faisalshahid03/Fatima-fellowship

Blind Spots of Qwen3.5-0.8B-Base

Model Tested

This dataset evaluates the weaknesses of the base language model Qwen3.5-0.8B-Base developed by the Qwen Team.

• Model Link: Qwen/Qwen3.5-0.8B-Base on Hugging Face
• Parameters: ~0.8 billion
• Type: Causal Language Model (Base Model)

Because it is a base model, it has not been fine-tuned for instruction following, providing a clear opportunity to analyze the kinds of mistakes smaller foundation models make when performing reasoning or knowledge tasks.

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Objective

The goal of this dataset is to identify blind spots of a frontier base model by testing the model on diverse prompts and documenting where its predictions differ from expected outputs.

Each entry in this dataset contains:

• input – The prompt given to the model.
• expected_output – The correct or desired response.
• model_output – The response generated by the model.

The dataset includes 10 diverse examples covering reasoning, arithmetic, logical inference, and factual knowledge.

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How the Model Was Loaded

The model was evaluated using the Transformers library from Hugging Face in a Google Colab GPU environment.

Installation

!pip install transformers accelerate torch

Loading the Model

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "Qwen/Qwen3.5-0.8B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

def generate(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(
        **inputs,
        max_new_tokens=120,
        temperature=0.7
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

Prompts were then passed to the generate() function to collect the model outputs.

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Observed Blind Spots

During experimentation, several recurring failure modes were observed:

1. Arithmetic Reasoning Errors: The model frequently struggles with basic arithmetic problems involving unit conversion or multi-step calculations.
2. Logical Inference Mistakes: When presented with simple logical statements, the model sometimes draws incorrect conclusions.
3. Hallucinated Knowledge: The model occasionally produces confident but incorrect factual statements when the prompt contains misleading or ambiguous information.
4. Instruction Following Issues: Despite instructions such as "answer with one word," the model sometimes generates longer explanations.
5. Multi-Step Reasoning Limitations: Problems requiring multiple reasoning steps often lead to incorrect answers or incomplete reasoning chains.

These blind spots are expected in smaller base models that have not been specifically fine-tuned for reasoning or instruction-following tasks.

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Suggested Fine-Tuning Dataset

To address these weaknesses, the model should be fine-tuned on datasets emphasizing reasoning, correctness, and instruction following.

Recommended datasets include:

• Arithmetic reasoning datasets (e.g., GSM8K)
• Logical reasoning datasets (e.g., StrategyQA)
• Factual verification datasets (e.g., TruthfulQA)
• Instruction-following datasets (e.g., OpenAssistant conversations)

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Dataset Collection Strategy

Such datasets could be assembled through:

1. Public reasoning benchmarks
2. Synthetic data generated by stronger language models
3. Human-annotated reasoning tasks
4. Fact-checking corpora from curated knowledge bases

Combining synthetic and human-verified data would help maintain both scale and accuracy.

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Estimated Dataset Size

Task Type	Estimated Samples
Arithmetic reasoning	~50k
Logical reasoning	~30k
Instruction following	~100k
Fact verification	~50k

Total estimated size: 200k–300k training examples.

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Conclusion

This dataset demonstrates several important blind spots in the Qwen3.5-0.8B-Base model, particularly in reasoning and instruction-following tasks. These limitations highlight the importance of targeted fine-tuning and dataset curation when adapting base models for real-world applications.