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Model: cs-552-2026-catma/general_knowledge_model
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model: Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- qwen3
+- sft
+- dpo
+- lora
+- general-knowledge
+- multiple-choice
+- cs-552
+datasets:
+- cais/mmlu
+- TIGER-Lab/MMLU-Pro
+- allenai/ai2_arc
+- allenai/openbookqa
+- allenai/sciq
+- tau/commonsense_qa
+- allenai/quartz
+metrics:
+- accuracy
+---
+
+# General Knowledge Model
+
+This model is the General Knowledge individual-model submission for the CS-552 Modern NLP course project. It is a merged post-trained checkpoint based on [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B), developed by Tuan Dang Nguyen for closed-book multiple-choice general knowledge evaluation.
+
+The uploaded checkpoint corresponds to the final Stage 5 merge-aware DPO model:
+
+```text
+sft_dpo_stage5_error_contrastive_mergeaware_v1_r16_lr8e8_beta003_eval100_500_merged
+```
+
+## Task And Output Format
+
+The model receives a multiple-choice question and should answer with exactly one option letter inside a LaTeX boxed expression:
+
+```text
+\boxed{C}
+```
+
+The evaluation pipeline extracts the letter inside `\boxed{...}`. Any surrounding reasoning is ignored for scoring, but the intended behavior is a concise boxed final answer.
+
+## Training Summary
+
+The training campaign used LoRA-based post-training on top of `Qwen/Qwen3-1.7B`.
+
+Main stages:
+
+- Supervised fine-tuning on mixed general-knowledge multiple-choice data.
+- Hard-source and CI-style refinements, including MMLU-Pro and variable option-count examples.
+- Plus Quartz v1 SFT, which first reached the best hidden-CI score.
+- Conservative Stage 2 SFT refinement from the Plus Quartz anchor.
+- Stage 5 merge-aware DPO using the Stage 2 model's own wrong boxed answers plus protection pairs.
+
+The final Stage 5 model is selected because it is the strongest merged local checkpoint. The strongest hidden-CI score was first reached by the Plus Quartz SFT anchor, and the later Stage 2/DPO submissions tied that hidden score.
+
+## Evaluation
+
+Local evaluation used the course ten-example public General Knowledge validation snapshot in both prompt modes plus a 290-example diagnostic set built from public multiple-choice sources.
+
+| Model | Role | Local diagnostic | Public 10-example validation | Extraction | Hidden CI |
+| --- | --- | ---: | ---: | ---: | ---: |
+| `sft_plus_quartz_v1_r128_7200_merged` | First hidden-CI anchor | 247/290 | 7/10 in both prompt modes | 100% | **0.4900** |
+| `sft_stage2_plus_quartz_v1_r32_lr5e7_800_merged` | Best retained SFT refinement | 248/290 | 7/10 in both prompt modes | 100% | 0.4900 tie |
+| `sft_dpo_stage2_plus_quartz_v1_from_800_mistake_only_r16_lr2e7_beta005_200_merged` | Early DPO refinement | 248/290 | 7/10 in both prompt modes | 100% | 0.4900 tie |
+| `sft_dpo_stage5_error_contrastive_mergeaware_v1_r16_lr8e8_beta003_eval100_500_merged` | Uploaded final model | **249/290** | 7/10 in both prompt modes | 100% | 0.4900 tie |
+
+Interpretation: DPO improved the retained merged local diagnostic result and made checkpoint selection more robust, but it did not improve beyond the best hidden-CI SFT score of `0.4900`.
+
+## Usage Notes
+
+This checkpoint is a fully merged model, not a standalone LoRA adapter. It can be loaded with standard `transformers` text-generation tooling.
+
+For best compatibility with the course evaluator:
+
+- Ask closed-book multiple-choice questions.
+- Include clear answer options.
+- Require the model to finish with `\boxed{LETTER}`.
+- Score only the extracted boxed letter.
+
+Example prompt:
+
+```text
+Answer the following multiple-choice question. Return only the final answer in the form \boxed{LETTER}.
+
+Question: Which planet is known as the Red Planet?
+A) Venus
+B) Mars
+C) Jupiter
+D) Mercury
+```
+
+Expected style:
+
+```text
+\boxed{B}
+```
+
+## Limitations
+
+This model is specialized for English closed-book multiple-choice general knowledge. It is not a general chat assistant and should not be used as a reliable factual oracle outside the benchmark setting. Local diagnostics were useful for model selection but did not perfectly predict hidden-CI changes; hidden-CI accuracy remained tied at `0.4900` for the final refinements.