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README.md

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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`", "`step0010000`", ..., "`step0025000`", "`step0050000`", "`step0075000`", ...
+
+## Training Details
+
+### Training Data
+
+The training dataset will be made available soon.
+<!-- at [OpenLLM-France/Lucie-Training-Dataset](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset)
+and described in ["The Lucie Training Dataset" (2024/5)](https://arxiv.org/abs/xxxx.xxxxx). -->
+
+### Training Procedure 
+
+The training code is available at [https://github.com/OpenLLM-France/Lucie-Training](https://github.com/OpenLLM-France/Lucie-Training),
+and this based on [this fork of Megatron-DeepSpeed](https://github.com/OpenLLM-France/Megatron-DeepSpeed).
+
+Lucie-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
+
+It was trained on 512 H100 80GB GPUs for about <<TODO>> GPU hours on [Jean Zay supercomputer](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html).
+
+#### 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
+
+[email protected]