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--- |
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library_name: transformers |
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license: cc-by-4.0 |
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language: |
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- en |
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- fr |
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- de |
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- it |
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- pt |
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- es |
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pipeline_tag: text-generation |
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--- |
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# helium-1-preview-2b |
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<!-- Provide a quick summary of what the model is/does. --> |
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## Model Details |
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### Model Description |
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<!-- Provide a longer summary of what this model is. --> |
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Helium-1 preview is a lightweight language model with 2B parameters, targeting edge and mobile devices. |
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It supports the following languages: English, French, German, Italian, Portuguese, Spanish. |
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⚠️ Helium-1 Preview is a base model, which was not fine-tuned to follow instructions or human preferences. |
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For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods. |
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- **Developed by:** Kyutai |
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- **Model type:** Large Language Model |
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- **Language(s) (NLP):** English, French, German, Italian, Portuguese, Spanish |
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- **License:** CC-BY 4.0 |
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<!-- ### Model Sources [optional] |
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Provide the basic links for the model. |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] --> |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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The intended use of the Helium model is research and development of natural language processing systems, including but not limited to language generation and understanding. |
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The model can be used in English, French, German, Italian, Portuguese and Spanish. |
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For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods. |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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The model should not be used in other languages than the ones on which it was trained. |
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The model is not intended to be used for any malicious or illegal activities of any kind. |
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The model was not fine-tuned to follow instructions, and thus should not be used as such. |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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Helium-1 preview is a base language model, which was not aligned to human preferences. |
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As such, the model can generate incorrect, biased, harmful or generally unhelpful content. |
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Thus, the model should not be used for downstream applications without further alignment, evaluations and mitigations of risks. |
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<!-- Thus, it should not be used without further evaluations of risks and mitigations. --> |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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```python |
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import torch |
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from transformers import pipeline |
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model_id = "kyutai/helium-1-preview-2b" |
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pipe = pipeline( |
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"text-generation", |
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model=model_id, |
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torch_dtype=torch.bfloat16, |
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device_map="auto" |
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) |
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text = pipe("Hello, today is a great day to") |
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``` |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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Helium-1 preview was trained on a mix of data including: Wikipedia, Stack Exchange, open-access scientific articles (from peS2o) and Common Crawl. |
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<!--#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] --> |
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<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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The model was evaluated on MMLU, TriviaQA, NaturalQuestions, ARC Easy & Challenge, Open Book QA, Common Sense QA, |
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Physical Interaction QA, Social Interaction QA, HellaSwag, WinoGrande, Multilingual Knowledge QA, FLORES 200. |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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We report accuracy on MMLU, ARC, OBQA, CSQA, PIQA, SIQA, HellaSwag, WinoGrande. |
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We report exact match on TriviaQA, NQ and MKQA. |
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We report BLEU on FLORES. |
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#### English Results |
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| Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) | |
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|--------------|:------:|:------:|:------:|:------:|:------:| |
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| MMLU | 51.2 | 50.4 | 53.1 | 56.6 | 61.0 | |
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| NQ | 17.3 | 15.1 | 17.7 | 22.0 | 13.1 | |
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| TQA | 47.9 | 45.4 | 49.9 | 53.6 | 35.9 | |
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| ARC E | 80.9 | 81.8 | 81.1 | 84.6 | 89.7 | |
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| ARC C | 62.7 | 64.7 | 66.0 | 69.0 | 77.2 | |
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| OBQA | 63.8 | 61.4 | 64.6 | 68.4 | 73.8 | |
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| CSQA | 65.6 | 59.0 | 64.4 | 65.4 | 72.4 | |
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| PIQA | 77.4 | 77.7 | 79.8 | 78.9 | 76.0 | |
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| SIQA | 64.4 | 57.5 | 61.9 | 63.8 | 68.7 | |
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| HS | 69.7 | 73.2 | 74.7 | 76.9 | 67.5 | |
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| WG | 66.5 | 65.6 | 71.2 | 72.0 | 64.8 | |
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| Average | 60.7 | 59.3 | 62.2 | 64.7 | 63.6 | |
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#### Multilingual Results |
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| Language | Benchmark | Helium-1 Preview | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) | |
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|-----|--------------|:------:|:------:|:------:|:------:|:------:| |
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| German | MMLU | 45.6 | 35.3 | 45.0 | 47.5 | 49.5 | |
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| | ARC C | 56.7 | 38.4 | 54.7 | 58.3 | 60.2 | |
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| | HS | 53.5 | 33.9 | 53.4 | 53.7 | 42.8 | |
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| | MKQA | 16.1 | 7.1 | 18.9 | 20.2 | 10.4 | |
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| | FLORES | 33.9 | 12.2 | 30.7 | 28.2 | 20.8 | |
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| Spanish | MMLU | 46.5 | 38.9 | 46.2 | 49.6 | 52.8 | |
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| | ARC C | 58.3 | 43.2 | 58.8 | 60.0 | 68.1 | |
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| | HS | 58.6 | 40.8 | 60.5 | 61.1 | 51.4 | |
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| | MKQA | 16.0 | 7.9 | 18.5 | 20.6 | 10.6 | |
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| | FLORES | 25.7 | 15.0 | 25.7 | 23.7 | 20.4 | |
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| French | MMLU | 46.0 | 37.7 | 45.7 | 48.8 | 51.9 | |
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| | ARC C | 57.9 | 40.6 | 57.5 | 60.1 | 67.4 | |
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| | HS | 59.0 | 41.1 | 60.4 | 59.6 | 51.2 | |
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| | MKQA | 16.8 | 8.4 | 18.4 | 19.6 | 9.7 | |
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| | FLORES | 44.3 | 20.0 | 43.3 | 39.3 | 31.2 | |
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| Italian | MMLU | 46.1 | 36.3 | 45.6 | 48.8 | 50.5 | |
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| | ARC C | 57.4 | 39.1 | 53.9 | 60.1 | 64.6 | |
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| | HS | 55.2 | 37.7 | 56.2 | 56.8 | 46.8 | |
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| | MKQA | 15.3 | 6.3 | 18.0 | 19.0 | 9.9 | |
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| | FLORES | 25.8 | 10.4 | 25.2 | 23.8 | 16.4 | |
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| Portuguese | MMLU | 46.2 | 37.7 | 45.6 | 49.2 | 53.0 | |
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| | ARC C | 56.8 | 40.6 | 57.0 | 62.1 | 66.6 | |
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| | HS | 57.3 | 41.0 | 58.7 | 59.1 | 50.9 | |
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| | MKQA | 14.7 | 6.6 | 16.9 | 19.1 | 9.2 | |
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| | FLORES | 43.0 | 20.0 | 43.6 | 40.5 | 33.0 | |
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| | Average | 42.1 | 27.8 | 42.3 | 43.6 | 40.0 |
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## Technical Specifications |
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### Model Architecture and Objective |
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| Hyperparameter | Value | |
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|--------------|:------:| |
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| Layers | 24 | |
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| Heads | 20 | |
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| Model dimension | 2560 | |
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| MLP dimension | 7040 | |
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| Context size | 4096 | |
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| Theta RoPE | 100,000 | |
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#### Hardware |
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The model was trained on 128 NVIDIA H100 Tensor Core GPUs. |
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#### Software |
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The model was trained using Jax. |
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## Citation |
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Blog post: https://kyutai.org/2025/01/13/helium.html |
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