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Model: cs-552-2026-aaty/group_model
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+library_name: transformers
+language:
+- en
+- it
+- es
+- zh
+- ru
+- hi
+datasets:
+- cs-552-2026-aaty/sft_mixture
+- cs-552-2026-aaty/grpo_mixture
+tags:
+- qwen3
+- group-model
+- reasoning
+- sft
+- grpo
+- cs-552
+- mnlp
+metrics:
+- accuracy
+---
+
+# group_model
+
+Group model for team **AATY**, CS-552 MNLP (EPFL). It is `Qwen/Qwen3-1.7B`
+post-trained with supervised fine-tuning followed by GRPO, and is the
+whole-team submission evaluated on all four domains: math, general knowledge,
+safety, and multilinguality. Its leaderboard rank is the 4-domain average.
+
+## Model details
+
+- **Base model:** `Qwen/Qwen3-1.7B`
+- **Post-training:** SFT (LoRA adapter, merged back into the base weights),
+  then GRPO seeded from the SFT checkpoint with reward functions for the math
+  and reasoning objectives
+- **Domains:** math (free-form, pass@8) and general knowledge, safety,
+  multilinguality (multiple-choice, pass@1)
+- **Format:** vLLM-loadable safetensors with `config.json`,
+  `generation_config.json`, and a tokenizer `chat_template`
+
+## Output contract
+
+The model writes its reasoning and then wraps the final answer in `\boxed{...}`.
+The training mix covers both question styles, because the group model is scored
+on both:
+
+Free-form:
+
+```
+Q: What is the smallest prime greater than 100?
+A: ...reasoning... \boxed{101}
+```
+
+Multiple-choice (the boxed content is the option letter, with 2 to 20 options):
+
+```
+Q: Which of the following is a noble gas?
+A) Oxygen
+B) Argon
+C) Nitrogen
+D) Hydrogen
+A: ...reasoning... \boxed{B}
+```
+
+## Thinking mode
+
+This model runs in **thinking mode**: it emits a `<think>...</think>` reasoning
+block before the final `\boxed{...}` answer. Thinking is forced on inside the
+chat template, because the evaluation passes only
+`tokenizer.apply_chat_template(messages, add_generation_prompt=True)` with no
+`enable_thinking` argument, so the template default is the only signal honored.
+
+The relevant line in `chat_template.jinja`:
+
+```jinja
+{%- set enable_thinking = true %}
+```
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tok = AutoTokenizer.from_pretrained("cs-552-2026-aaty/group_model")
+model = AutoModelForCausalLM.from_pretrained("cs-552-2026-aaty/group_model")
+
+messages = [{"role": "user", "content": "What is the capital of Australia?"}]
+prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tok(prompt, return_tensors="pt").to(model.device)
+out = model.generate(**inputs, max_new_tokens=512)
+print(tok.decode(out[0], skip_special_tokens=True))
+```
+
+## Training data
+
+- **SFT:** `cs-552-2026-aaty/sft_mixture`, the chat-formatted mixture built from
+  public QA, knowledge, instruction, and math datasets.
+- **GRPO:** `cs-552-2026-aaty/grpo_mixture`, prompts with verifiable answers
+  used for reward-driven optimization.
+
+See the team data pipeline in `code/data/` for the exact sources and filters.