Reinforcement Learning
Transformers
Safetensors
olmo2
text-generation
rlvr
rl-zero
chain-of-thought
faithfulness
reward-hacking
cue-injection
unfaithrl
Instructions to use UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024") model = AutoModelForCausalLM.from_pretrained("UnfaithRL/OLMo-2-0425-1B-hint_following_reward-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 508 Bytes
3c26e63 | 1 2 3 4 5 6 7 8 9 | {{ bos_token }}{% for message in messages %}{% if message['role'] == 'system' %}{{ '<|system|>
' + message['content'] + '
' }}{% elif message['role'] == 'user' %}{{ '<|user|>
' + message['content'] + '
' }}{% elif message['role'] == 'assistant' %}{% if not loop.last %}{{ '<|assistant|>
' + message['content'] + eos_token + '
' }}{% else %}{{ '<|assistant|>
' + message['content'] + eos_token }}{% endif %}{% endif %}{% if loop.last and add_generation_prompt %}{{ '<|assistant|>
' }}{% endif %}{% endfor %} |