Add model card for GRACE

This PR adds a comprehensive model card for the GRACE generator model. It includes:
- Relevant metadata (license, library_name, and pipeline_tag).
- Links to the paper [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://huggingface.co/papers/2305.14934) and the official GitHub repository.
- A brief description of the stepwise guided decoding approach.
- The official citation from the paper.

README.md

@@ -0,0 +1,45 @@
+---
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+tags:
+- reasoning
+- chain-of-thought
+- math
+---
+
+# GRACE: Discriminator-Guided Chain-of-Thought Reasoning
+
+This model is part of the work presented in the paper [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://huggingface.co/papers/2305.14934). 
+
+GRACE (Guiding chain-of-thought ReAsoning with a CorrectnEss Discriminator) is a stepwise decoding approach that steers the decoding process towards producing correct reasoning steps. It employs a step-level verifier or discriminator trained with a contrastive loss over correct and incorrect steps, which is used during decoding to score next-step candidates based on their correctness.
+
+## Resources
+- **Paper:** [GRACE: Discriminator-Guided Chain-of-Thought Reasoning](https://huggingface.co/papers/2305.14934)
+- **GitHub Repository:** [https://github.com/mukhal/grace](https://github.com/mukhal/grace)
+- **Authors:** Muhammad Khalifa, Lajanugen Logeswaran, Moontae Lee, Honglak Lee, Lu Wang
+
+## Sample Usage
+The official implementation for running guided decoding using this model can be found in the GitHub repository. Below is an example of how to run the GRACE decoding:
+
+```bash
+WANDB_MODE=disabled python run_grace.py \
+                        --model_name_or_path mkhalifa/flan-t5-large-gsm8k \
+                        --in_file data/gsm8k/dev.jsonl \
+                        --task gsm8k \
+                        --disc_path ckpts/discrim/flan-t5-gsm8k/ \
+                        --beta 0.1 --n_candidate_steps 20 --generation_type step-score \
+                        --step_sampling_method top_p --device2 cuda:0 --top_p .95 --sample_calc true \
+                        --max_steps 6  --max_step_length 60 --step_delimiter '|' --temperature .8  --n_self_consistency 1 --seed 42
+```
+
+## Citation
+If you use this work, please cite the following paper:
+```bibtex
+@article{khalifa2023grace,
+  title={Grace: Discriminator-guided chain-of-thought reasoning},
+  author={Khalifa, Muhammad and Logeswaran, Lajanugen and Lee, Moontae and Lee, Honglak and Wang, Lu},
+  journal={arXiv preprint arXiv:2305.14934},
+  year={2023}
+}
+```