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Dromedary-65B-LoRA GGML

These files are the result of merging the delta weights of IBM's Dromedary 65B LoRA with the original Llama 65B model.

This repo contains GGML files for for CPU inference using llama.cpp.

Repositories available

Provided files

Name Quant method Bits Size RAM required Use case
dromedary-lora-65B.ggml.q4_0.bin q4_0 4bit 40.8GB 43GB Maximum compatibility
dromedary-lora-65B.ggml.q4_2.bin q4_2 4bit 40.8GB 43GB Best compromise between resources, speed and quality
dromedary-lora-65B.ggml.q5_0.bin q5_0 5bit 44.9GB 47GB Brand new 5bit method. Potentially higher quality than 4bit, at cost of slightly higher resources.
dromedary-lora-65B.ggml.q5_1.bin q5_1 5bit 49GB 51GB Brand new 5bit method. Slightly higher resource usage than q5_0.
  • The q4_0 file provides lower quality, but maximal compatibility. It will work with past and future versions of llama.cpp
  • The q4_2 file offers the best combination of performance and quality. This format is still subject to change and there may be compatibility issues, see below.
  • The q5_0 file is using brand new 5bit method released 26th April. This is the 5bit equivalent of q4_0.
  • The q5_1 file is using brand new 5bit method released 26

Original Dromedary Model Card

See https://github.com/IBM/Dromedary#model-weights for instructions.

Model details

Dromedary Logo

Model type: Dromedary is an open-source self-aligned language model trained with minimal human supervision. The base language model is LLaMA-65b, based on the transformer architecture.

Model date: Dromedary was trained between April 2023 and May 2023, but its knowledge only goes up until Sept-2021.

Organizations developing the model: The Dromedary team as a joint effort between CMU and IBM.

Paper or resources for more information: https://mitibmdemos.draco.res.ibm.com/dromedary

License: LLaMA's Non-commercial bespoke license

Where to send questions or comments about the model: https://github.com/IBM/Dromedary/issues

Intended use

Primary intended uses: The primary use of Dromedary is research on the alignment of large language models.

Primary intended users: The primary intended users of the model are researchers in artificial intelligence.

Delta weights

We use the following configuration for the LoRA weights:

--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \

Training dataset

Fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning),

Evaluation dataset

We evaluate Dromedary on TruthfulQA and HHH Eval, as well as Vicuna benchmark questions.