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

README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+model_name: general_knowledge_model
+tags:
+  - general-knowledge
+  - multiple-choice
+  - reasoning
+  - rejection-sampling
+  - rft
+  - lora
+  - cs-552
+---
+
+# Model Card for `general_knowledge_model`
+
+Post-trained version of [`Qwen/Qwen3-1.7B`](https://huggingface.co/Qwen/Qwen3-1.7B)
+for the **General Knowledge** benchmark of EPFL **CS-552 — Modern NLP** (Spring 2026),
+team CentraleSupéchec.
+
+The task is **closed-book multiple-choice QA** (2–20 options). The model reasons
+inside a `<think> ... </think>` block and ends its reply with the answer wrapped in
+`\boxed{LETTER}`, which is parsed for `pass@1` scoring.
+
+## Training
+
+The model is trained with **Rejection Fine-Tuning (RFT)** — STaR-style
+self-distillation — with an **answer-only loss**:
+
+1. Sample `n=8` completions (`T=0.7`) from the base model over a ~4.7k-question
+   pool of **GPQA** and **MMLU-Pro** (excluding Math/CS).
+2. Keep the 722 questions the base fails at `pass@1` but solves under repeated
+   sampling, producing self-generated correct reasoning traces.
+3. Fine-tune a **LoRA** adapter (`r=16`, `α=32`) with the cross-entropy loss
+   **masked to the `\boxed{}` answer span only** — the `<think>` reasoning
+   conditions the forward pass but receives no gradient. This preserves the
+   model's pretrained reasoning while sharpening answer commitment and output
+   formatting.
+
+The chat template (baked into the tokenizer) enforces a strict `\boxed{LETTER}`
+output and a 16,384-token reasoning budget.
+
+## Quick start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "cs-552-2026-centralesupechec/general_knowledge_model"
+tok = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="bfloat16", device_map="cuda")
+
+question = (
+    "Which of the following is the capital of Australia?\n\n"
+    "Choices:\nA. Sydney\nB. Melbourne\nC. Canberra\nD. Perth"
+)
+inputs = tok.apply_chat_template(
+    [{"role": "user", "content": question}],
+    add_generation_prompt=True, return_tensors="pt",
+).to(model.device)
+
+out = model.generate(inputs, max_new_tokens=16384, temperature=0.6, top_p=0.95, top_k=20)
+print(tok.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+# ... reasoning ... \boxed{C}
+```
+
+For vLLM, mirror the CI: apply the model's chat template, `seed=42`,
+`max_new_tokens=16384`, `temperature=0.6`, `top_p=0.95`, `top_k=20`.
+
+## Generation config
+
+`max_new_tokens: 16384` · `temperature: 0.6` · `top_p: 0.95` · `top_k: 20` ·
+`do_sample: true`. The 16k budget is essential: it removes the format failures
+that occur when reasoning is truncated before the boxed answer.
+
+## Evaluation
+
+`pass@1` on held-out sets disjoint from training (n=4, 16k tokens):
+
+| Set | pass@1 |
+|---|---|
+| 650-question MMLU sweep (26 subjects) | ~0.74 |
+| Internal 100-question expert set | ~0.59 |
+
+See the project report and code for the full comparison against the base model,
+full-trace SFT, and GRPO.
+
+## Framework versions
+
+- Transformers 5.7.0
+- PyTorch 2.10.0+cu128
+- TRL 0.12, PEFT 0.13
+
+## Citation
+
+```bibtex
+@inproceedings{zelikman2022star,
+  title     = {{STaR}: Bootstrapping Reasoning With Reasoning},
+  author    = {Zelikman, Eric and Wu, Yuhuai and Mu, Jesse and Goodman, Noah D.},
+  booktitle = {Advances in Neural Information Processing Systems},
+  year      = {2022}
+}
+```