are an AI assistant that helps people find information.<|prompter|>What's the Earth total population<|assistant|> - text: <|system|>You are an AI assistant that follows instruction extremely well. Help as much as you can.<|prompter|>Write a story about future of AI development<|assistant|> ---
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# Llama2 13B Orca 8K 3319 - GGUF - Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant) - Original model: [Llama2 13B Orca 8K 3319](https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319) ## Description This repo contains GGUF format model files for [OpenAssistant's Llama2 13B Orca 8K 3319](https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319). ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. The key benefit of GGUF is that it is a extensible, future-proof format which stores more information about the model as metadata. It also includes significantly improved tokenization code, including for the first time full support for special tokens. This should improve performance, especially with models that use new special tokens and implement custom prompt templates. Here are a list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with full GPU accel across multiple platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ## Repositories available * [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GPTQ) * [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF) * [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGML) * [OpenAssistant's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/OpenAssistant/llama2-13b-orca-8k-3319) ## Prompt template: OpenAssistant-System ``` <|system|>{system_message}<|prompter|>{prompt}<|assistant|> ``` ## Compatibility These quantised GGUF files are compatible with llama.cpp from August 21st 2023 onwards, as of commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9) They are now also compatible with many third party UIs and libraries - please see the list at the top of the README. ## Explanation of quantisation methods
Click to see details The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw Refer to the Provided Files table below to see what files use which methods, and how.
## Provided files | Name | Quant method | Bits | Size | Max RAM required | Use case | | ---- | ---- | ---- | ---- | ---- | ----- | | [openassistant-llama2-13b-orca-8k-3319.Q2_K.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes | | [openassistant-llama2-13b-orca-8k-3319.Q3_K_S.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss | | [openassistant-llama2-13b-orca-8k-3319.Q3_K_M.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss | | [openassistant-llama2-13b-orca-8k-3319.Q3_K_L.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss | | [openassistant-llama2-13b-orca-8k-3319.Q4_0.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M | | [openassistant-llama2-13b-orca-8k-3319.Q4_K_S.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss | | [openassistant-llama2-13b-orca-8k-3319.Q4_K_M.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended | | [openassistant-llama2-13b-orca-8k-3319.Q5_0.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M | | [openassistant-llama2-13b-orca-8k-3319.Q5_K_S.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended | | [openassistant-llama2-13b-orca-8k-3319.Q5_K_M.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended | | [openassistant-llama2-13b-orca-8k-3319.Q6_K.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss | | [openassistant-llama2-13b-orca-8k-3319.Q8_0.gguf](https://huggingface.co/TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF/blob/main/openassistant-llama2-13b-orca-8k-3319.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended | **Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead. ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9](https://github.com/ggerganov/llama.cpp/commit/6381d4e110bd0ec02843a60bbeb8b6fc37a9ace9) or later. For compatibility with older versions of llama.cpp, or for any third-party libraries or clients that haven't yet updated for GGUF, please use GGML files instead. ``` ./main -t 10 -ngl 32 -m openassistant-llama2-13b-orca-8k-3319.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|system|>{system_message}<|prompter|>{prompt}<|assistant|>" ``` Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`. If offloading all layers to GPU, set `-t 1`. Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 4096` to the desired sequence length for this model. