Instructions to use DesaiJayM/Bharat-24B-Math-Code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use DesaiJayM/Bharat-24B-Math-Code with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Small-3.2-24B-Instruct-2506") model = PeftModel.from_pretrained(base_model, "DesaiJayM/Bharat-24B-Math-Code") - Notebooks
- Google Colab
- Kaggle
File size: 1,252 Bytes
4cf8d9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"model": "Bharat 24B by Leo Labs",
"evaluation_date": "2026-10-12",
"completed_run_cases": 222,
"reported_scope": "Math and code results only",
"reported_unique_cases": 192,
"math": {
"dataset": "GSM8K fixed128-case subset",
"correct_numeric_final_answer": 123,
"cases": 128,
"accuracy_percent": 96.09375,
"checker": "MathVerify0.9.0 numeric/symbolic final-answer match",
"secondary_analysis": true,
"registered_strict_format_correct": 111,
"registered_strict_format_accuracy_percent": 86.71875,
"token_limit_cases": 6
},
"code": {
"dataset": "Original HumanEval fixed64-case subset",
"correct": 51,
"cases": 64,
"pass_at_1_percent": 79.6875,
"attempts_per_case": 1,
"greedy_decoding": true,
"token_limit_cases": 0
},
"inference": "Original NF4 backbone plus saved checkpoint625 adapter; native prompts and greedy decoding",
"math_code_evaluation_wall_time_cuts": 0,
"training_overlap_normalized_exact_questions": 0,
"scope_note": "Fixed subsets, not full benchmark or leaderboard results. Math format-tolerant scoring was selected after an earlier format artifact and declared before the remaining100 responses were generated."
} |