Upload README.md

README.md

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+---
+library_name: transformers
+license: cc-by-4.0
+language:
+- en
+- fr
+- de
+- it
+- pt
+- es
+pipeline_tag: text-generation
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+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
+
+<!-- ### Model Sources [optional]
+
+ Provide the basic links for the model.
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]  -->
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+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.
+The model can be used in English, French, German, Italian, Portuguese and Spanish.
+For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods.
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+The model should not be used in other languages than the ones on which it was trained.
+The model is not intended to be used for any malicious or illegal activities of any kind.
+The model was not fine-tuned to follow instructions, and thus should not be used as such.
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+Helium-1 preview is a base language model, which was not aligned to human preferences.
+As such, the model can generate incorrect, biased, harmful or generally unhelpful content.
+Thus, the model should not be used for downstream applications without further alignment, evaluations and mitigations of risks.
+<!-- Thus, it should not be used without further evaluations of risks and mitigations. -->
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+from transformers import pipeline
+
+model_id = "kyutai/helium-1-preview"
+
+pipe = pipeline(
+    "text-generation", 
+    model=model_id, 
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+text = pipe("Hello, today is a great day to")
+```
+
+## Training Details
+
+### Training Data
+
+<!-- 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. -->
+
+Helium-1 preview was trained on a mix of data including: Wikipedia, Stack Exchange, open-access scientific articles (from peS2o) and Common Crawl.
+
+<!--#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] -->
+
+<!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+## 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 |
+| | FLORES | 33.9 | 12.2 | 30.7 | 28.2 | 20.8 |
+| 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 |
+| | FLORES | 25.7 | 15.0 | 25.7 | 23.7 | 20.4 |
+| French | MMLU | 46.0 | 37.7 | 45.7 | 48.8 | 51.9 |
+| | ARC C | 57.9 | 40.6 | 57.5 | 60.1 | 67.4 |
+| | HS | 59.0 | 41.1 | 60.4 | 59.6 | 51.2 |
+| | MKQA | 16.8 | 8.4 | 18.4 | 19.6 | 9.7 |
+| | FLORES | 44.3 | 20.0 | 43.3 | 39.3 | 31.2 |
+| Italian | MMLU | 46.1 | 36.3 | 45.6 | 48.8 | 50.5 |
+| | ARC C | 57.4 | 39.1 | 53.9 | 60.1 | 64.6 |
+| | HS | 55.2 | 37.7 | 56.2 | 56.8 | 46.8 |
+| | MKQA | 15.3 | 6.3 | 18.0 | 19.0 | 9.9 |
+| | FLORES | 25.8 | 10.4 | 25.2 | 23.8 | 16.4 |
+| Portuguese | MMLU | 46.2 | 37.7 | 45.6 | 49.2 | 53.0 |
+| | ARC C | 56.8 | 40.6 | 57.0 | 62.1 | 66.6 |
+| | HS | 57.3 | 41.0 | 58.7 | 59.1 | 50.9 |
+| | MKQA | 14.7 | 6.6 | 16.9 | 19.1 | 9.2 |
+| | FLORES | 43.0 | 20.0 | 43.6 | 40.5 | 33.0 |
+| | | | | | | |
+| | Average | 42.1 | 27.8 | 42.3 | 43.6 | 40.0
+
+## 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 |
+
+#### Hardware
+
+The model was trained on 128 NVIDIA H100 Tensor Core GPUs.
+
+#### Software
+
+The model was trained using Jax.
+
+## Citation
+
+Blog post: https://kyutai.org/2025/01/13/helium-release.html