"Accuracy" metric for LLM model(s)

@mohitmundhragithub

Which "accuracy" metric(s) should we use for LLM benchmarking?

MMLU: the first item people choose. it covers several field with multiple-choice questions.
- @mohitmundhragithub please point where/how MLPerf Client use this
- mostly running a full-set of this is gonna take several hours on Android devices
- hence, other choice is TinyMMLU
- TinyMMLU: 100 questions only
Other tasks such as summarization, Q/A

In the MLPerf client group, they have used the MMLU score for accuracy computation.
They have a separate set of scripts, which processes the output logs generated during the inference to compute the mmlu score. it's an offline process.
You are right... it takes several hours to run the whole suite. In that sense TinyMMLU makes more sense.

The scripts that are used in the client group are shared here: https://github.com/mlcommons/mlperf_client_dev/tree/main/tools/mmlu

But, sadly, there is no public repository containing all the mmlu computation scripts.

One big problem I see is that MMLU score doesn't measure the tasks used for performance. As, its measuring the accuracy of only the 1st token. Basically, we are not measuring the whole pipeline.
Even in the client group, they are looking for other options.

In the inference group, the metric adopted is RogueN. May be we can explore that as well.

@Mostelk

Let's check if we can run tinyBenchmarks (100 row validation sets maybe) with llama 3.2 1B and 3B and get acceptable numbers first.

@Mostelk @AhmedTElthakeb and @mohitmundhragithub

Let's check if we can run tinyBenchmarks (100 row validation sets maybe) with llama 3.2 1B and 3B and get acceptable numbers first.

@Mostelk @AhmedTElthakeb and @mohitmundhragithub

I think we discussed llama-3.1-8B and llama-3.2-3B in the meeting... right?

Let's check if we can run tinyBenchmarks (100 row validation sets maybe) with llama 3.2 1B and 3B and get acceptable numbers first.
@Mostelk @AhmedTElthakeb and @mohitmundhragithub

I think we discussed llama-3.1-8B and llama-3.2-3B in the meeting... right?

right, my bad.

baseline numbers

llama 3.2 3B
with lm_eval --model hf --model_args pretrained=meta-llama/Llama-3.2-3B --tasks tinyBenchmarks

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.5055	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.3170	±	N/A
		strict-match	5	exact_match	↑	0.3170	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.7742	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.5910	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4042	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.6875	±	N/A

llama 3.2 3B Instruct
with lm_eval --model hf --model_args pretrained=meta-llama/Llama-3.2-3B-Instruct --tasks tinyBenchmarks

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.5670	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.6233	±	N/A
		strict-match	5	exact_match	↑	0.5994	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.7494	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.6244	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4795	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.6298	±	N/A

llama 3.1 8B
with lm_eval --model hf --model_args pretrained=meta-llama/Llama-3.1-8B --tasks tinyBenchmarks

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.5733	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.5037	±	N/A
		strict-match	5	exact_match	↑	0.5037	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.8344	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.6335	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4776	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.7507	±	N/A

llama 3.1 8B Instruct
with lm_eval --model hf --model_args pretrained=meta-llama/Llama-3.1-8B-Instruct --tasks tinyBenchmarks

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.6533	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.7610	±	N/A
		strict-match	5	exact_match	↑	0.7289	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.8054	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.6413	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.5409	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.7263	±	N/A

gemma 3 1b

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.4571	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.2647	±	N/A
		strict-match	5	exact_match	↑	0.2514	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.5312	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.4935	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.3836	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.6210	±	N/A

gemma 3 1b qat q4_0 unquantized

(https://huggingface.co/google/gemma-3-4b-it-qat-q4_0-unquantized)