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. If you want to have a chat-style conversation, replace the `-p ` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. ### How to load this model from Python using ctransformers #### First install the package ```bash # Base ctransformers with no GPU acceleration pip install ctransformers>=0.2.24 # Or with CUDA GPU acceleration pip install ctransformers[cuda]>=0.2.24 # Or with ROCm GPU acceleration CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers # Or with Metal GPU acceleration for macOS systems CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers ``` #### Simple example code to load one of these GGUF models ```python from ctransformers import AutoModelForCausalLM # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = AutoModelForCausalLM.from_pretrained("TheBloke/OpenAssistant-Llama2-13B-Orca-8K-3319-GGUF", model_file="openassistant-llama2-13b-orca-8k-3319.q4_K_M.gguf", model_type="llama", gpu_layers=50) print(llm("AI is going to")) ``` ## How to use with LangChain Here's guides on using llama-cpp-python or ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) ## Discord For further support, and discussions on these models and AI in general, join us at: [TheBloke AI's Discord server](https://discord.gg/theblokeai) ## Thanks, and how to contribute. Thanks to the [chirper.ai](https://chirper.ai) team! I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training. If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects. Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits. * Patreon: https://patreon.com/TheBlokeAI * Ko-Fi: https://ko-fi.com/TheBlokeAI **Special thanks to**: Aemon Algiz. **Patreon special mentions**: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser Thank you to all my generous patrons and donaters! And thank you again to a16z for their generous grant. # Original model card: OpenAssistant's Llama2 13B Orca 8K 3319 # llama2-13b-orca-8k-3319 ## Model Description This model is a fine-tuning of Meta's Llama2 13B model with 8K context size on a long-conversation variant of the Dolphin dataset ([orca-chat](https://huggingface.co/datasets/shahules786/orca-chat)). Note: **At least Huggingface Transformers [4.31.0](https://pypi.org/project/transformers/4.31.0/) is required to load this model!** ## Usage ```python import torch from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained("OpenAssistant/llama2-13b-orca-8k-3319", use_fast=False) model = AutoModelForCausalLM.from_pretrained("OpenAssistant/llama2-13b-orca-8k-3319", torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto") system_message = "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information." user_prompt = "Write me a poem please" prompt = f"""<|system|>{system_message}<|prompter|>{user_prompt}<|assistant|>""" inputs = tokenizer(prompt, return_tensors="pt").to("cuda") output = model.generate(**inputs, do_sample=True, top_p=0.95, top_k=0, max_new_tokens=256) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ## Model Details - base model: [meta-llama/Llama-2-13b](https://huggingface.co/meta-llama/Llama-2-13b) - License: [Llama 2 Community License Agreement](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) - sampling report: [2023-07-25_OpenAssistant_llama2-13b-orca-8k-3319_sampling_llama2_prompt.json](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-pretrained%2F2023-07-25_OpenAssistant_llama2-13b-orca-8k-3319_sampling_llama2_prompt.json) - wandb: [public-sft/runs/2jfazjt9](https://wandb.ai/open-assistant/public-sft/runs/2jfazjt9) - checkpoint: 3319 steps - datatpye: fp16 - sponsored by: [Redmond.ai](https://redmond.ai/) ## Long context (RoPE Scaling) This model was fine-tuned with a context size of 8192 tokens using linear scaling of RoPE embeddings. This feature was recently added to [Huggingface transformers](https://github.com/huggingface/transformers/). Before loading this model please make sure HF transformers >=4.31.0 is installed (`pip install transformers>=4.31.0`). ## Conversation Template For the initial response use (e.g. the [llama2 default system prompt](https://github.com/facebookresearch/llama/blob/6c7fe276574e78057f917549435a2554000a876d/llama/generation.py#L46) works well): ``` <|system|>system message<|prompter|>user prompt<|assistant|> ``` For multi-turn conversations use: ``` <|system|>system message<|prompter|>Q1<|assistant|>A1<|prompter|>Q2<|assistant|> ``` The model was trained with the following 15 system messages used to generate the training examples (see [ORCA paper](https://arxiv.org/abs/2306.02707)): 1. You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer. 2. You are an AI assistant. You will be given a task. You must generate a detailed and long answer. 3. You are a helpful assistant, who always provide explanation. Think like you are answering to a five year old. 4. You are an AI assistant that follows instruction extremely well. Help as much as you can. 5. You are an AI assistant that helps people find information. Provide a detailed answer so user don’t need to search outside to understand the answer. 