metadata

language:
  - en
  - hi
  - bn
  - mr
  - te
  - ta
  - kn
  - ml
  - gu
  - as
  - pa
license: unknown
tags:
  - Krutrim
  - language-model
widget:
  - text: Category-wise evaluation results
    output:
      url: images/cumulative_score_category.png
  - text: Language-wise evaluation results
    output:
      url: images/cumulative_score_langauge.png

Krutrim-2

Model Overview

Krutrim-2 is a 12B parameter language model developed by the OLA Krutrim team. It is built on the Mistral-NeMo 12B architecture and trained across various domains, including web data, code, math, Indic languages, Indian context data, synthetic data, and books. Following pretraining, the model was finetuned for instruction following on diverse data covering a wide range of tasks, including knowledge recall, math, reasoning, coding, safety, and creative writing.

After fine-tuning, the model underwent Direct Preference Optimization (DPO) to enhance alignment across multiple aspects. DPO was applied to improve response helpfulness, safety, and compliance, making the model more robust against harmful prompts, reducing biases, and improving factual consistency.

Key Features

12B parameter dense transformer model leading to better generalization compared to Krutrim-1 7B;
Supports context up to 128K tokens making it suitable for long multi-turn conversations, long-form generations, document translations and others;
Delivers competitive performance on most English benchmarks and HumanEval coding task;
Natively multilingual delivering best-in-class performance on Indic benchmarks;
Matches or exceeds performance of models much larger (x6) on multilingual Indic generation tasks including creative writing, summarization, and translation;
Stronger Indian cultural context relevance - scored the highest in manual evaluation with multiple models in an anonymised setting;
Delivers top-3 performance on 5 (out of 7) tasks in BharatBench among much larger open source and commercial models.
Available in both pre-trained and instruction-tuned versions

Model Developer

OLA Krutrim Team

Model Dates

Krutrim-2 was trained between Dec 2024 and Jan 2025.

Release History

Model Name	Release Date	Release Note	Reference
Krutrim-2-Base	2024-01-31	Trained with MN12B architecture	Here
Krutrim-2-Instruct	2024-01-31	Finetuned and aligned version of Krutrim-2-Base	Here

Data Freshness

The dataset includes information up to April 2024.

Model Architecture

Layers: 40
Hidden Dimension: 5,120
Head Dimension: 128
Hidden Dimension: 14,336
Activation Function: SiLU
Number of Heads: 32
Number of KV-Heads: 8 (GQA)
Rotary Embeddings: Theta = 1M
Vocabulary Size: 131072 (2^17)
Architecture Type: Transformer Decoder (Auto-regressive Language Model)

Evaluation Results

English/Code/Math Benchmarks

Benchmark	Krutrim-1 7B	MN-12B-Instruct	Krutrim-2-base	Krutrim-2-instruct	llama-3.3-70B	Gemini-1.5 Flash	GPT-4o
Hellaswag (0-shot) - Accuracy	0.74	0.82	0.80	0.83	0.95	0.87 (10-shot)	0.95 (10-shot)
Winogrande (0-shot) - Accuracy	0.67	0.74	0.73	0.77	0.85 (5-shot)	-	0.88 (5-shot)
OpenBookQA (0-shot) - Accuracy	0.45	0.46	0.47	0.49	-	-	-
CommonSenseQA (0-shot) - Accuracy	0.74	0.70	0.66	0.74	-	-	0.85
TruthfulQA (0-shot) - Accuracy	0.49	0.54	0.48	0.59	-	-	0.59
MMLU (5-shot) - Accuracy	0.47	0.68	0.64	0.63	0.82	0.79	0.86
TriviaQA (5-shot) - EM	0.44	0.72	0.66	0.62	-	-	-
NaturalQuestions (5-shot) - EM	0.15	0.28	0.27	0.26	-	-	-
GSM8K (0-shot) - EM	0.07	0.74	0.55	0.71	0.93 (8-shot, CoT)	0.86 (11-shot)	0.89
ARC_Challenge (0-shot) - Accuracy	0.48	0.59	0.55	0.60	0.93 (25-shot)	-	0.50
ARC_Easy (0-shot) - Accuracy	0.73	0.80	0.79	0.82	-	-	-
HumanEval - Pass@10	0.00	0.23	0.59	0.80	0.88	0.74 (0-shot)	0.90
IF_Eval (0-shot) - Accuracy	0.16	0.46	-	0.56	0.92	-	0.84

