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
Download tokenizer.json from ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step/resolve/main/tokenizer.json
- Command line
-
hf download hf://ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ZaandaTeika/Qwen2.5-Math-7B-SHARP-Step/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- 6a270da2872eff7c4d65eea7da1c2b79d24e9bab1dde1bfe1efb7d0f0e3a20b1
- Size of remote file:
- 11.4 MB
- SHA256:
- 95c6876df803c949f96a170e0750b4d26e8dcbaf7849fc40a9079617fa8ccadb
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