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-Instruct-code_problems_hint_following-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-Instruct-code_problems_hint_following-1024 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("UnfaithRL/OLMo-2-0425-1B-Instruct-code_problems_hint_following-1024") model = AutoModelForCausalLM.from_pretrained("UnfaithRL/OLMo-2-0425-1B-Instruct-code_problems_hint_following-1024", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 644 Bytes
d979987 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | {
"architectures": [
"Olmo2ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 100257,
"dtype": "float32",
"eos_token_id": 100257,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 8192,
"max_position_embeddings": 4096,
"model_type": "olmo2",
"num_attention_heads": 16,
"num_hidden_layers": 16,
"num_key_value_heads": 16,
"pad_token_id": 100277,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 500000,
"tie_word_embeddings": false,
"transformers_version": "4.56.1",
"use_cache": false,
"vocab_size": 100352
}
|