6. You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps. 7. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. Think like you are answering to a five year old. 8. Explain how you used the definition to come up with the answer. 9. You are an AI assistant. You should describe the task and explain your answer. While answering a multiple choice question, first output the correct answer(s). Then explain why other answers are wrong. You might need to use additional knowledge to answer the question. 10. You are an AI assistant that helps people find information. User will you give you a question. Your task is to answer as faithfully as you can. While answering think step-by- step and justify your answer. 11. User will you give you a task with some instruction. Your job is follow the instructions as faithfully as you can. While answering think step-by-step and justify your answer. 12. You are a teacher. Given a task, you explain in simple steps what the task is asking, any guidelines it provides and how to use those guidelines to find the answer. 13. You are an AI assistant, who knows every language and how to translate one language to another. Given a task, you explain in simple steps what the task is asking, any guidelines that it provides. You solve the task and show how you used the guidelines to solve the task. 14. Given a definition of a task and a sample input, break the definition into small parts. Each of those parts will have some instruction. Explain their meaning by showing an example that meets the criteria in the instruction. Use the following format: Part \#: a key part of the definition. Usage: Sample response that meets the criteria from the key part. Explain why you think it meets the criteria. 15. You are an AI assistant that helps people find information. ## Datasets: Orca-Chat/Dolphin, RedPajama1T & FanFics This model was trained on: - [shahules786/orca-chat](https://huggingface.co/datasets/shahules786/orca-chat) - [togethercomputer/RedPajama-Data-1T-Sample](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) - [atom-in-the-universe/fanfics-10k-50k](https://huggingface.co/datasets/atom-in-the-universe/fanfics-10k-50k) ``` Dataset Composition: Tain (sampled): orca-chat: 188842 (100%) fanfics: 47760 (100%) red_pajama: 188262 (25%) Valid: orca-chat: 5000 fanfics: 1000 red_pajama: 1000 ``` The dataset [shahules786/orca-chat](https://huggingface.co/datasets/shahules786/orca-chat) combines similar examples of the GPT-4 subset of [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin) to form longer conversations to improve long-context training. Additionally, RedPajama and FanFics were used for classic language modelling as an auxiliary task to improve the RoPE scaling for the 8k context size. ## Model Configuration ``` llama2_13b_orca_8k: rng_seed: 0xe1291f1a use_custom_sampler: true sort_by_length: false dtype: fp16 log_dir: "llama2_log_13b_orca_8k" learning_rate: 1e-5 model_name: /mnt/data/llama2/Llama-2-13b-hf/ output_dir: llama2_13b_orca_8k deepspeed_config: configs/zero_config_pretrain.json weight_decay: 0.0 max_length: 8192 warmup_steps: 100 use_flash_attention: true gradient_checkpointing: true gradient_accumulation_steps: 8 per_device_train_batch_size: 2 per_device_eval_batch_size: 1 residual_dropout: 0.0 eval_steps: 200 save_steps: 1000 # (total steps: 3319) num_train_epochs: 1 save_total_limit: 4 superhot: true superhot_config: type: linear scale: 2 datasets: - orca-chat: max_val_set: 5000 - fanfics: max_chunk_size: 65535 max_val_set: 1000 - red_pajama: fraction: 0.25 max_val_set: 1000 max_chunk_size: 65535 peft_model: false ``` # Developers - [shahules786](https://github.com/shahules786) - [jordiclive](https://github.com/jordiclive) - [andreaskoepf](https://github.com/andreaskoepf/) # Special Thanks We want to especially thank Eric Hartford who spared no expense in replicating ORCA and making it available at [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin)! Also, shoutout to the whole team working on [LLongMA-2-13b](https://huggingface.co/conceptofmind/LLongMA-2-13b) & the [scaled-rope](https://github.com/jquesnelle/scaled-rope) repository for their awesome work: bloc97, jquesnelle & conceptofmind! The whole Open-Assistant team is very grateful for the continued support of [Redmond.ai](https://redmond.ai/) who sponsored the training compute required for this model. # License - Llama 2 is licensed under the LLAMA 2 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved. - Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the [Acceptable Use Policy](https://ai.meta.com/llama/use-policy) for the Llama Materials.