Indic Benchmarks

Benchmark	Metric	Krutrim-1 7B	MN-12B-Instruct	Krutrim-2 12B	llama-3.1-8B	llama-3.3-70B	Gemini-1.5 Flash	GPT-4o
IndicSentiment (0-shot)	Accuracy	0.65	0.70	0.95	0.05	0.96	0.99	0.98
IndicCOPA (0-shot)	Accuracy	0.51	0.58	0.80	0.48	0.83	0.88	0.91
IndicXParaphrase (0-shot)	Accuracy	0.67	0.74	0.88	0.75	0.87	0.89	TBD
IndicXNLI (0-shot)	Accuracy	0.47	0.54	0.55	0.00	TBD	TBD	TBD
IndicQA (0-shot)	Bert Score	0.90	0.90	0.91	TBD	TBD	TBD	TBD
CrossSumIN (1-shot)	chrF++	0.04	0.17	0.21	0.21	0.26	0.24	TBD
FloresIN Translation xx-en (1-shot)	chrF++	0.54	0.50	0.58	0.54	0.60	0.62	0.63
FloresIN Translation en-xx (1-shot)	chrF++	0.41	0.34	0.48	0.37	0.46	0.47	0.48
IN22 Translation xx-en (0-shot)	chrF++	0.50	0.48	0.57	0.49	0.58	0.55	TBD
IN22 Translation en-xx (0-shot)	chrF++	0.36	0.33	0.45	0.32	0.42	0.44	TBD

BharatBench

The existing Indic benchmarks are not natively in Indian languages, rather, they are translations of existing En benchmarks. They do not sufficiently capture the linguistic nuances of Indian languages and aspects of Indian culture. Towards that Krutrim released BharatBench - a natively Indic benchmark that encompasses the linguistic and cultural diversity of the Indic region, ensuring that the evaluations are relevant and representative of real-world use cases in India.

Benchmark	Metric	Krutrim-1 7B	MN-12B-Instruct	Krutrim-2 12B	llama-3.1-8B-Instruct	llama-3.1-70B-Instruct	Gemma-2-9B-Instruct	Gemma-2-27B-Instruct	GPT-4o
Indian Cultural Context (0-shot)	Bert Score	0.86	0.56	0.88	0.87	0.88	0.87	0.87	0.89
Grammar Correction (5-shot)	Bert Score	0.96	0.94	0.98	0.95	0.98	0.96	0.96	0.97
Multi Turn (0-shot)	Bert Score	0.88	0.87	0.91	0.88	0.90	0.89	0.89	0.92
Multi Turn Comprehension (0-shot)	Bert Score	0.90	0.89	0.92	0.92	0.93	0.91	0.91	0.94
Multi Turn Translation (0-shot)	Bert Score	0.85	0.87	0.92	0.89	0.91	0.90	0.91	0.92
Text Classification (5-shot)	Accuracy	0.61	0.71	0.76	0.72	0.88	0.82	0.86	0.89
Named Entity Recognition (5-shot)	Accuracy	0.31	0.51	0.53	0.55	0.61	0.61	0.65	0.65

Qualitative Results

Below are the results from manual evaluation of prompt-response pairs across languages and task categories. Scores are between 1-5 (higher the better). Model names were anonymised during the evaluation.

Usage

To use the model, you can load it with AutoModelForCausalLM as follows:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "krutrim-ai-labs/Krutrim-2-instruct"

# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained(model_id)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Add custom chat template
prompt_dict = [{"role":'system','content':"You are an AI assistant."},{"role":'user','content':"Who are you?"}]
prompt = tokenizer.apply_chat_template(prompt_dict, add_generation_prompt=True, tokenize=False)
inputs = tokenizer(prompt, return_tensors='pt')
inputs.pop("token_type_ids", None)

# Generate response
outputs = model.generate(
    **inputs,
    max_length=4096,
    temperature=0.3,
    top_k=50,
    top_p=0.9,
    repetition_penalty=1.2,
    num_return_sequences=1,
    do_sample=True,
    eos_token_id=2,
)

response_list = [tokenizer.decode(output).split(prompt)[1] for output in outputs]

Note: The provided chat template, which is the default chat template, helps generate the best response by structuring conversations optimally for the model. We recommend using temperature=0.3 for the best performance

Limitations

The model was trained on a dataset that includes content from the internet, which may contain toxic language, biases, and unsafe content. As a result, the model may:

Amplify biases present in the training data
Generate toxic responses, especially when prompted with toxic inputs
Provide inaccurate, incomplete, or redundant answers
Generate responses in languages inconsistent with the prompt

License

TBD

Ethical Considerations

The model may produce biased or offensive outputs based on its training data.
Users should apply human oversight when using the model for decision-making in sensitive areas.
While safeguards have been implemented, the model may still generate socially undesirable text in certain contexts.

Contact

TBD