Text Classification
Transformers
Safetensors
English
Chinese
qwen2
text-generation
reward model
Qwen-PRM
custom_code
text-embeddings-inference
Instructions to use prithivMLmods/PRM-Math-7B-Reasoner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/PRM-Math-7B-Reasoner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("prithivMLmods/PRM-Math-7B-Reasoner", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "<R>": 151646, | |
| "<S>": 151647, | |
| "<X>": 151648, | |
| "<extra_0>": 151651, | |
| "<mask>": 151649, | |
| "<sep>": 151650, | |
| "<|endoftext|>": 151643, | |
| "<|im_end|>": 151645, | |
| "<|im_start|>": 151644 | |
| } | |