Model Stock: All we need is just a few fine-tuned models
Paper • 2403.19522 • Published • 14
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("appvoid/arco-exp-19")
model = AutoModelForCausalLM.from_pretrained("appvoid/arco-exp-19")Quick Links
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the Model Stock merge method using appvoid/text-arco as a base.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
models:
- model: appvoid/arco
- model: appvoid/arco-2-reasoning-20k
- model: appvoid/arco-2
merge_method: model_stock
base_model: appvoid/text-arco
normalize: false
int8_mask: true
dtype: float16
- Downloads last month
- 3
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="appvoid/arco-exp-19")