Modelpublic

brettleehari/cricketmind-nemotron-mini

sourceHugging Faceapache-2.0updated 6mo agoView on Hugging Face

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Model Card

CricketMind — Cricket Domain Expert (Nemotron Mini 4B)

A fine-tuned version of nvidia/Nemotron-Mini-4B-Instruct specialized in MCC Laws of Cricket and match situation analysis.

Training

—Method: LoRA (r=16, alpha=32) on bfloat16
—Target modules: qproj, vproj
—Data: ~170 examples — Laws QA + response distillation from Claude
—Hardware: NVIDIA A100 80GB SXM
—Epochs: 3
—Final training loss: 1.65

Evaluation — CricketBench v0.1

LLM-as-judge evaluation (Claude) across 20 questions in 4 categories:

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)

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

Dataset

Training data and evaluation suite: brettleehari/cricketbench-v1

Author

Hariprasad Sudharshan — AI Product Manager

—LinkedIn
—GitHub