Text Classification
Transformers
Safetensors
English
qwen2
reward-model
code-generation
rlhf
text-embeddings-inference
Instructions to use Rishubi/CodeRM-NT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Rishubi/CodeRM-NT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Rishubi/CodeRM-NT")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Rishubi/CodeRM-NT") model = AutoModelForSequenceClassification.from_pretrained("Rishubi/CodeRM-NT", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 844 Bytes
e51cd2a | 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 29 30 31 32 33 34 35 36 37 | {
"_name_or_path": "Rishubi/CodeRM-NT",
"architectures": [
"Qwen2ForSequenceClassification"
],
"attention_dropout": 0.0,
"bos_token_id": 151643,
"eos_token_id": 151643,
"hidden_act": "silu",
"hidden_size": 3584,
"id2label": {
"0": "LABEL_0"
},
"initializer_range": 0.02,
"intermediate_size": 18944,
"label2id": {
"LABEL_0": 0
},
"max_position_embeddings": 32768,
"max_window_layers": 28,
"model_type": "qwen2",
"num_attention_heads": 28,
"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "4.46.1",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 152064
}
|