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* Add the helium model. * Add a missing helium. * And add another missing helium. * Use float for the rmsnorm mul. * Add the Helium tokenizer converter. * Add the pad token as suggested by Arthur. * Update the RMSNorm + some other tweaks. * Fix more rebase issues. * fix copies and style * fixes and add helium.md * add missing tests * udpate the backlink * oups * style * update init, and expected results * small fixes * match test outputs * style fixup, fix doc builder * add dummies and we should be good to go!z * update sdpa and fa2 documentation --------- Co-authored-by: laurent <laurent.mazare@gmail.com>
159 lines
5.0 KiB
Markdown
159 lines
5.0 KiB
Markdown
<!--Copyright 2024 Kyutai and The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the
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specific language governing permissions and limitations under the License.
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⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
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rendered properly in your Markdown viewer.
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# Helium
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## Overview
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Helium was proposed in [Announcing Helium-1 Preview](https://kyutai.org/2025/01/13/helium.html) by the Kyutai Team.
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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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- **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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## 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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|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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## 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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Tips:
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- This model was contributed by [Laurent Mazare](https://huggingface.co/lmz)
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## Usage tips
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`Helium` can be found on the [Huggingface Hub](https://huggingface.co/collections/kyutai/helium-1-preview)
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In the following, we demonstrate how to use `helium-1-preview` for the inference.
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```python
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>>> from transformers import AutoModelForCausalLM, AutoTokenizer
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>>> device = "cuda" # the device to load the model onto
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>>> model = AutoModelForCausalLM.from_pretrained("helium-1-preview", device_map="auto")
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>>> tokenizer = AutoTokenizer.from_pretrained("helium-1-preview")
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>>> prompt = "Give me a short introduction to large language model."
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>>> messages = [{"role": "user", "content": prompt}]
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>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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>>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
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>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
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>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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## HeliumConfig
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[[autodoc]] HeliumConfig
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## HeliumModel
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[[autodoc]] HeliumModel
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- forward
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## HeliumForCausalLM
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[[autodoc]] HeliumForCausalLM
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- forward
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## HeliumForSequenceClassification
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[[autodoc]] HeliumForSequenceClassification
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- forward
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## HeliumForTokenClassification
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[[autodoc]] HeliumForTokenClassification
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- forward
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