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Model: AI-ModelScope/helium-1-2b-science Source: Original Platform
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README.md
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README.md
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---
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library_name: transformers
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license: cc-by-sa-4.0
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language:
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- bg
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- cs
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- da
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- de
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- el
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- en
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- es
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- et
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- fi
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- fr
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- ga
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- hr
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- hu
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- it
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- lt
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- lv
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- mt
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- nl
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- pl
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- pt
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- ro
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- sk
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- sl
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- sv
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pipeline_tag: text-generation
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---
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# Helium-1-2b
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<img src="https://huggingface.co/kyutai/moshi-1-2b/resolve/main/helium_sticker.png" width="400">
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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 is a lightweight language model with 2B parameters, targeting edge and mobile devices.
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It supports the 24 official languages of the European Union.
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⚠️ Helium-1 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):** Bulgarian, Czech, Danish, German, Greek, English, Spanish, Estonian, Finnish, French, Irish, Croatian, Hungarian, Italian, Lithuanian, Latvian, Maltese, Dutch, Polish, Portuguese, Romanian, Slovak, Slovenian, Swedish.
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- **License:** CC-BY-SA 4.0
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- **Terms of use:** As a model distilled from Gemma 2, Helium 1 is subject to the Gemma Terms of Use found at ai.google.dev/gemma/terms
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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 Bulgarian, Czech, Danish, German, Greek, English, Spanish, Estonian, Finnish, French, Irish, Croatian, Hungarian, Italian, Lithuanian, Latvian, Maltese, Dutch, Polish, Portuguese, Romanian, Slovak, Slovenian, Swedish.
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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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Helium-1 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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## 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-1-2b"
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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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Helium-1 was trained on data from Common Crawl, which was preprocessed with the dactory library.
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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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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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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 | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
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|--------------|:------:|:------:|:------:|:------:|:------:|
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| | | | | | |
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| MMLU | 52.0 | 50.4 | 53.1 | 56.6 | 61.0 |
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| NQ | 16.5 | 15.1 | 17.7 | 22.0 | 13.1 |
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| TQA | 46.5 | 45.4 | 49.9 | 53.6 | 35.9 |
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| ARC E | 82.2 | 81.8 | 81.1 | 84.6 | 89.7 |
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| ARC C | 64.6 | 64.7 | 66.0 | 69.0 | 77.2 |
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| OBQA | 65.4 | 61.4 | 64.6 | 68.4 | 73.8 |
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| CSQA | 63.6 | 59.0 | 64.4 | 65.4 | 72.4 |
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| PIQA | 78.5 | 77.7 | 79.8 | 78.9 | 76.0 |
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| SIQA | 62.3 | 57.5 | 61.9 | 63.8 | 68.7 |
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| HS | 73.6 | 73.2 | 74.7 | 76.9 | 67.5 |
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| WG | 66.9 | 65.6 | 71.2 | 72.0 | 64.8 |
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| Average | 61.1 | 59.3 | 62.2 | 64.7 | 63.6 |
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#### Multilingual Results
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| Benchmark | Helium-1 | Gemma-2 (2.6B) | Llama-3.2 (3B) |
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|--------------|:------:|:------:|:------:|
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| ARC E | 71.1 | 65.8 | 68.2 |
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| ARC C | 54.8 | 51.1 | 52.6 |
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| MMLU | 44.8 | 43.1 | 45.3 |
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| HS | 51.9 | 49.9 | 48.4 |
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| FLORES | 20.6 | 21.9 | 19.8 |
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| MKQA | 16.5 | 17.2 | 19.7 |
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| Average | 43.3 | 41.5 | 42.3 |
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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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| Model dimension | 2048 |
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| MLP dimension | 8192 |
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| Layers | 28 |
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| Heads | 16 |
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| RoPE theta | 20,000 |
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| Context size | 4096 |
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| Max learning rate | 2.4e-04 |
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| Total steps | 500,000 |
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| Weight decay | 0.1 |
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| Gradient clip | 1.0 |
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#### Hardware
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The model was trained on 64 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: [Helium 1: a modular and multilingual LLM](https://kyutai.org/2025/04/30/helium.html).
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