Text Classification
Transformers
Safetensors
English
qwen2
feature-extraction
prm
process-reward-model
reward model
math
hallucination-detection
custom_code
text-embeddings-inference
Instructions to use ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step", trust_remote_code=True) model = AutoModel.from_pretrained("ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 1,743 Bytes
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"architectures": [
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],
"attention_dropout": 0.0,
"auto_map": {
"AutoConfig": "configuration_qwen2_rm.Qwen2RMConfig",
"AutoModel": "modeling_qwen2_rm.Qwen2ForProcessRewardModel",
"AutoModelForSequenceClassification": "modeling_qwen2_rm.Qwen2ForProcessRewardModel",
"AutoModelForTokenClassification": "modeling_qwen2_rm.Qwen2ForProcessRewardModel"
},
"dtype": "bfloat16",
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"hidden_act": "silu",
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"max_position_embeddings": 4096,
"max_window_layers": 28,
"model_type": "qwen2",
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"num_hidden_layers": 28,
"num_key_value_heads": 4,
"pad_token_id": 151643,
"rms_norm_eps": 1e-06,
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"tie_word_embeddings": false,
"transformers_version": "4.57.6",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 152064,
"torch_dtype": "bfloat16",
"num_labels": 2,
"problem_type": "single_label_classification"
}
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