UltraLM-13B-GGML

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Open BMB's UltraLM 13B GGML

These files are GGML format model files for Open BMB's UltraLM 13B.

Note: I cannot make GGML k-quants for this model due to its vocab size of 32,001. Please see Compatibility below for more detail.

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

• text-generation-webui
• KoboldCpp
• LoLLMS Web UI
• llama-cpp-python
• ctransformers

Repositories available

• 4-bit GPTQ models for GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference
• Merged, unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Vicuna 1.1

USER: prompt
ASSISTANT:

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Compatibility

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

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 2d5db48.

These are guaranteed to be compatbile with any UIs, tools and libraries released since late May.

New k-quant methods not compatible with this model at this time

Unfortunately this model has a vocab size of 32,001. This breaks compatibility with the new GGML k-quant method. I cannot make k-quants for this reason.

For further explanation, please see:

• https://huggingface.co/openbmb/UltraLM-13b/discussions/1
• https://github.com/ggerganov/llama.cpp/issues/1919

<!-- compatibility_ggml end -->

Provided files

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 ultralm-13b.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:"

If you're able to use full GPU offloading, you should use -t 1 to get best performance.

If not able to fully offload to GPU, you should use more cores. Change -t 10 to the number of physical CPU cores you have, or a lower number depending on what gives best performance.

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.

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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: https://patreon.com/TheBlokeAI
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Thank you to all my generous patrons and donaters!

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Original model card: Open BMB's UltraLM 13B

UltraLM-13b

<!-- Provide a quick summary of what the model is/does. -->

This is UltraLM-13b delta weights, a chat language model trained upon UltraChat

Model Details

Model Description

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The model is fine-tuned based on LLaMA-13b with a multi-turn chat-format template as below

User: instruction 1<eos_token>
Assistant: response 1<eos_token>
User: instruction 2<eos_token>
Assistant: response 2<eos_token>
...

• License: UltraLM is based on LLaMA and should be used under LLaMA's model license.
• Finetuned from model: LLaMA-13b
• Finetuned on data: UltraChat

Model Sources

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• Repository: UltraChat
• Paper: arxiv
• Demo: [More Information Needed]

Uses

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To use this model, you need to recover the full model from the delta weights and perform inference following the template below:

[Optional]User: system prompt<eos_token>
User: user input<eos_token>
Assistant: