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cs-552-2026-aaty
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Model Details
Model Provider
cs-552-2026-aaty
Model Tree
Qwen/Qwen3-1.7B
Input Modalities
Output Modalities
Supported Functionality
Qwen/Qwen3-1.7Bconfig.json,
generation_config.json, and a tokenizer chat_templateThe 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:
text
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):
text
Q: Which of the following is a noble gas?A) OxygenB) ArgonC) NitrogenD) HydrogenA: ...reasoning... \boxed{B}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 %}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))cs-552-2026-aaty/sft_mixture, the chat-formatted mixture built from
public QA, knowledge, instruction, and math datasets.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.
Q: Which of the following is a noble gas?A) OxygenB) ArgonC) NitrogenD) HydrogenA: ...reasoning... \boxed{B}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))