license, base_model, pipeline_tag, library_name, language, datasets, tags, metrics

license	base_model	pipeline_tag	library_name	language	datasets	tags	metrics
apache-2.0	Qwen/Qwen3-1.7B	text-generation	transformers	en	cs-552-2026-aaty/sft_mixture	qwen3 general-knowledge reasoning sft cs-552 mnlp	accuracy

general_knowledge_model

General-knowledge specialty model for team AATY, CS-552 MNLP (EPFL). It is Qwen/Qwen3-1.7B supervised fine-tuned on a general-knowledge mixture to answer closed-book factual and reasoning questions across the sciences, humanities, and geography.

Model details

• Base model: Qwen/Qwen3-1.7B
• Post-training: supervised fine-tuning (LoRA adapter, merged back into the base weights)
• Domain: general knowledge, multiple-choice, scored at 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{...}. For multiple-choice items the boxed content is the letter of the chosen option, and option counts can range from 2 to 20.

Q: Which planet is closest to the Sun?
A) Venus
B) Mercury
C) Mars
D) Earth
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:

{%- set enable_thinking = true %}

Usage

from transformers import AutoTokenizer, AutoModelForCausalLM

tok = AutoTokenizer.from_pretrained("cs-552-2026-aaty/general_knowledge_model")
model = AutoModelForCausalLM.from_pretrained("cs-552-2026-aaty/general_knowledge_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

Supervised fine-tuning on cs-552-2026-aaty/sft_mixture, the chat-formatted mixture built from public QA and knowledge datasets. See the team data pipeline in code/data/ for the exact sources and filters.