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.4125	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.1290	±	N/A
		strict-match	5	exact_match	↑	0.1290	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.5563	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.4550	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.3881	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.6292	±	N/A

gemma 3 4b

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.6002	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.7760	±	N/A
		strict-match	5	exact_match	↑	0.7760	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.7045	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.6005	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4619	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.6961	±	N/A

gemma 3 4b qat q4_0 unquantized

Tasks	Version	Filter	n-shot	Metric		Value		Stderr
tinyBenchmarks	N/A
- tinyArc	0	none	25	acc_norm	↑	0.6137	±	N/A
- tinyGSM8k	0	flexible-extract	5	exact_match	↑	0.7574	±	N/A
		strict-match	5	exact_match	↑	0.7574	±	N/A
- tinyHellaswag	0	none	10	acc_norm	↑	0.6894	±	N/A
- tinyMMLU	0	none	0	acc_norm	↑	0.6036	±	N/A
- tinyTruthfulQA	0	none	0	acc	↑	0.4426	±	N/A
- tinyWinogrande	0	none	5	acc_norm	↑	0.7046	±	N/A

Let us try to quantize these and report accuracy llama 3.1 8B Instruct, llama 3.2 3B Instruct

@Mostelk

After some exploration, the use cases we are trying to enable (say summarization, context generation etc.,) are not properly captured by the datasets used in TinyBenchmark. Most of the datasets have single-token outputs (except for GSM8k and AlpacaEval) and none of them capture summarization task in particular.

@Mostelk should we still go with above task of quantization and accuracy collection for these datasets ?

We are also looking into Open-Orca dataset which captures all the tasks we discussed on last meet.

We are also looking whether ROGUE-n score could be a viable accuracy metric for Open-Orca dataset.

@Aswinoss

After some exploration, the use cases we are trying to enable (say summarization, context generation etc.,) are not properly captured by the datasets used in TinyBenchmark. Most of the datasets have single-token outputs (except for GSM8k and AlpacaEval) and none of them capture summarization task in particular.

@Mostelk should we still go with above task of quantization and accuracy collection for these datasets ?

We are also looking into Open-Orca dataset which captures all the tasks we discussed on last meet.

We are also looking whether ROGUE-n score could be a viable accuracy metric for Open-Orca dataset.

@Aswinoss I am open to other benchmarks. However, I don't agree that benchmarks used by the TinyBenchmarks are single-token outputs. They are not.

@freedomtan

@freedomtan From the tinyBenchmarks page, the ones I have marked as Single-Token had descriptions similar to single-token outputs (PFB). We have not run it, to be exactly sure. But it looks like, the mentioned benchmarks won't have stream of text or content stream as output (except for GSM8k and AlpacaEval).

For example, TinyTruthfulQA had input/output description in this way, where we ask a question and the LLM responds with label / label list, where the true option has 1 and rest all 0s.

One more thing,
we are also okay with any of these accuracy benchmarks. But based on the discussion we had in the last engineering meeting, we thought that these datasets may not be representative enough.

@Aswinoss

@freedomtan From the tinyBenchmarks page, the ones I have marked as Single-Token had descriptions similar to single-token outputs (PFB). We have not run it, to be exactly sure. But it looks like, the mentioned benchmarks won't have stream of text or content stream as output (except for GSM8k and AlpacaEval).

For example, TinyTruthfulQA had input/output description in this way, where we ask a question and the LLM responds with label / label list, where the true option has 1 and rest all 0s.

One more thing, we are also okay with any of these accuracy benchmarks. But based on the discussion we had in the last engineering meeting, we thought that these datasets may not be representative enough.

@Aswinoss it's quite easy to check outputs of those benchmarks. For example, we can

feed the inputs and get outputs and check them, or
add --output_path and --log_samples as described at https://github.com/EleutherAI/lm-evaluation-harness?tab=readme-ov-file#saving--caching-results then we can check those outputs

The example you show is kinda internal representation of the dataset.

How about quantized model from Meta folks, we know they are available on Huggingface too

Well, they are not in Huggingface safetensor format, that is, we can evaluate them with lm_ev --model hf --model_args pretrained=.... It seems to me that we have to use ExecuTorch (see https://github.com/pytorch/executorch/blob/main/examples/models/llama/README.md)

@mohitmundhragithub

@mohitmundhragithub @Aswinoss and @Mostelk OpenOrca is a dataset, not a benchmark.

