---
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.