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CalderaAI's 13B BlueMethod GGML

These files are GGML format model files for CalderaAI's 13B BlueMethod.

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.

Repositories available

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction: {prompt}

### Response:

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 They may be phased out soon, as they are largely superseded by the new k-quant methods.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
13b-bluemethod.ggmlv3.q2_K.bin	q2_K	2	5.51 GB	8.01 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.
13b-bluemethod.ggmlv3.q3_K_L.bin	q3_K_L	3	6.93 GB	9.43 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
13b-bluemethod.ggmlv3.q3_K_M.bin	q3_K_M	3	6.31 GB	8.81 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
13b-bluemethod.ggmlv3.q3_K_S.bin	q3_K_S	3	5.66 GB	8.16 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
13b-bluemethod.ggmlv3.q4_0.bin	q4_0	4	7.32 GB	9.82 GB	Original quant method, 4-bit.
13b-bluemethod.ggmlv3.q4_1.bin	q4_1	4	8.14 GB	10.64 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.
13b-bluemethod.ggmlv3.q4_K_M.bin	q4_K_M	4	7.87 GB	10.37 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
13b-bluemethod.ggmlv3.q4_K_S.bin	q4_K_S	4	7.37 GB	9.87 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
13b-bluemethod.ggmlv3.q5_0.bin	q5_0	5	8.95 GB	11.45 GB	Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference.
13b-bluemethod.ggmlv3.q5_1.bin	q5_1	5	9.76 GB	12.26 GB	Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference.
13b-bluemethod.ggmlv3.q5_K_M.bin	q5_K_M	5	9.23 GB	11.73 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
13b-bluemethod.ggmlv3.q5_K_S.bin	q5_K_S	5	8.97 GB	11.47 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
13b-bluemethod.ggmlv3.q6_K.bin	q6_K	6	10.68 GB	13.18 GB	New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization
13b-bluemethod.ggmlv3.q8_0.bin	q8_0	8	13.83 GB	16.33 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 13b-bluemethod.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.

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Special thanks to: Luke from CarbonQuill, Aemon Algiz.

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Thank you to all my generous patrons and donaters!

Original model card: CalderaAI's 13B BlueMethod

13B-BlueMethod

Composition:

BlueMethod is a bit of a convoluted experiment in tiered merging. Furthering the experimental nature of the merge, the models combined were done so with a custom script that randomized the percent of each layer merged from one model to the next. This is a warmup for a larger project. [Tier One and Two Merges not released; internal naming convention]

Tier One Merges:

13B-Metharme+13B-Nous-Hermes=13B-Methermes

13B-Vicuna-cocktail+13B-Manticore=13B-Vicortia

13B-HyperMantis+13B-Alpacino=13B-PsychoMantis

Tier Two Merges:

13B-Methermes+13B-Vicortia=13B-Methphistopheles

13B-PsychoMantis+13B-BlueMoonRP=13B-BlueMantis

Tier Three Merge:

13B-Methphistopheles+13B-BlueMantis=13B-BlueMethod

Use:

Multiple instruct models and model composites were combined to make the final resulting model; This model is highly open to experimental prompting, both Alpaca and Vicuna instruct can be used. It can have interesting results.

Language Models and LoRAs Used Credits:

13B-Metharme by PygmalionAI

https://www.huggingface.co/PygmalionAI/metharme-13b

13B-Nous-Hermes by NousResearch

https://www.huggingface.co/NousResearch/Nous-Hermes-13b

13B-Vicuna-cocktail by reeducator

https://www.huggingface.co/reeducator/vicuna-13b-cocktail

13B-Manticore by openaccess-ai-collective

https://www.huggingface.co/openaccess-ai-collective/manticore-13b

13B-HyperMantis and 13B-Alpacino by Digitous

https://huggingface.co/digitous/13B-HyperMantis https://huggingface.co/digitous/Alpacino13b

Also thanks to Meta for LLaMA.

Each model and LoRA was hand picked and considered for what it could contribute to this ensemble. Thanks to each and every one of you for your incredible work developing some of the best things to come out of this community.