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+⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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+*This model was contributed to Hugging Face Transformers on 2025-01-13.*
+
+# Helium
+
+<div class="flex flex-wrap space-x-1">
+<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
+<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
+</div>
+
+## 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
+
+
+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(model.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