OpenLLM-France
/

Lucie-7B

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-# Model Card for Lucie-7B
-<!-- inspired from the following template:
-https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1
--->
-* [Model Description](#model-description)
-<!-- * [Uses](#uses) -->
-* [Example code in python](#example-code-in-python)
-  * [Sentence completion](#sentence-completion)
-  * [Load a checkpoint](#load-a-checkpoint)
-* [Training Details](#training-details)
-  * [Training Data](#training-data)
-  * [Training Procedure](#training-procedure)
-<!-- * [Evaluation](#evaluation) -->
-* [Acknowledgements](#acknowledgements)
-* [Contact](#contact)
-## Model Description
-Lucie-7B is a pretrained 7B parameter causal language model built by [LINAGORA](https://labs.linagora.com/) and [OpenLLM-France](https://github.com/OpenLLM-France),
-available under the [Apache 2.0 license](https://www.apache.org/licenses/LICENSE-2.0).
-Lucie-7B was trained on 3 trillion tokens of multilingual data, including
-English (33.2%),
-French (32.4%),
-German (6.9%),
-Spanish (6.6%),
-Italian (3.8%),
-and parallel data from those languages (2.5%),
-as well as several programming languages (14.7%).
-## Example code in python
-### Sentence completion
-Load the model (quantized version on GPU if possible, for efficient inference):
-```python
-import transformers
-model_name = "OpenLLM-France/Lucie-7B"
-tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
-    device_map="auto",
-    load_in_4bit=True       # For efficient inference, if quantization is supported by the GPU card
-)
-```
-Wrap the model in a text generation pipeline, and prepare some generation parameters:
-```
-pipeline = transformers.pipeline("text-generation", model=model, tokenizer=tokenizer)
-generation_kwargs = dict(
-    num_return_sequences=1,               # Number of variants to generate.
-    return_full_text= False,              # Do not include the prompt in the generated text.
-    do_sample=True,
-    temperature=1.0, top_p=1, top_k=None, # Sampling parameters.
-    max_new_tokens=200,                   # Maximum length for the output text (in number of tokens).
-)
-```
-Try 1-shot question answering:
-```python
-prompt = """\
-Quelle est la capitale de l'Espagne ? Madrid\n\
-Quelle est la capitale de la France ?\
-"""
-completions = pipeline(prompt, **generation_kwargs)
-for completion in completions:
-    print(prompt + " […]" + completion['generated_text'])
-```
-This will print something like:
-```
-Quelle est la capitale de l'Espagne ? Madrid
-Quelle est la capitale de la France ? […] Paris
-Quelle est la capitale de l'Italie? Rome
-Quelle est la capitale de la Grande-Bretagne? Londres
-Quelle est la capitale de la Suisse? Berne
-Quelle est la capitale du Portugal? Lisbonne
-Quelle est la capitale de l'Algérie? Alger
-...
-```
-If running on GPU (`cuda` device), you will need at least 6GB of VRAM to run inference using 4bit quantization (16GB of VRAM without 4bit quantization).
-### Load a checkpoint
-Checkpoints at several training steps are available under revision tags,
-every 5000 steps during the first 25000 steps, and then every 25000 steps.
-Intermediate checkpoints can be loaded using the `revision` parameter:
-```python
-model = transformers.AutoModelForCausalLM.from_pretrained(model_name,
-    revision="step0400000",
-    ...
-)
-```
-where `revision` can be one of:
-* ["`step0005000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0005000), ["`step0010000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0010000), ["`step0015000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0015000), ["`step0020000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0020000): each 5000 steps for the first pre-training steps.
-* ["`step0025000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0025000), ["`step0050000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0050000), ["`step0075000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0075000), ["`step0100000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0100000), ..., ["`step0750000`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0750000): each 25000 steps from 25k to 750k steps.
-* ["`step0753851`"](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/step0753851): last pre-training step before context extension and annealing.
-## Training Details
-### Training Data
-The training dataset used for the pretraining of Lucie-7B is available
-at [OpenLLM-France/Lucie-Training-Dataset](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset).
-<!-- and described in ["The Lucie Training Dataset" (2024/12)](https://arxiv.org/abs/xxxx.xxxxx). -->
-The initial composition of the training data is as follows:
-![Initial Data Composition](figures/fig_dataset_composition.png)
-Some of the data was upsampled to balance the training data distribution, and the final composition is as follows:
-![Training Data Composition](figures/fig_dataset_composition_training.png)
-### Training Procedure
-Lucie-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
-It was pre-trained on 512 H100 80GB GPUs for about 550\,000 GPU hours on [Jean Zay supercomputer](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html).
-The training code is available at [https://github.com/OpenLLM-France/Lucie-Training](https://github.com/OpenLLM-France/Lucie-Training).
-It is based on [this fork of Megatron-DeepSpeed](https://github.com/OpenLLM-France/Megatron-DeepSpeed).
-Optimizer checkpoints are available at [OpenLLM-France/Lucie-7B-optimizer-states](https://huggingface.co/OpenLLM-France/Lucie-7B-optimizer-states).
-#### Neural Network Architecture
-Lucie-7B has the same neural network architecture as Llama3.
-It has exactly 6 706 958 336 free parameters,
-with the following hyperparameters:
-| **Hyperparameter**        | **Value** |
-|---------------------------|---------|
-| Vocabulary size (\# tokens)| 65 024|
-| ROPE theta                | 500 000|
-| \# transformer blocks     |      32|
-| \# attention heads        |      32|
-| \# key-value heads        |       8|
-| Hidden size               |   4 096|
-| Feed-Forward hidden size  |  12 288|
-| Activation                |  `silu`|
-| RMS norm epsilon          |    1e-5|
-#### Training Hyperparameters
-Training hyperparameters in torch/Megatron-DeepSpeed were the following:
-| **Hyperparameter**     | **Value**  |
-|------------------------|------------|
-| Optimizer              | `AdamW`    |
-| Precision              | `bfloat16` |
-| Initial batch size     | 256        |
-| Final batch size       | 1024       |
-| Batch size rampup      | by steps of 64 over 10M samples |
-| Context length         | 4096       |
-| Learning rate schedule | warmup + cosine annealing |
-| Maximum Learning rate  | 3e-4       |
-| Final Learning rate    | 3e-5       |
-| Weight decay           | 0.1        |
-| Dropout                | _          |
-| Gradient clipping      | 1          |
-| Initializer range      | 0.2        |
-| Tensor Parallelism (with 512 GPUs)   | 4           |
-| Pipeline Parallelism (with 512 GPUs) | 4           |
-| Data Parallelism (with 512 GPUs)     | 32          |
-## Acknowledgements
-This work was performed using HPC resources from GENCI–IDRIS (Grant 2024-GC011015444).
-Lucie-7B was created by members of [LINAGORA](https://labs.linagora.com/) and OpenLLM-France community, including in alphabetical order:
-Christophe Cerisara (LORIA),
-Evan Dufraisse (CEA),
-Julie Hunter (LINAGORA),
-Jean-Pierre Lorré (LINAGORA),
-Jérôme Louradour (LINAGORA),
-Michel-Marie Maudet (LINAGORA),
-Olivier Gouvert (LINAGORA),
-Pierre-Carl Langlais (OpSci),
-Yaya Sy (LORIA).
-## Contact
-contact@openllm-france.fr

 #         top_p: 1.0
 #         top_k: null
 #         max_new_tokens: null
+training_progress:
+   num_steps: 754351
+   num_tokens: 3123790086144
+   context_length: 32000
 ---