Update README.md

README.md

@@ -25,314 +25,3 @@ training_progress:
   context_length: 32000
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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)
-  * [Load the model](#load-the-model)
-  * [Sentence completion](#sentence-completion)
-  * [Load a checkpoint](#load-a-checkpoint)
-* [Training Details](#training-details)
-  * [Training Data](#training-data)
-  * [Training Procedure](#training-procedure)
-    * [Neural Network Architecture](#neural-network-architecture)
-    * [Training Hyperparameters](#training-hyperparameters)
-      1. [Main Pre-training](#1-main-pre-training)
-      2. [Context Extension](#2-context-extension)
-      3. [Annealing](#3-annealing)
-  * [Training Logs and Learning Curves](#training-logs-and-learning-curves)
-<!-- * [Evaluation](#evaluation) -->
-* [Disclaimer](#disclaimer)
-* [Citation](#citation)
-* [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
-
-### Load the model
-
-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
-)
-```
-### Sentence completion
-
-Wrap the model in a text generation pipeline, and specify 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="step0753851",
-    ...
-)
-```
-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)": every 5000 steps for the first pre-training steps (with a context length of 4096).
-* "[`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)": every 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.
-* "[`extension_step0000250`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/extension_step0000250)", "[`extension_step0000500`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/extension_step0000500)", "[`extension_step0000750`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/extension_step0000750)", "[`extension_step0001000`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/extension_step0001000)", "[`extension_step0001220`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/extension_step0001220)": several checkpoints during context extension (with a context length of 32000).
-
-## 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/pie_dataset_composition.png)
-
-Some of the data was upsampled to balance the training data distribution yielding the following  composition for training:
-
-![Training Data Composition](figures/pie_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 the [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 [Llama3.1](https://huggingface.co/meta-llama/Llama-3.1-8B).
-It has exactly 6 706 958 336 free parameters,
-with the following hyperparameters:
-| **Hyperparameter**        | **Value** |
-|---------------------------|---------|
-| Vocabulary size (\# tokens)| 65 024 |
-| \# 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 |
-
-The "theta" parameter of Rotary Positional Embedding (RoPE) was increased during the training process. Its values are indicated in the tables with training hyperparameters below.
-
-#### Training Hyperparameters
-
-The training consisted of three main phases:
-1. Main pre-training on 3.1T tokens, with a context length of 4096,
-2. Context extension on 5B tokens, with a context length of 32000,
-3. Annealing on 5B tokens of high quality data composed of a mixture of new data and data seen during training.
-<!-- perhaps cite the dataset for annealing  -->
-
-The details of each phase are given below.
-
-##### 1. Main Pre-training
-
-Training hyperparameters in torch/Megatron-DeepSpeed were as follows:
-| **Hyperparameter**     | **Value**  |
-|------------------------|------------|
-| Total \# samples| 762 144 586 (3.1T tokens) |
-| Total \# steps  | 753 851    |
-| RoPE theta             | 500 000    |
-| Context length         | 4 096      |
-| Initial Batch size     | 256        |
-| Final Batch size       | 1 024      |
-| Batch size rampup      | by steps of 64 over 10M samples |
-| Learning rate schedule | warmup (2M samples) + cosine annealing |
-| Maximum Learning rate  | 3e-4       |
-| Final Learning rate    | 3e-5       |
-| Weight decay           | 0.1        |
-| Dropout                | _          |
-| Gradient clipping      | 1          |
