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--- |
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license: other |
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inference: false |
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--- |
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## Dromedary-65B-LoRA GGML |
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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. |
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This repo contains GGML files for for CPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp). |
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## Repositories available |
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* [4bit GPTQ models for GPU inference](https://huggingface.co./TheBloke/dromedary-65B-lora-GPTQ) |
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* [4bit and 5bit GGML models for CPU inference in llama.cpp](https://huggingface.co./TheBloke/dromedary-65B-lora-GGML) |
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* [float16 unquantised model for GPU](https://huggingface.co./TheBloke/dromedary-65B-lora-HF) |
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## THE FILES IN MAIN BRANCH REQUIRES LATEST LLAMA.CPP (May 19th 2023 - commit 2d5db48)! |
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llama.cpp recently made another breaking change to its quantisation methods - https://github.com/ggerganov/llama.cpp/pull/1508 |
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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. |
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For files compatible with the previous version of llama.cpp, please see branch `previous_llama_ggmlv2`. |
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## Provided files |
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| Name | Quant method | Bits | Size | RAM required | Use case | |
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| ---- | ---- | ---- | ---- | ---- | ----- | |
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`dromedary-lora-65B.ggmlv3.q4_0.bin` | q4_0 | 4bit | 40.8GB | 43GB | 4-bit. | |
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`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. | |
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`dromedary-lora-65B.ggmlv3.q5_0.bin` | q5_0 | 5bit | 44.9GB | 47GB | 5-bit. Higher accuracy, higher resource usage and slower inference. | |
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`dromedary-lora-65B.ggmlv3.q5_1.bin` | q5_1 | 5bit | 49GB | 51GB | 5-bit. Even higher accuracy, higher resource usage and slower inference. | |
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# Original Dromedary Model Card |
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See https://github.com/IBM/Dromedary#model-weights for instructions. |
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## Model details |
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<div align="center"> |
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<img src="https://raw.githubusercontent.com/IBM/Dromedary/main/assets/images/dromedary_logo.svg" alt="Dromedary Logo"/> |
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</div> |
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**Model type:** |
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Dromedary is an open-source self-aligned language model trained with minimal human supervision. |
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The base language model is LLaMA-65b, based on the transformer architecture. |
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**Model date:** |
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Dromedary was trained between April 2023 and May 2023, but its knowledge only goes up until Sept-2021. |
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**Organizations developing the model:** |
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The Dromedary team as a joint effort between CMU and IBM. |
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**Paper or resources for more information:** |
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https://mitibmdemos.draco.res.ibm.com/dromedary |
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**License:** |
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LLaMA's Non-commercial bespoke license |
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**Where to send questions or comments about the model:** |
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https://github.com/IBM/Dromedary/issues |
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## Intended use |
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**Primary intended uses:** |
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The primary use of Dromedary is research on the alignment of large language models. |
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**Primary intended users:** |
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The primary intended users of the model are researchers in artificial intelligence. |
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## Delta weights |
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We use the following configuration for the LoRA weights: |
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``` |
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--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \ |
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--lora_r=16 \ |
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``` |
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## Training dataset |
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Fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning), |
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## Evaluation dataset |
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We evaluate Dromedary on TruthfulQA and HHH Eval, as well as Vicuna benchmark questions. |
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