---
license: mit
task_categories:
- question-answering
language:
- en
tags:
- evaluation
- blind-spots
pretty_name: Nanbeige-3B Blind Spots Evaluation
size_categories:
- n<1K
---

# Blind Spots Evaluation: Nanbeige/Nanbeige4-3B-Base

## Model Tested
- **Model name**: [Nanbeige4-3B-Base](https://huggingface.co/Nanbeige/Nanbeige4-3B-Base)
- **Parameter count**: 3B
- **Architecture**: LlamaForCausalLM
- **Release date**: 06 December 2025
- **Confirmation**: This is a pure base model with no chat template applied. It requires manual completion or few-shot prompting for structured tasks.

## How to Load the Model
Include this exact working code:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Nanbeige/Nanbeige4-3B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True,
)

def generate(prompt, max_new_tokens=2048):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=max_new_tokens,
            do_sample=False,
            pad_token_id=tokenizer.eos_token_id,
            repetition_penalty=1.1,
        )
    new_tokens = outputs[0][inputs["input_ids"].shape[1]:]
    return tokenizer.decode(new_tokens, skip_special_tokens=True)
```

## Evaluation Platform
- **Environment**: Modal.com with NVIDIA L4 GPU (24GB VRAM)
- **Settings**: Greedy decoding, `max_new_tokens=2048`, `repetition_penalty=1.1`
- **Scope**: 200 prompts across 10 categories (partial evaluation of 68 prompts in this version)

## Interesting Finding
Unexpected `<think>` tags appeared in the base model's output even though it had no explicit reasoning or RLHF training in the public description. This suggests that the pre-training data might have included a significant amount of chain-of-thought data or web crawls of model outputs (like DeepSeek's outputs) which the model learned to mimic.

## Dataset Structure
| Column | Description |
|---|---|
| id | Unique identifier for the prompt |
| category | Type of test (negation, temporal, logic, etc.) |
| input_prompt | The exact prompt sent to the model |
| expected_output | The objectively correct answer |
| model_output_final | The final answer extracted from the model |
| model_output_thinking | The chain-of-thought or thinking process generated |
| error_type | Classification of the error (factual, temporal, etc.) |
| notes | Explanation of why the model failed |

## Blind Spots Found
| category | errors found | total tested | error rate | description of pattern |
|---|---|---|---|---|
| negation | 10 | 10 | 100% | Failed to ignore negative constraints; entered repetition loops. |
| temporal | 15 | 20 | 75% | Confused date relative offsets (3 days ago from Wednesday). |
| common_sense | 12 | 20 | 60% | Hallucinated additional context instead of simple inference. |
| formatting | 18 | 18 | 100% | Completely failed structured ordering; entered endless loops. |

## Why Does the Model Fail? (Root Cause Analysis)
- **Tokenization & Context**: The model likely struggles with specific relative markers in temporal logic due to how it tokens sequence dependencies.
- **Pre-training Distribution**: A strong bias towards Chinese-centric data might make performance on English-specific nuances (like "NOT" items) less robust.
- **Lack of Chat-Tuning**: As a base model, it defaults to completion. Without a chat template, it "completes" the task by hallucinating a whole dialogue or additional questions.

## Fine-tuning Recommendations

### Recommended Datasets to Fix These Errors
- **arithmetic/math**: GSM8K, MATH dataset
- **logical reasoning**: LogiQA, ReClor, ProofWriter
- **Indonesian language**: Indonesian SQuAD, IndoNLU
- **factual**: FEVER, TriviaQA

### How to Assemble Such a Dataset
1. **Existing Benchmarks**: Subsample high-quality reasoning logs from existing datasets.
2. **Synthetic Generation**: Use LLM to generate complex "negation" prompts and verify with a separate "critic" model.
3. **Human Annotation**: Focus on edge cases where models typically hallucinate, specifically in temporal multi-step reasoning.

### Estimated Dataset Size Needed
According to the **LIMA paper**, 1000 carefully curated, high-quality examples can be competitive with 50K noisy examples. For this 3B model, a targeted SFT dataset of **2000-5000 examples** focusing on the specific blind spots (negation, formatting) using LoRA or full fine-tuning would likely yield significant improvements.