@mohitmundhragithub

@mohitmundhragithub @freedomtan I agree let us consider other metrics as well, but I prefer one metric that is publicly published by Meta, for example I see here they use SQUAD for reading comprehension,
Also we need to decide whether to use general model or instruction tuned model, looks reading comprehension results below are using the original general model
https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct

Given we used SQUAD before in MobileBert, I don;t think should be hard here as well?
Does Meta report numbers for summarization task?

Reading comprehension

| SQuAD | 1 | em | 76.4 | 77.0 | 85.6 | 81.8 | 89.3
-- | -- | -- | -- | -- | -- | -- | -- | --
QuAC (F1) | 1 | f1 | 44.4 | 44.9 | 51.1 | 51.1 | 53.6
BoolQ | 0 | acc_char | 75.7 | 75.0 | 79.0 | 79.4 | 80.0
DROP (F1) | 3 | f1 | 58.4 | 59.5 | 79.7 | 79.6 | 84.8

@mohitmundhragithub

@mohitmundhragithub @freedomtan please check these 3.1 8B quantized models, with their evaluation, looks using lm-eval, so may be we adopt some tiny lm-eval ( appears using static weight quantization, but dynamic activation quantization)
https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct
https://huggingface.co/RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w8a8
https://huggingface.co/RedHatAI/Meta-Llama-3.1-8B-Instruct-quantized.w4a16

100 for tinyMMLU (use it as is) + 30 ifeval

Per our discussion today, for TinyMMLU we only focus on zero-shot, since it is consistent

TinyIFEval-29:
This first version is created by selecting every 18th sample from the original 541 IFEval entries input_data.jsonl resulting in a deterministic set of 30 evenly spaced examples. Then removing one entry (key:1203) whose response exceeds 1K output tokens, so we end up with 29 set.

Entry ID	IFEval Key	#Tokens	#Words
1	1000	351	268
2	1108	227	14
3	127	636	510
4	1375	434	260
5	1498	362	244
6	1592	127	92
7	1658	353	210
8	1776	374	276
9	1880	259	172
10	1964	119	92
11	2078	116	81
12	2195	362	266
13	227	6	4
14	2355	38	27
15	2416	661	356
16	2497	187	149
17	2596	145	61
18	2704	47	34
19	2798	209	176
20	2871	276	207
21	2957	37	30
22	3079	336	265
23	3195	20	8
24	3281	567	498
25	3362	147	114
26	343	551	432
27	3534	378	308
28	3623	100	5
29	3718	54	45

Results (Full IFEval vs TinyIFEval-29)

Model	IFEval	Eval Setting	Prompt‑Level	Instruction‑Level	Avg
8B fp	Full	Strict	0.729	0.805	0.779
		Loose	0.757	0.829	⬛
	Tiny-29	Strict	0.759	0.827	0.806
		Loose	0.793	0.846	⬛
1B fp	Full	Strict	0.428	0.560	0.508
		Loose	0.461	0.589	⬛
	Tiny-29	Strict	0.345	0.596	0.489
		Loose	0.379	0.635	⬛

@mohitmundhragithub

TinyIFEval-29: This first version is created by selecting every 18th sample from the original 541 IFEval entries input_data.jsonl resulting in a deterministic set of 30 evenly spaced examples. Then removing one entry (key:1203) whose response exceeds 1K output tokens, so we end up with 29 set.

Entry ID IFEval Key #Tokens #Words
1 1000 351 268
2 1108 227 14
3 127 636 510
4 1375 434 260
5 1498 362 244
6 1592 127 92
7 1658 353 210
8 1776 374 276
9 1880 259 172
10 1964 119 92
11 2078 116 81
12 2195 362 266
13 227 6 4
14 2355 38 27
15 2416 661 356
16 2497 187 149
17 2596 145 61
18 2704 47 34
19 2798 209 176
20 2871 276 207
21 2957 37 30
22 3079 336 265
23 3195 20 8
24 3281 567 498
25 3362 147 114
26 343 551 432
27 3534 378 308
28 3623 100 5
29 3718 54 45
Results (Full IFEval vs TinyIFEval-29)

