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* sampling : one sequence per sampling context ggml-ci * speculative : add tree-based sampling support ggml-ci * speculative : reuse the n_parallel CLI param * speculative : refactor sampling * examples : fix build after sampling refactoring ggml-ci * batched : fix n_seq_id * sampling : fix malloc ggml-ci * swift : fix build ggml-ci * swift : try to fix build ggml-ci * prompts : add assistant.txt * common : add llama_batch_add() and llama_batch_clear() helpers * speculative : minor refactor ggml-ci * minor : comments + rename ggml-ci * speculative : fix off-by-one for n_drafted * speculative : fix the n_drafted fix + p constants
Examples for input embedding directly
Requirement
build libembdinput.so
run the following comman in main dir (../../).
make
LLaVA example (llava.py)
- Obtian LLaVA model (following https://github.com/haotian-liu/LLaVA/ , use https://huggingface.co/liuhaotian/LLaVA-13b-delta-v1-1/).
- Convert it to ggml format.
llava_projection.pthis 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)
- Check the path of LLaVA model and
llava_projection.pthinllava.py.
PandaGPT example (panda_gpt.py)
- 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. Theadapter_config.jsonis
{
"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"]
}
- Papare the
vicunav0 model. - Obtain the ImageBind model.
- Clone the PandaGPT source.
git clone https://github.com/yxuansu/PandaGPT
- Install the requirement of PandaGPT.
- Check the path of PandaGPT source, ImageBind model, lora model and vicuna model in panda_gpt.py.
MiniGPT-4 example (minigpt4.py)
- Obtain MiniGPT-4 model from https://github.com/Vision-CAIR/MiniGPT-4/ and put it in
embd-input. - Clone the MiniGPT-4 source.
git clone https://github.com/Vision-CAIR/MiniGPT-4/
- Install the requirement of PandaGPT.
- Papare the
vicunav0 model. - Check the path of MiniGPT-4 source, MiniGPT-4 model and vicuna model in
minigpt4.py.