File size: 1,186 Bytes
02a940e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | ---
base_model: GSAI-ML/LLaDA-8B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- adapter-transformers
- lora
- peft
- text-generation
- llada
---
# llada-instruct-void
LoRA adapter for
[`GSAI-ML/LLaDA-8B-Instruct`](https://huggingface.co/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
```python
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
|