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Model: kyutai/helium-1-2b-stem
Source: Original Platform

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README.md

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+---
+library_name: transformers
+license: cc-by-sa-4.0
+language:
+- bg
+- cs
+- da
+- de
+- el
+- en
+- es
+- et
+- fi
+- fr
+- ga
+- hr
+- hu
+- it
+- lt
+- lv
+- mt
+- nl
+- pl
+- pt
+- ro
+- sk
+- sl
+- sv
+pipeline_tag: text-generation
+---
+
+# Helium-1-2b
+
+<img src="https://huggingface.co/kyutai/moshi-1-2b/resolve/main/helium_sticker.png" width="400">
+
+
+## Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+Helium-1 is a lightweight language model with 2B parameters, targeting edge and mobile devices.
+It supports the 24 official languages of the European Union.
+
+⚠️ Helium-1 is a base model, which was not fine-tuned to follow instructions or human preferences.
+For most downstream use cases, the model should be aligned with supervised fine-tuning, RLHF or related methods.
+
+- **Developed by:** Kyutai
+- **Model type:** Large Language Model
+- **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.
+- **License:** CC-BY-SA 4.0
+- **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
+
+<!-- ### 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 Bulgarian, Czech, Danish, German, Greek, English, Spanish, Estonian, Finnish, French, Irish, Croatian, Hungarian, Italian, Lithuanian, Latvian, Maltese, Dutch, Polish, Portuguese, Romanian, Slovak, Slovenian, Swedish.
+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
+
+Helium-1 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.
+
+## 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-1-2b"
+
+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
+
+Helium-1 was trained on data from Common Crawl, which was preprocessed with the dactory library.
+
+<!--#### 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
+
+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 | HF SmolLM2 (1.7B) | Gemma-2 (2.6B) | Llama-3.2 (3B) | Qwen2.5 (1.5B) |
+|--------------|:------:|:------:|:------:|:------:|:------:|
+| | | | | | |
+| MMLU | 52.0 | 50.4 | 53.1 | 56.6 | 61.0 |
+| NQ   | 16.5 | 15.1 | 17.7 | 22.0 | 13.1 |
+| TQA  | 46.5 | 45.4 | 49.9 | 53.6 | 35.9 |
+| ARC E | 82.2 | 81.8 | 81.1 | 84.6 | 89.7 |
+| ARC C | 64.6 | 64.7 | 66.0 | 69.0 | 77.2 |
+| OBQA | 65.4 | 61.4 | 64.6 | 68.4 | 73.8 |
+| CSQA | 63.6 | 59.0 | 64.4 | 65.4 | 72.4 |
+| PIQA | 78.5 | 77.7 | 79.8 | 78.9 | 76.0 |
+| SIQA | 62.3 | 57.5 | 61.9 | 63.8 | 68.7 |
+| HS | 73.6 | 73.2 | 74.7 | 76.9 | 67.5 |
+| WG | 66.9 | 65.6 | 71.2 | 72.0 | 64.8 |
+| | | | | | |
+| Average | 61.1 | 59.3 | 62.2 | 64.7 | 63.6 |
+
+#### Multilingual Results
+
+| Benchmark | Helium-1 | Gemma-2 (2.6B) | Llama-3.2 (3B) |
+|--------------|:------:|:------:|:------:|
+| | | | | | |
+| ARC E  | 71.1 | 65.8 | 68.2 |
+| ARC C  | 54.8 | 51.1 | 52.6 |
+| MMLU   | 44.8 | 43.1 | 45.3 |
+| HS     | 51.9 | 49.9 | 48.4 |
+| FLORES | 20.6 | 21.9 | 19.8 |
+| MKQA   | 16.5 | 17.2 | 19.7 |
+| | | | | | |
+| Average | 43.3 | 41.5 | 42.3 |
+
+
+## Technical Specifications
+
+### Model Architecture and Objective
+
+| Hyperparameter | Value |
+|--------------|:------:|
+| Model dimension | 2048 |
+| MLP dimension | 8192 |
+| Layers | 28 |
+| Heads  | 16 |
+| RoPE theta | 20,000 |
+| Context size | 4096 |
+| Max learning rate | 2.4e-04 |
+| Total steps | 500,000 |
+| Weight decay | 0.1 |
+| Gradient clip | 1.0 |
+
+#### Hardware
+
+The model was trained on 64 NVIDIA H100 Tensor Core GPUs.
+
+#### Software
+
+The model was trained using Jax.
+
+## Citation
+
+Blog post: [Helium 1: a modular and multilingual LLM](https://kyutai.org/2025/04/30/helium.html).

config.json

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+{
+    "architectures": [
+	"LlamaForCausalLM"
+    ],
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "bos_token_id": 0,
+    "eos_token_id": 1,
+    "hidden_act": "silu",
+    "hidden_size": 2048,
+    "initializer_range": 0.02,
+    "intermediate_size": 8192,
+    "max_position_embeddings": 4096,
+    "mlp_bias": false,
+    "model_type": "llama",
+    "num_attention_heads": 16,
+    "num_hidden_layers": 28,
+    "num_key_value_heads": 8,
+    "pretraining_tp": 1,
+    "rms_norm_eps": 1e-08,
+    "rope_theta": 20000.0,
+    "tie_word_embeddings": false,
+    "torch_dtype": "bfloat16",
+    "transformers_version": "4.45.0.dev0",
+    "use_cache": true,
+    "vocab_size": 64000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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