Model IFEval Eval Setting Prompt‑Level Instruction‑Level Avg
8B fp Full Strict 0.729 0.805 0.779
Loose 0.757 0.829 ⬛
Tiny-29 Strict 0.759 0.827 0.806
Loose 0.793 0.846 ⬛
1B fp Full Strict 0.428 0.560 0.508
Loose 0.461 0.589 ⬛
Tiny-29 Strict 0.345 0.596 0.489
Loose 0.379 0.635 ⬛

@mohitmundhragithub @freedomtan please check if this list is representative enough for TinyEval, @farook-edev please check for POC accuracy pipeline

@farook-edev

@farook-edev check UI how to output both TinyEVAL and TINYMMLU accuracy out of 100 each. Per @freedomtan we also need check loadgen dataset loader implementation how to do two accuracy tests, two set of logs

@Mostelk

@Mostelk check if the 29 prompts have good coverage of the 25 "types" listed in the paper.

@farook-edev

@farook-edev please help test these 29 prompts from ifeval.

@freedomtan

@freedomtan @Mostelk I checked the TinyIFEval (29) prompts, they seem to run correctly, but coverage isn't 100%, here's a breakdown image I made by reading and marking each instruction in the dataset on a page from the IFEval paper:

And here's a checklist i made that's in markdown:

TinyIFEval Instruction Checklist Summary

🟩 Keywords Group

Status	Instruction	Meaning	Occurrences
✅	Include Keywords	Must include specific keywords	2
✅	Keyword Frequency	Each keyword must appear N times	3
✅	Forbidden Words	Must avoid certain forbidden words	3
✅	Letter Frequency	Specific letter must appear N times	4

🟩 Language Group

Status	Instruction	Meaning	Occurrences
✅	Response Language	Entire answer must be in a specific language	4

🟩 Length Constraints

Status	Instruction	Meaning	Occurrences
❌	Number Paragraphs	Must have exactly N paragraphs	0
✅	Number Words	Must have at least / around / at most N words	4
✅	Number Sentences	Must have at least / around / at most N sentences	3
✅	Number Paragraphs + First Word in i-th Paragraph	Must have N paragraphs; each paragraph starts with specific word	1

🟩 Detectable Content

Status	Instruction	Meaning	Occurrences
❌	Postscript	Must end with a postscript marker	0
❌	Number Placeholder	Must contain at least N placeholders like `[address]`	0

🟩 Detectable Format

Status	Instruction	Meaning	Occurrences
✅	Number Bullets	Must contain exactly N bullet points	2
✅	Title	Must contain a title wrapped in `<< >>`	2
✅	Choose From	Must pick one from given options	1
✅	Minimum Number Highlighted Section	Must highlight N sections with markdown (e.g. `highlighted`)	1
❌	Multiple Sections	Must have N sections marked by `{section_splitter}`	0
❌	JSON Format	Must output in JSON format	0

🟩 Combination Instructions

Status	Instruction	Meaning	Occurrences
✅	Repeat Prompt	Must repeat the request before answering	2
✅	Two Responses	Must give two responses separated by `******`	3

🟩 Change Cases

Status	Instruction	Meaning	Occurrences
✅	All Uppercase	Entire response must be in uppercase	1
✅	All Lowercase	Entire response must be in lowercase	1
✅	Frequency of All-Capital Words	Some words must be all caps	1

🟩 Start With / End With

Status	Instruction	Meaning	Occurrences
✅	End Checker	Must finish with a specific phrase	1
✅	Quotation	Entire answer wrapped in double quotes	4

🟩 Punctuation

Status	Instruction	Meaning	Occurrences
✅	No Commas	Response must not contain any commas	7

❌ Never Occurred

Instruction	Meaning
Number Paragraphs	Must have exactly N paragraphs
Number Placeholder	Must contain at least N placeholders like `[address]`
Postscript	Must end with a postscript marker
Multiple Sections	Must have N sections marked by `{section_splitter}`
JSON Format	Must output in JSON format

