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cs-552-2026-ma-que
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Model Details
Model Provider
cs-552-2026-ma-que
Model Tree
Qwen/Qwen3-1.7B-Base
Input Modalities
Output Modalities
Supported Functionality
General knowledge expert for the EPFL CS-552 (Modern NLP, Spring 2026) group project "Building a robust small language model for edge devices" (Group 11, Ma Que). This is an individual model; the team also maintains a merged group model (https://huggingface.co/cs-552-2026-ma-que/group_model).
Values below are exactly those in generation_config.json:
┌────────────────┬────────────────────────────────────────────────────────────────┐ │ param │ value │ ├────────────────┼────────────────────────────────────────────────────────────────┤ │ do_sample │ true │ │ temperature │ 0.6 │ │ top_k │ 20 │ │ top_p │ 0.95 │ │ max_new_tokens │ not set in generation_config.json; use 16384 for final grading │ └────────────────┴────────────────────────────────────────────────────────────────┘
Usage:
text
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "cs-552-2026-ma-que/general_knowledge_model" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, torch_dtype="auto", device_map="auto", ) messages = [{"role": "user", "content": "Which planet is known as the Red Planet?\n" "A) Venus\nB) Mars\nC) Jupiter\nD) Mercury"}] text = tok.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) out = model.generate( **tok(text, return_tensors="pt").to(model.device), max_new_tokens=16384, ) print(tok.decode(out[0], skip_special_tokens=True)) # -> ... \boxed{B}The model is also vLLM-loadable for batched evaluation.
The thinking model was trained with sft_thinking.py using the dataset TangYeqing/maque-data, as configured in cfgs_thinking.yml.
The script loads the dataset's train split and performs a local train_test_split(test_size=0.005, seed=42). Each row contains conversational messages; if a row has no system message, the training script prepends:
You are a helpful assistant that provides step-by-step solutions to math problems.
The assistant turns already contain literal ... reasoning traces, which are preserved during SFT. Training uses assistant-only loss, so the loss is computed on the assistant response, including the thinking trace and final boxed answer.
Research artifact for the CS-552 general knowledge benchmark; produces boxed multiple-choice answers only. It is not an authoritative factual system and may encode outdated, incomplete, or incorrect facts, so outputs should be verified before any real-world or safety-critical use.
Built on Qwen3-1.7B (Qwen Team, 2025), arXiv:2505.09388 — https://arxiv.org/abs/2505.09388
from transformers import AutoModelForCausalLM, AutoTokenizer repo = "cs-552-2026-ma-que/general_knowledge_model" tok = AutoTokenizer.from_pretrained(repo) model = AutoModelForCausalLM.from_pretrained( repo, torch_dtype="auto", device_map="auto", ) messages = [{"role": "user", "content": "Which planet is known as the Red Planet?\n" "A) Venus\nB) Mars\nC) Jupiter\nD) Mercury"}] text = tok.apply_chat_template( messages, tokenize=False, add_generation_prompt=True, ) out = model.generate( **tok(text, return_tensors="pt").to(model.device), max_new_tokens=16384, ) print(tok.decode(out[0], skip_special_tokens=True)) # -> ... \boxed{B}