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
| { | |
| "architectures": ["Qwen2ForSequenceClassification"], | |
| "model_type": "qwen2", | |
| "vocab_size": 151936, | |
| "hidden_size": 2048, | |
| "num_hidden_layers": 36, | |
| "num_attention_heads": 16, | |
| "num_key_value_heads": 2, | |
| "intermediate_size": 11008, | |
| "hidden_act": "silu", | |
| "max_position_embeddings": 32768, | |
| "initializer_range": 0.02, | |
| "rms_norm_eps": 1e-6, | |
| "tie_word_embeddings": true, | |
| "dtype": "bfloat16", | |
| "pad_token_id": 151643, | |
| "bos_token_id": null, | |
| "eos_token_id": 151643, | |
| "use_cache": false, | |
| "transformers_version": "5.0.0" | |
| } |