c23a1c1932
* 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>
5.0 KiB
Helium
Overview
Helium was proposed in Announcing Helium-1 Preview by the Kyutai Team.
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
Evaluation
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 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 | |
| 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 |
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 |
Tips:
- This model was contributed by Laurent Mazare
Usage tips
Helium can be found on the Huggingface Hub
In the following, we demonstrate how to use helium-1-preview for the inference.
>>> from transformers import AutoModelForCausalLM, AutoTokenizer
>>> device = "cuda" # the device to load the model onto
>>> model = AutoModelForCausalLM.from_pretrained("helium-1-preview", device_map="auto")
>>> tokenizer = AutoTokenizer.from_pretrained("helium-1-preview")
>>> prompt = "Give me a short introduction to large language model."
>>> messages = [{"role": "user", "content": prompt}]
>>> text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
>>> model_inputs = tokenizer([text], return_tensors="pt").to(device)
>>> generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512, do_sample=True)
>>> generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
>>> response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
HeliumConfig
autodoc HeliumConfig
HeliumModel
autodoc HeliumModel - forward
HeliumForCausalLM
autodoc HeliumForCausalLM - forward
HeliumForSequenceClassification
autodoc HeliumForSequenceClassification - forward
HeliumForTokenClassification
autodoc HeliumForTokenClassification - forward