Add model card

README.md

@@ -1,5 +1,77 @@
 ---
-license: apache-2.0
-base_model:
-- GSAI-ML/LLaDA-8B-Instruct
----
+base_model: GSAI-ML/LLaDA-8B-Instruct
+library_name: transformers
+model_name: akkikiki/LLaDA-8B-Instruct-judge-fs
+tags:
+- generated_from_trainer
+- trl
+- sft
+licence: license
+---
+
+# Model Card for akkikiki/LLaDA-8B-Instruct-judge-fs
+
+This model is a fine-tuned version of [GSAI-ML/LLaDA-8B-Instruct](https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct).
+It has been trained using [TRL](https://github.com/huggingface/trl).
+
+## Quick start
+
+```python
+from transformers import pipeline
+
+prompt = """###Task Description:
+An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, and a score rubric representing a evaluation criteria are given.
+1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general.
+2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric.
+3. The output format should look as follows: "Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)"
+4. Please do not generate any other opening, closing, and explanations.
+
+###The instruction to evaluate:
+{orig_instruction}
+
+###Response to evaluate:
+{orig_response}
+
+###Reference Answer (Score 5):
+{orig_reference_answer}
+
+###Score Rubrics:
+[{orig_criteria}]
+Score 1: {orig_score1_description}
+Score 2: {orig_score2_description}
+Score 3: {orig_score3_description}
+Score 4: {orig_score4_description}
+Score 5: {orig_score5_description}
+
+###Feedback: """
+
+generator = pipeline("text-generation", model="akkikiki/LLaDA-8B-Instruct-judge-fs", device="cuda")
+output = generator([{"role": "user", "content": prompt}], max_new_tokens=128, return_full_text=False)[0]
+print(output["generated_text"])
+```
+
+## Training procedure
+
+This model was trained with SFT on 95% of [prometheus-eval/Feedback-Collection](https://huggingface.co/datasets/prometheus-eval/Feedback-Collection) with 5% held out as a validation set.
+
+### Framework versions
+
+- TRL: 0.23.0
+- Transformers: 4.56.2
+- Pytorch: 2.8.0
+- Datasets: 4.0.0
+- Tokenizers: 0.22.1
+
+## Citations
+
+```bibtex
+@misc{fujinuma2026unlockingpromptinfillingcapability,
+      title={Unlocking Prompt Infilling Capability for Diffusion Language Models}, 
+      author={Yoshinari Fujinuma and Keisuke Sakaguchi},
+      year={2026},
+      eprint={2604.03677},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2604.03677}, 
+}
+```