inference: false
language:
- en
license: other
model_creator: Mikael110
model_link: https://huggingface.co/Mikael110/llama-2-70b-guanaco-qlora
model_name: Llama2 70B Guanaco QLoRA
model_type: llama
pipeline_tag: text-classification
quantized_by: TheBloke
tags:
- llama-2
Llama2 70B Guanaco QLoRA - GGML
- Model creator: Mikael110
- Original model: Llama2 70B Guanaco QLoRA
Description
This repo contains GGML format model files for Mikael110's Llama2 70b Guanaco QLoRA.
CUDA GPU acceleration is now available for Llama 2 70B GGML files. Metal acceleration (macOS) is not yet available. I haven't tested AMD acceleration - let me know if it owrks. The following clients/libraries are known to work with these files, including with CUDA GPU acceleration:
- llama.cpp, commit
e76d630and later. - text-generation-webui, the most popular web UI.
- KoboldCpp, version 1.37 and later. A powerful GGML web UI, especially good for story telling.
- llama-cpp-python, version 0.1.77 and later. A Python library with LangChain support, and OpenAI-compatible API server.
Repositories available
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
- Unquantised fp16 model in pytorch format, for GPU inference and for further conversions
- Mikael110's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions
Prompt template: Guanaco
### Human: {prompt}
### Assistant:
Compatibility
Requires llama.cpp commit e76d630 or later.
Or one of the other tools and libraries listed above.
To use in llama.cpp, you must add -gqa 8 argument.
For other UIs and libraries, please check the docs.
Explanation of the new k-quant 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
- GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
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 |
|---|---|---|---|---|---|
| llama-2-70b-guanaco-qlora.ggmlv3.q2_K.bin | q2_K | 2 | 28.59 GB | 31.09 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| llama-2-70b-guanaco-qlora.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 36.15 GB | 38.65 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| llama-2-70b-guanaco-qlora.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 33.04 GB | 35.54 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| llama-2-70b-guanaco-qlora.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 29.75 GB | 32.25 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| llama-2-70b-guanaco-qlora.ggmlv3.q4_0.bin | q4_0 | 4 | 38.87 GB | 41.37 GB | Original quant method, 4-bit. |
| llama-2-70b-guanaco-qlora.ggmlv3.q4_1.bin | q4_1 | 4 | 43.17 GB | 45.67 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| llama-2-70b-guanaco-qlora.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 41.38 GB | 43.88 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| llama-2-70b-guanaco-qlora.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 38.87 GB | 41.37 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| llama-2-70b-guanaco-qlora.ggmlv3.q5_0.bin | q5_0 | 5 | 47.46 GB | 49.96 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| llama-2-70b-guanaco-qlora.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 48.75 GB | 51.25 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| llama-2-70b-guanaco-qlora.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 47.46 GB | 49.96 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
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.
How to run in llama.cpp
I use the following command line; adjust for your tastes and needs:
./main -t 10 -ngl 40 -gqa 8 -m llama-2-70b-guanaco-qlora.ggmlv3.q4_K_M.bin --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Human: Write a story about llamas\n### 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.
Change -ngl 40 to the number of GPU layers you have VRAM for. Use -ngl 100 to offload all layers to VRAM, if you have a 48GB card, or 2 x 24GB, or similar. Otherwise you can partially offload as many as you have VRAM for, on one or more GPUs.
Remember the -gqa 8 argument, required for Llama 70B models.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to run in text-generation-webui
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute.
Thanks to the 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: Luke from CarbonQuill, Aemon Algiz.
Patreon special mentions: Slarti, Chadd, John Detwiler, Pieter, zynix, K, Mano Prime, ReadyPlayerEmma, Ai Maven, Leonard Tan, Edmond Seymore, Joseph William Delisle, Luke @flexchar, Fred von Graf, Viktor Bowallius, Rishabh Srivastava, Nikolai Manek, Matthew Berman, Johann-Peter Hartmann, ya boyyy, Greatston Gnanesh, Femi Adebogun, Talal Aujan, Jonathan Leane, terasurfer, David Flickinger, William Sang, Ajan Kanaga, Vadim, Artur Olbinski, Raven Klaugh, Michael Levine, Oscar Rangel, Randy H, Cory Kujawski, RoA, Dave, Alex, Alexandros Triantafyllidis, Fen Risland, Eugene Pentland, vamX, Elle, Nathan LeClaire, Khalefa Al-Ahmad, Rainer Wilmers, subjectnull, Junyu Yang, Daniel P. Andersen, SuperWojo, LangChain4j, Mandus, Kalila, Illia Dulskyi, Trenton Dambrowitz, Asp the Wyvern, Derek Yates, Jeffrey Morgan, Deep Realms, Imad Khwaja, Pyrater, Preetika Verma, biorpg, Gabriel Tamborski, Stephen Murray, Spiking Neurons AB, Iucharbius, Chris Smitley, Willem Michiel, Luke Pendergrass, Sebastain Graf, senxiiz, Will Dee, Space Cruiser, Karl Bernard, Clay Pascal, Lone Striker, transmissions 11, webtim, WelcomeToTheClub, Sam, theTransient, Pierre Kircher, chris gileta, John Villwock, Sean Connelly, Willian Hasse
Thank you to all my generous patrons and donaters!
Original model card: Mikael110's Llama2 70b Guanaco QLoRA
This is a Llama-2 version of Guanaco. It was finetuned from the base Llama-70b model using the official training scripts found in the QLoRA repo. I wanted it to be as faithful as possible and therefore changed nothing in the training script beyond the model it was pointing to. The model prompt is therefore also the same as the original Guanaco model.
This repo contains the QLoRA adapter.
A 7b version of the adapter can be found here. A 13b version of the adapter can be found here.
Legal Disclaimer: This model is bound by the usage restrictions of the original Llama-2 model. And comes with no warranty or gurantees of any kind.