Instructions to use akpon900/llada-instruct-void with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akpon900/llada-instruct-void with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("GSAI-ML/LLaDA-8B-Instruct") model = PeftModel.from_pretrained(base_model, "akpon900/llada-instruct-void") - Adapters
How to use akpon900/llada-instruct-void with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("akpon900/llada-instruct-void", set_active=True) - Notebooks
- Google Colab
- Kaggle
llada-instruct-void
LoRA adapter for
GSAI-ML/LLaDA-8B-Instruct.
This repository contains adapter weights only; load it together with the base
model.
LoRA Config
- Rank:
32 - Alpha:
64 - Dropout:
0.05 - Target modules:
q_proj,up_proj,ff_out,v_proj,ff_proj,k_proj
Usage
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_id = "GSAI-ML/LLaDA-8B-Instruct"
adapter_path = "akpon900/llada-instruct-void"
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True,
)
model = PeftModel.from_pretrained(base_model, adapter_path)
model.eval()
Notes
- Intended for research and experimentation.
- No benchmark results are included in this release.
Framework Versions
- PEFT 0.17.1
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Model tree for akpon900/llada-instruct-void
Base model
GSAI-ML/LLaDA-8B-Instruct