Nanbeige4.1-3B Blind Spots Dataset

Model Tested

Nanbeige/Nanbeige4.1-3B
A 3B parameter bilingual (Chinese/English) reasoning model by Nanbeige LLM Lab.

How I Loaded the Model

Run on Google Colab with T4 GPU (free tier).

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

tokenizer = AutoTokenizer.from_pretrained(
    "Nanbeige/Nanbeige4.1-3B", use_fast=False, trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
    "Nanbeige/Nanbeige4.1-3B",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

What Errors Did I Find?

The model made mistakes on:

• Decimal comparisons (e.g., confusing 9.11 vs 9.9)
• Character counting (counting letters in words)
• String reversal tasks
• Day-of-week arithmetic
• Trick questions requiring common sense

What Dataset Would Fix These Errors?

A fine-tuning dataset should include:

1. Math reasoning: arithmetic, decimal comparisons, word problems
2. String manipulation: reversal, counting characters, anagrams
3. Logical riddles: common sense and trick questions
4. Temporal reasoning: date/day calculations

How Would You Assemble Such a Dataset?

• Use existing benchmarks: GSM8K, BIG-Bench Hard, HellaSwag
• Generate synthetic examples using GPT-4 or Claude
• Scrape puzzle websites and logic problem books
• Use human annotators to write corner-case examples

How Big a Dataset Would You Need?

For these targeted error types, approximately 5,000–20,000 high-quality examples with chain-of-thought reasoning explanations would likely be sufficient, based on published research on small model fine-tuning.