Maham789/blind-spots-qwen2.5
Blind Spots of Qwen2.5-1.5B Model Tested Model: Qwen/Qwen2.5-1.5B Link: https://huggingface.co/Qwen/Qwen2.5-1.5B Type: Base language model (not finetuned) Parameters: 1.5 Billion How I Loaded the Model I used Google Colab with a free T4 GPU. from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_name = "Qwen/Qwen2.5-1.5B" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(… See the full description on the dataset page: https://huggingface.co/datasets/Maham789/blind-spots-qwen2.5.
Blind Spots of Qwen2.5-1.5B
Model Tested
- Model: Qwen/Qwen2.5-1.5B
- Link: https://huggingface.co/Qwen/Qwen2.5-1.5B
- Type: Base language model (not finetuned)
- Parameters: 1.5 Billion
How I Loaded the Model
I used Google Colab with a free T4 GPU.
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_name = "Qwen/Qwen2.5-1.5B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)Dataset Description
This dataset contains 10 diverse prompts where the model made incorrect or poor predictions. Each row has:
- input: the prompt given to the model
- expected_output: the correct answer
- model_output: what the model actually said
What Fine-Tuning Dataset Would Fix These Errors?
How Would I Assemble Such a Dataset?
- Download existing open datasets from Hugging Face
- Manually write diverse Q&A pairs
- Use crowdsourcing to collect varied examples
How Big a Dataset is Needed?
- For targeted fixes: 5,000–20,000 examples is sufficient
- For broad improvement: 100,000+ examples
- Quality matters more than quantity