llama.cpp/examples/embd-input

Examples for input embedding directly

Requirement

build libembdinput.so run the following comman in main dir (../../).

make

LLaVA example (llava.py)

1. Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
2. Convert it to ggml format.
3. llava_projection.pth is pytorch_model-00003-of-00003.bin.

import torch

bin_path = "../LLaVA-13b-delta-v1-1/pytorch_model-00003-of-00003.bin"
pth_path = "./examples/embd-input/llava_projection.pth"

dic = torch.load(bin_path)
used_key = ["model.mm_projector.weight","model.mm_projector.bias"]
torch.save({k: dic[k] for k in used_key}, pth_path)

4. Check the path of LLaVA model and llava_projection.pth in llava.py.

PandaGPT example (panda_gpt.py)

1. Obtian PandaGPT lora model from https://github.com/yxuansu/PandaGPT. Rename the file to adapter_model.bin. Use convert-lora-to-ggml.py to convert it to ggml format. The adapter_config.json is

{
  "peft_type": "LORA",
  "fan_in_fan_out": false,
  "bias": null,
  "modules_to_save": null,
  "r": 32,
  "lora_alpha": 32,
  "lora_dropout": 0.1,
  "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"]
}

2. Papare the vicuna v0 model.
3. Obtain the ImageBind model.
4. Clone the PandaGPT source.

git clone https://github.com/yxuansu/PandaGPT

5. Install the requirement of PandaGPT.
6. Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.

MiniGPT-4 example (minigpt4.py)

1. Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in embd-input.
2. Clone the MiniGPT-4 source.

git clone https://github.com/Vision-CAIR/MiniGPT-4/

3. Install the requirement of PandaGPT.
4. Papare the vicuna v0 model.
5. Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in minigpt4.py.