inference: false license: other
These files are GGML format model files for Koala 7B.
GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:
- 4-bit GPTQ models for GPU inference
- 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
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
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.
|Name||Quant method||Bits||Size||Max RAM required||Use case|
|koala-7B.ggmlv3.q2_K.bin||q2_K||2||2.80 GB||5.30 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.|
|koala-7B.ggmlv3.q3_K_L.bin||q3_K_L||3||3.55 GB||6.05 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|
|koala-7B.ggmlv3.q3_K_M.bin||q3_K_M||3||3.23 GB||5.73 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|
|koala-7B.ggmlv3.q3_K_S.bin||q3_K_S||3||2.90 GB||5.40 GB||New k-quant method. Uses GGML_TYPE_Q3_K for all tensors|
|koala-7B.ggmlv3.q4_0.bin||q4_0||4||3.79 GB||6.29 GB||Original llama.cpp quant method, 4-bit.|
|koala-7B.ggmlv3.q4_1.bin||q4_1||4||4.21 GB||6.71 GB||Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.|
|koala-7B.ggmlv3.q4_K_M.bin||q4_K_M||4||4.05 GB||6.55 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|
|koala-7B.ggmlv3.q4_K_S.bin||q4_K_S||4||3.79 GB||6.29 GB||New k-quant method. Uses GGML_TYPE_Q4_K for all tensors|
|koala-7B.ggmlv3.q5_0.bin||q5_0||5||4.63 GB||7.13 GB||Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.|
|koala-7B.ggmlv3.q5_1.bin||q5_1||5||5.06 GB||7.56 GB||Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference.|
|koala-7B.ggmlv3.q5_K_M.bin||q5_K_M||5||4.77 GB||7.27 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|
|koala-7B.ggmlv3.q5_K_S.bin||q5_K_S||5||4.63 GB||7.13 GB||New k-quant method. Uses GGML_TYPE_Q5_K for all tensors|
|koala-7B.ggmlv3.q6_K.bin||q6_K||6||5.53 GB||8.03 GB||New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors|
|koala-7B.ggmlv3.q8_0.bin||q8_0||8||7.16 GB||9.66 GB||Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.|
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.
I use the following command line; adjust for your tastes and needs:
./main -t 10 -ngl 32 -m koala-7B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
-t 10 to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use
-ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the
-p <PROMPT> argument with
Further instructions here: text-generation-webui/docs/llama.cpp-models.md.
For further support, and discussions on these models and AI in general, join us at:
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.
Special thanks to: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
Patreon special mentions: Oscar Rangel, Eugene Pentland, Talal Aujan, Cory Kujawski, Luke, Asp the Wyvern, Ai Maven, Pyrater, Alps Aficionado, senxiiz, Willem Michiel, Junyu Yang, trip7s trip, Sebastain Graf, Joseph William Delisle, Lone Striker, Jonathan Leane, Johann-Peter Hartmann, David Flickinger, Spiking Neurons AB, Kevin Schuppel, Mano Prime, Dmitriy Samsonov, Sean Connelly, Nathan LeClaire, Alain Rossmann, Fen Risland, Derek Yates, Luke Pendergrass, Nikolai Manek, Khalefa Al-Ahmad, Artur Olbinski, John Detwiler, Ajan Kanaga, Imad Khwaja, Trenton Dambrowitz, Kalila, vamX, webtim, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
This repo contains the weights diff against the base LLaMA for the Koala model. Check out the following links to get started:
- Blog post
- Online demo
- EasyLM: training and serving framework on GitHub
- Documentation for running Koala locally