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Meta's LLaMA 65B GGML

These files are GGML format model files for Meta's LLaMA 65B.

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: None

{prompt}

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 They may be phased out soon, as they are largely superseded by the new k-quant methods.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
llama-65b.ggmlv3.q2_K.bin	q2_K	2	27.33 GB	29.83 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-65b.ggmlv3.q3_K_L.bin	q3_K_L	3	34.55 GB	37.05 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
llama-65b.ggmlv3.q3_K_M.bin	q3_K_M	3	31.40 GB	33.90 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-65b.ggmlv3.q3_K_S.bin	q3_K_S	3	28.06 GB	30.56 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
llama-65b.ggmlv3.q4_0.bin	q4_0	4	36.73 GB	39.23 GB	Original quant method, 4-bit.
llama-65b.ggmlv3.q4_1.bin	q4_1	4	40.81 GB	43.31 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-65b.ggmlv3.q4_K_M.bin	q4_K_M	4	39.28 GB	41.78 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-65b.ggmlv3.q4_K_S.bin	q4_K_S	4	36.73 GB	39.23 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
llama-65b.ggmlv3.q5_0.bin	q5_0	5	44.89 GB	47.39 GB	Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
llama-65b.ggmlv3.q5_1.bin	q5_1	5	48.97 GB	51.47 GB	Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
llama-65b.ggmlv3.q5_K_M.bin	q5_K_M	5	46.20 GB	48.70 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-65b.ggmlv3.q5_K_S.bin	q5_K_S	5	44.89 GB	47.39 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
llama-65b.ggmlv3.q6_K.bin	q6_K	6	53.56 GB	56.06 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
llama-65b.ggmlv3.q8_0.bin	q8_0	8	69.370 GB	71.87 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.

q6_K and q8_0 files require expansion from archive

Note: HF does not support uploading files larger than 50GB. Therefore I have uploaded the q6_K and q8_0 files as multi-part ZIP files. They are not compressed, it is just storing the .bin file in two parts.

q6_K

Please download:

llama-65b.ggmlv3.q6_K.zip
llama-65b.ggmlv3.q6_K.z01

q8_0

Please download:

llama-65b.ggmlv3.q8_0.zip
llama-65b.ggmlv3.q8_0.z01

Then extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use 7zip - the basic unzip tool did not work. Example:

sudo apt update -y && sudo apt install 7zip
7zz x llama-65b.ggmlv3.q6_K.zip`

Once the .bin is extracted you can delete the .zip and .z01 files

How to run in `llama.cpp`

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

./main -t 10 -ngl 32 -m llama-65b.ggmlv3.q4_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Llamas are nice because"

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

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TheBloke AI's Discord server

Thanks, and how to contribute.

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Original model card: Meta's LLaMA 65B

This contains the weights for the LLaMA-65b model. This model is under a non-commercial license (see the LICENSE file). You should only use this repository if you have been granted access to the model by filling out this form but either lost your copy of the weights or got some trouble converting them to the Transformers format.