## Dromedary-65B-LoRA GGML
These files are the result of merging the [delta weights of IBM's Dromedary 65B LoRA](https://huggingface.co./zhiqings/dromedary-65b-lora-delta-v0) with the original Llama 65B model.
This repo contains GGML files for for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp).
## Repositories available
* [4bit GPTQ models for GPU inference](https://huggingface.co./TheBloke/dromedary-65B-lora-GPTQ)
* [4bit and 5bit GGML models for CPU inference in llama.cpp](https://huggingface.co./TheBloke/dromedary-65B-lora-GGML)
* [float16 unquantised model for GPU](https://huggingface.co./TheBloke/dromedary-65B-lora-HF)
## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)!
llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508
I have quantised the GGML files in this repo with the latest version. Therefore you will require llama.cpp compiled on May 19th or later (commit `2d5db48` or later) to use them.
For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`.
## Provided files
| Name | Quant method | Bits | Size | RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
`dromedary-lora-65B.ggmlv3.q4_0.bin` | q4_0 | 4bit | 40.8GB | 43GB | 4-bit. |
`dromedary-lora-65B.ggmlv3.q4_1.bin` | q4_1 | 4bit | 44.9GB | 47GB | 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
`dromedary-lora-65B.ggmlv3.q5_0.bin` | q5_0 | 5bit | 44.9GB | 47GB | 5-bit. Higher accuracy, higher resource usage and slower inference. |
`dromedary-lora-65B.ggmlv3.q5_1.bin` | q5_1 | 5bit | 49GB | 51GB | 5-bit. Even higher accuracy, higher resource usage and slower inference. |
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://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.
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Thank you to all my generous patrons and donaters!
# Original Dromedary Model Card
See https://github.com/IBM/Dromedary#model-weights for instructions.
## Model details
**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.