For Tiny-IFEval, we came up with another 33 prompts, we will call it Tiny IFEval-33 that covers all categories

tiny33_ifeval.json

For Tiny-IFEval, we came up with another 33 prompts, we will call it Tiny IFEval-33 that covers all categories

tiny33_ifeval.json

Distribution can be found here https://docs.google.com/presentation/d/1bcwl-Wng75KMrdQtVmWVmLmOwC4CzLM-qvNB_KBXM7g/edit?slide=id.g3a01f8da923_0_0#slide=id.g3a01f8da923_0_0

PyTorch bf16 with my script https://colab.research.google.com/drive/1iJvkHTxi2VZ_bYrOQ6_9QKVtH2FV7Pmt?usp=sharing

zero-shot 5-shot 5-shot w input_formatted
1b instruct 49% 46% 65%
3b instruct 55% 53% 84%
8b instruct 69% 60% 83%

There is an issue with this colab, with HF API, dataset does not include the questions, just the examples for fewshot2,
if you download dataset directly from here https://huggingface.co/datasets/tinyBenchmarks/tinyMMLU/tree/main/all
and replace this

dataset = load_dataset("tinyBenchmarks/tinyMMLU", split="all")
with
dataset = load_dataset("parquet", data_files={"dev": "/path/dev-00000-of-00001.parquet", "test":"/path/test-00000-of-00001.parquet"}, split="all")

The result for few-shot2 is different, float, 2K input, 1k output

	Local Downloaded		HF API
Models	Zero Shot TinyMMLU	Few Shot 2 Tiny MMLU	Zero Shot TinyMMLU	Few Shot 2 Tiny MMLU
Llama-3.2-1B-Instruct (Prefill Len 2048)	48%	43.16%	48%	71.28%
Llama-3.1-8B-Instruct (Prefill Len 2048)	68%	63.16%%	67%	88.30%

@Mostelk

@Mostelk will add 1B, 3B, 8B, float IFEVal and TInyMMLU
@mohitmundhragithub @freedomtan @Mostelk start report quantized numbers, finalize by 03/31

Float PyTorch Accuracies for Llama Instruct Models, with Prefill Max Token length of 2048 Tokens, and Decode max output of 1024 Tokens:

	Zero Shot TinyMMLU	Few Shot-2 Tiny MMLU	Strict Prompt	Strict Instruction	Loose Prompt	Loose Instruction	Avg IFEval
Llama-3.2-1B-Instruct (Prefill Len 2048)	48%	43.16%	0.273	0.547	0.303	0.563	0.422
Llama-3.2-3B-Instruct (Prefill Len 2048)	55%	61.05%	0.545	0.75	0.576	0.781	0.663
Llama-3.1-8B-Instruct (Prefill Len 2048)	68%	63.16%%	0.545	0.703	0.636	0.797	0.67

@freedomtan

let's put this (https://colab.research.google.com/drive/1iJvkHTxi2VZ_bYrOQ6_9QKVtH2FV7Pmt?usp=sharing) to mobile-open. @freedomtan to do it.

@mohitmundhragithub

@mohitmundhragithub @freedomtan @Mostelk please report accuracy numbers.

Hi,

On our devices we are seeing about

llama-3.1-8B -- ~ 60%
llama-3.1-8B (Instruct) -- ~ 55%

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TinyIFEval Instruction Checklist Summary

🟩 Keywords Group

🟩 Language Group

🟩 Length Constraints

🟩 Detectable Content

🟩 Detectable Format

🟩 Combination Instructions

🟩 Change Cases

🟩 Start With / End With

🟩 Punctuation

❌ Never Occurred

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TinyIFEval Instruction Checklist Summary

🟩 Keywords Group

🟩 Language Group

🟩 Length Constraints

🟩 Detectable Content

🟩 Detectable Format

🟩 Combination Instructions

🟩 Change Cases

🟩 Start With / End With

🟩 Punctuation

❌ Never Occurred

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