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Specify absolute path in READMEs

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Sypherd 2023-09-19 08:05:26 -06:00
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8
README.md
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@ -64,9 +64,9 @@ Download the corresponding LLM weights from the following huggingface space via
[Downlad](https://huggingface.co/Vision-CAIR/vicuna/tree/main) | [Download](https://huggingface.co/Vision-CAIR/vicuna-7b/tree/main) | [Download](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf/tree/main)
Then, set the path to the vicuna weight in the model config file
Then, set the absolute path to the vicuna weight in the model config file
[here](minigpt4/configs/models/minigpt4_vicuna0.yaml#L18) at Line 18
and/or the path to the llama2 weight in the model config file
and/or the absolute path to the llama2 weight in the model config file
[here](minigpt4/configs/models/minigpt4_llama2.yaml#L15) at Line 15.
**3. Prepare the pretrained MiniGPT-4 checkpoint**
@ -78,7 +78,7 @@ Download the pretrained checkpoints according to the Vicuna model you prepare.
[Downlad](https://drive.google.com/file/d/1a4zLvaiDBr-36pasffmgpvH5P7CKmpze/view?usp=share_link) | [Download](https://drive.google.com/file/d/1RY9jV0dyqLX-o38LrumkKRh6Jtaop58R/view?usp=sharing) | [Download](https://drive.google.com/file/d/11nAPjEok8eAGGEG1N2vXo3kBLCg0WgUk/view?usp=sharing)
Then, set the path to the pretrained checkpoint in the evaluation config file
Then, set the absolute path to the pretrained checkpoint in the evaluation config file
in [eval_configs/minigpt4_eval.yaml](eval_configs/minigpt4_eval.yaml#L10) at Line 8 for Vicuna version or [eval_configs/minigpt4_llama2_eval.yaml](eval_configs/minigpt4_llama2_eval.yaml#L10) for LLama2 version.
@ -137,7 +137,7 @@ and convert it to a conversation format to further align MiniGPT-4.
To download and prepare our second stage dataset, please check our
[second stage dataset preparation instruction](dataset/README_2_STAGE.md).
To launch the second stage alignment,
first specify the path to the checkpoint file trained in stage 1 in
first specify the absolute path to the checkpoint file trained in stage 1 in
[train_configs/minigpt4_stage1_pretrain.yaml](train_configs/minigpt4_stage2_finetune.yaml).
You can also specify the output path there.
Then, run the following command. In our experiments, we use 1 A100.

4
dataset/README_1_STAGE.md
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@ -84,11 +84,11 @@ The final dataset structure
## Set up the dataset configuration files
Then, set up the LAION dataset loading path in
Then, set up the absolute LAION dataset loading path in
[here](../minigpt4/configs/datasets/laion/defaults.yaml#L5) at Line 5 as
${MINIGPT4_DATASET}/laion/laion_dataset/{00000..10488}.tar
and the Conceptual Captoin and SBU datasets loading path in
and the absolute Conceptual Captoin and SBU datasets loading path in
[here](../minigpt4/configs/datasets/cc_sbu/defaults.yaml#L5) at Line 5 as
${MINIGPT4_DATASET}/cc_sbu/cc_sbu_dataset/{00000..01255}.tar

2
dataset/README_2_STAGE.md
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@ -14,6 +14,6 @@ cc_sbu_align
```
Put the folder to any path you want.
Then, set up the dataset path in the dataset config file
Then, set up the absolute dataset path in the dataset config file
[here](../minigpt4/configs/datasets/cc_sbu/align.yaml#L5) at Line 5.