Text Classification
Transformers
Safetensors
English
qwen2
reward-model
3b
RLHF
text-embeddings-inference
Instructions to use kanishkez/Reward-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kanishkez/Reward-Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kanishkez/Reward-Model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kanishkez/Reward-Model") model = AutoModelForSequenceClassification.from_pretrained("kanishkez/Reward-Model") - Notebooks
- Google Colab
- Kaggle
| { | |
| "unk_token": "<|end_of_text|>", | |
| "sep_token": "<|end_of_text|>", | |
| "pad_token": "<|end_of_text|>", | |
| "cls_token": "<|begin_of_text|>", | |
| "mask_token": null, | |
| "bos_token": "<|begin_of_text|>", | |
| "eos_token": "<|end_of_text|>", | |
| "additional_special_tokens": ["<|eom_id|>", "<|eot_id|>", "<|python_tag|>"], | |
| "model_max_length": 2048, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "clean_up_tokenization_spaces": true | |
| } |