Hugging Face's logo

TheBloke
/
guanaco-7B-GGML

Transformers
llama
Model card Files Community
3
guanaco-7B-GGML / README.md
TheBloke's picture
TheBloke
Update README.md
91e6d9b
preview code
|
raw
history blame
12.7 kB
metadata
inference: false
license: other
model_type: llama
TheBlokeAI

Tim Dettmers' Guanaco 7B GGML

These files are GGML format model files for Tim Dettmers' Guanaco 7B.

GGML files are for CPU + GPU inference using llama.cpp and libraries and UIs which support this format, such as:

  • KoboldCpp, a powerful GGML web UI with full GPU acceleration out of the box. Especially good for story telling.
  • LoLLMS Web UI, a great web UI with GPU acceleration via the c_transformers backend.
  • LM Studio, a fully featured local GUI. Supports full GPU accel on macOS. Also supports Windows, without GPU accel.
  • text-generation-webui, the most popular web UI. Requires extra steps to enable GPU accel via llama.cpp backend.
  • ctransformers, a Python library with LangChain support and OpenAI-compatible AI server.
  • llama-cpp-python, a Python library with OpenAI-compatible API server.

These files were quantised using hardware kindly provided by Latitude.sh.

Repositories available

Prompt template: Guanaco 

### Human: {prompt}
### Assistant:

Compatibility

Original llama.cpp quant methods: q4_0, q4_1, q5_0, q5_1, q8_0

These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.

New k-quant methods: q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K

These new quantisation methods are compatible with llama.cpp as of June 6th, commit 2d43387.

They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.

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
guanaco-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.
guanaco-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
guanaco-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
guanaco-7B.ggmlv3.q3_K_S.bin q3_K_S 3 2.95 GB 5.45 GB New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
guanaco-7B.ggmlv3.q4_0.bin q4_0 4 3.79 GB 6.29 GB Original quant method, 4-bit.
guanaco-7B.ggmlv3.q4_1.bin q4_1 4 4.21 GB 6.71 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.
guanaco-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
guanaco-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
guanaco-7B.ggmlv3.q5_0.bin q5_0 5 4.63 GB 7.13 GB Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
guanaco-7B.ggmlv3.q5_1.bin q5_1 5 5.06 GB 7.56 GB Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
guanaco-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
guanaco-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
guanaco-7B.ggmlv3.q6_K.bin q6_K 6 5.53 GB 8.03 GB New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
guanaco-7B.ggmlv3.q8_0.bin q8_0 8 7.16 GB 9.66 GB Original 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.

How to run in llama.cpp

I use the following command line; adjust for your tastes and needs:

./main -t 10 -ngl 32 -m guanaco-7B.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"

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 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 -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:

TheBloke AI's Discord server

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.

Special thanks to: Luke from CarbonQuill, Aemon Algiz.

Patreon special mentions: Space Cruiser, Nikolai Manek, Sam, Chris McCloskey, Rishabh Srivastava, Kalila, Spiking Neurons AB, Khalefa Al-Ahmad, WelcomeToTheClub, Chadd, Lone Striker, Viktor Bowallius, Edmond Seymore, Ai Maven, Chris Smitley, Dave, Alexandros Triantafyllidis, Luke @flexchar, Elle, ya boyyy, Talal Aujan, Alex , Jonathan Leane, Deep Realms, Randy H, subjectnull, Preetika Verma, Joseph William Delisle, Michael Levine, chris gileta, K, Oscar Rangel, LangChain4j, Trenton Dambrowitz, Eugene Pentland, Johann-Peter Hartmann, Femi Adebogun, Illia Dulskyi, senxiiz, Daniel P. Andersen, Sean Connelly, Artur Olbinski, RoA, Mano Prime, Derek Yates, Raven Klaugh, David Flickinger, Willem Michiel, Pieter, Willian Hasse, vamX, Luke Pendergrass, webtim, Ghost , Rainer Wilmers, Nathan LeClaire, Will Dee, Cory Kujawski, John Detwiler, Fred von Graf, biorpg, Iucharbius , Imad Khwaja, Pierre Kircher, terasurfer , Asp the Wyvern, John Villwock, theTransient, zynix , Gabriel Tamborski, Fen Risland, Gabriel Puliatti, Matthew Berman, Pyrater, SuperWojo, Stephen Murray, Karl Bernard, Ajan Kanaga, Greatston Gnanesh, Junyu Yang.

Thank you to all my generous patrons and donaters!

Original model card: Tim Dettmers' Guanaco 7B

TheBlokeAI

Tim Dettmers' Guanaco 7B fp16 HF

These files are fp16 HF model files for Tim Dettmers' Guanaco 7B.

It is the result of merging the LoRA then saving in HF fp16 format.

Other repositories available

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

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 special mentions: Aemon Algiz, Dmitriy Samsonov, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, Jonathan Leane, Talal Aujan, V. Lukas, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Sebastain Graf, Johann-Peter Hartman.

Thank you to all my generous patrons and donaters!

Original model card

Not provided by original model creator.