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Helium

Overview

Helium was proposed in Announcing Helium-1 Preview by the Kyutai Team.

Helium-1 preview is a lightweight language model with 2B parameters, targeting edge and mobile devices. It supports the following languages: English, French, German, Italian, Portuguese, Spanish.

Developed by: Kyutai
Model type: Large Language Model
Language(s) (NLP): English, French, German, Italian, Portuguese, Spanish
License: CC-BY 4.0

Evaluation

Testing Data

The model was evaluated on MMLU, TriviaQA, NaturalQuestions, ARC Easy & Challenge, Open Book QA, Common Sense QA, Physical Interaction QA, Social Interaction QA, HellaSwag, WinoGrande, Multilingual Knowledge QA, FLORES 200.

Metrics

We report accuracy on MMLU, ARC, OBQA, CSQA, PIQA, SIQA, HellaSwag, WinoGrande. We report exact match on TriviaQA, NQ and MKQA. We report BLEU on FLORES.

English Results

Benchmark	Helium-1 Preview	HF SmolLM2 (1.7B)	Gemma-2 (2.6B)	Llama-3.2 (3B)	Qwen2.5 (1.5B)

MMLU	51.2	50.4	53.1	56.6	61.0
NQ	17.3	15.1	17.7	22.0	13.1
TQA	47.9	45.4	49.9	53.6	35.9
ARC E	80.9	81.8	81.1	84.6	89.7
ARC C	62.7	64.7	66.0	69.0	77.2
OBQA	63.8	61.4	64.6	68.4	73.8
CSQA	65.6	59.0	64.4	65.4	72.4
PIQA	77.4	77.7	79.8	78.9	76.0
SIQA	64.4	57.5	61.9	63.8	68.7
HS	69.7	73.2	74.7	76.9	67.5
WG	66.5	65.6	71.2	72.0	64.8

Average	60.7	59.3	62.2	64.7	63.6

Multilingual Results

Language	Benchmark	Helium-1 Preview	HF SmolLM2 (1.7B)	Gemma-2 (2.6B)	Llama-3.2 (3B)	Qwen2.5 (1.5B)

German	MMLU	45.6	35.3	45.0	47.5	49.5
	ARC C	56.7	38.4	54.7	58.3	60.2
	HS	53.5	33.9	53.4	53.7	42.8
	MKQA	16.1	7.1	18.9	20.2	10.4

Spanish	MMLU	46.5	38.9	46.2	49.6	52.8
	ARC C	58.3	43.2	58.8	60.0	68.1
	HS	58.6	40.8	60.5	61.1	51.4
	MKQA	16.0	7.9	18.5	20.6	10.6

Technical Specifications

Model Architecture and Objective

Hyperparameter	Value
Layers	24
Heads	20
Model dimension	2560
MLP dimension	7040
Context size	4096
Theta RoPE	100,000

Tips:

This model was contributed by Laurent Mazare

Usage tips

Helium can be found on the Huggingface Hub

In the following, we demonstrate how to use helium-1-preview for the inference.

>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto

>>> model = AutoModelForCausalLM.from_pretrained("kyutai/helium-1-preview-2b", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("kyutai/helium-1-preview-2b")

>>> prompt = "Give me a short introduction to large language model."

>>> model_inputs = tokenizer(prompt, return_tensors="pt").to(device)

>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)

>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]

>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]

HeliumConfig

autodoc HeliumConfig

HeliumModel

autodoc HeliumModel - forward

HeliumForCausalLM

autodoc HeliumForCausalLM - forward

HeliumForSequenceClassification

autodoc HeliumForSequenceClassification - forward

HeliumForTokenClassification

autodoc HeliumForTokenClassification - forward

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