Category	CricketMind	Baseline Nemotron	Improvement
Laws Recall (30%)	60.0%	40%	+20pp
Conditional Reasoning (35%)	70.0%	25%	+45pp
Match Situation (25%)	80.0%	30%	+50pp
Edge Case (10%)	50.0%	20%	+30pp
Overall	67.5%	30.2%	+37.3pp

Usage

With Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "brettleehari/cricketmind-nemotron-mini"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

prompt = """### Instruction:
You are CricketMind, an expert in the Laws of Cricket. Cite Law numbers and reason step by step.

### Input:
A batter is struck on the pad outside the line of off stump. They played a shot. Is it out LBW?

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Google Colab (free GPU)

Go to colab.google.com → New notebook
Runtime → Change runtime type → T4 GPU
Paste the code above and run

Dataset

Training data and evaluation suite: brettleehari/cricketbench-v1

Author

Hariprasad Sudharshan — AI Product Manager