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
| { | |
| "additional_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<R>", | |
| "<S>", | |
| "<X>", | |
| "<mask>", | |
| "<sep>", | |
| "<extra_0>" | |
| ], | |
| "eos_token": { | |
| "content": "<|im_end|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| }, | |
| "pad_token": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false | |
| } | |
| } | |