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
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https://huggingface.co/lancehar/last_linear_seed5/resolve/main/README.md
- Command line
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hf download hf://lancehar/last_linear_seed5/README.md
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curl -L -o README.md https://huggingface.co/lancehar/last_linear_seed5/resolve/main/README.md
727 Bytes
| 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 | |
| ```python | |
| 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. | |