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
| { | |
| "bos_token_id": 151643, | |
| "do_sample": true, | |
| "eos_token_id": [ | |
| 151645, | |
| 151643 | |
| ], | |
| "max_length": 4096, | |
| "pad_token_id": 151643, | |
| "repetition_penalty": 1.05, | |
| "temperature": 0.7, | |
| "top_k": 20, | |
| "top_p": 0.8, | |
| "transformers_version": "4.47.1" | |
| } | |