# Helium

## 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 #### 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](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