Instructions to use lancehar/last_linear_seed5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lancehar/last_linear_seed5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="lancehar/last_linear_seed5", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForSequenceClassification model = AutoModelForSequenceClassification.from_pretrained("lancehar/last_linear_seed5", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from lancehar/last_linear_seed5: direct link, hf CLI and curl.
- Browser
- Download file 727 Bytes
-
https://huggingface.co/lancehar/last_linear_seed5/resolve/main/README.md
- Command line
-
hf download hf://lancehar/last_linear_seed5/README.md
-
curl -L -o README.md https://huggingface.co/lancehar/last_linear_seed5/resolve/main/README.md
727 Bytes
metadata
library_name: transformers
tags:
- reward-model
- gpt-neox
- transformers
lancehar/last_linear_seed5
This repository contains the last_linear_seed5 head-diverse reward model checkpoint from
mc-dropout-vs-reward-ensemble.
Loading
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model_name = "lancehar/last_linear_seed5"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForSequenceClassification.from_pretrained(
model_name,
trust_remote_code=True,
)
These checkpoints use a custom GPT-NeoX reward-model class, so trust_remote_code=True
is required unless you register the class locally from this repository.