Instructions to use akpon900/dream-instruct-void with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use akpon900/dream-instruct-void with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Dream-org/Dream-v0-Instruct-7B") model = PeftModel.from_pretrained(base_model, "akpon900/dream-instruct-void") - Adapters
How to use akpon900/dream-instruct-void with Adapters:
from adapters import AutoAdapterModel model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("akpon900/dream-instruct-void", set_active=True) - Notebooks
- Google Colab
- Kaggle
dream-instruct-void
LoRA adapter for
Dream-org/Dream-v0-Instruct-7B.
This repository contains adapter weights only; load it together with the base
model.
LoRA Config
- Rank:
32 - Alpha:
64 - Dropout:
0.05 - Target modules:
gate_proj,q_proj,k_proj,up_proj,v_proj,down_proj
Usage
import torch
from peft import PeftModel
from transformers import AutoModel, AutoTokenizer
base_model_id = "Dream-org/Dream-v0-Instruct-7B"
adapter_path = "akpon900/dream-instruct-void"
tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModel.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 0.19.1
- Downloads last month
- 33
Model tree for akpon900/dream-instruct-void
Base model
Dream-org/Dream-v0-Instruct-7B