transformers/docs/source/en/model_doc/helium.md

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# Helium

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## Overview

Helium was proposed in [Announcing Helium-1 Preview](https://kyutai.org/2025/01/13/helium.html) 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

<!-- This section describes the evaluation protocols and provides the results. -->

#### Testing Data

<!-- This should link to a Dataset Card if possible. -->

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

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

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](https://huggingface.co/lmz)

  
## Usage tips

`Helium` can be found on the [Huggingface Hub](https://huggingface.co/models?other=helium)

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

```python
>>> 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