-| Initializer range      | 0.009        |
-| Optimizer              | `AdamW` (β₁=0.9, β₂=0.95, ε=1e-5)    |
-| Precision              | `bfloat16` |
-| Tensor Parallelism (with 512 GPUs)   | 4           |
-| Pipeline Parallelism (with 512 GPUs) | 4           |
-| Data Parallelism (with 512 GPUs)     | 32          |
-
-#### 2. Context Extension
-
-Training hyperparameters are the same as above, with the following changes:
-| **Hyperparameter**     | **Value**  |
-|------------------------|------------|
-| Total \# samples| 156 250 (5B tokens) |
-| Total \# steps  | 1 220      |
-| RoPE theta             | 20 000 000 |
-| Context length         | 32 000     |
-| Batch size             | 128        |
-| Learning rate          | 2e-5       |
-| Learning rate schedule | constant   |
-| Tensor Parallelism (with 128 GPUs)   | 4     |
-| Pipeline Parallelism (with 128 GPUs) | 4     |
-| Data Parallelism (with 128 GPUs)     | 8     |
-
-#### 3. Annealing
-
-Training hyperparameters are the same as for context extension, with the following changes:
-| **Hyperparameter**     | **Value**  |
-|------------------------|------------|
-| Learning rate schedule | linear annealing |
-| Maximum Learning rate  | 3e-5       |
-| Final Learning rate    | 0          |
-
-### Training Logs and Learning Curves
-
-#### Training loss
-
-Training logs can be found in Tensorboard format in:
-* [`metadata/training_logs/`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/main/metadata/training_logs)
-<br> ├── [`1_pretraining.zip`](metadata/training_logs/1_pretraining.zip) training logs for the first pre-training phases,
-in a zip file. Each file in the zip corresponds to a job of at most 20H of training (parallelized over 512 GPUs).
-<br> ├── [`2_extension/`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/main/metadata/training_logs/2_extension) folder containing the training log <br> └── [`3_annealing/`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/main/metadata/training_logs/3_annealing) folder containing the training log for the annealing phase, which also took around 13H of training (parallelized over 128 GPUs).
-
-The convergence curves of the three pre-training phases are the following:
-
-![figures/convergence-curve-pretraining.png](figures/convergence-curve-pretraining.png)
-
-Data corresponding to these plots were extracted from tensorboard logs and are available in the following CSV files:
-* [`metadata/training_logs/`](https://huggingface.co/OpenLLM-France/Lucie-7B/tree/main/metadata/training_logs)
-<br> ├── [`1_pretraining.csv`](metadata/training_logs/1_pretraining.csv)
-<br> ├── [`2_extension.csv`](metadata/training_logs/2_extension.csv)
-<br> └── [`3_annealing.csv`](metadata/training_logs/3_annealing.csv)
-
-#### Evaluations
-
-Multiple evaluations were conducted during Lucie-7B's training to assess its performance on standard benchmarks,
-primarily in French and English, as well as in Spanish, German, and Italian.
-
-Evaluation results on benchmark datasets of checkpoints of Lucie-7B throughout the training process are available at
-[metadata/evaluation_learning_curve_lucie.csv](metadata/evaluation_learning_curve_lucie.csv).
-Evaluation results of baseline models on the same benchmark datasets are available at
-[metadata/evaluation_baselines.csv](metadata/evaluation_baselines.csv).
-
-Main results are summarized in the following figures:
-
-### French
-![figures/learning-curve-evaluation-french-bench.png](figures/learning-curve-evaluation-french-bench.png)
-
-### English
-![figures/learning-curve-evaluation-benchmarks-in-english.png](figures/learning-curve-evaluation-benchmarks-in-english.png)
-
-### other
-![figures/learning-curve-evaluation-multilingual-arc-benchmark.png](figures/learning-curve-evaluation-multilingual-arc-benchmark.png)
-
-### Needle in a Haystack
-
-#### Pretraining
-![figures/needle-in-a-haystack/Lucie-7B-main.png](figures/needle-in-a-haystack/Lucie-7B-main.png) 
-
-#### Context Extension
-![figures/needle-in-a-haystack/Lucie-7B-extension.png](figures/needle-in-a-haystack/Lucie-7B-extension.png) 
-
-#### Annealing
-![figures/needle-in-a-haystack/Lucie-7B-annealing.png](figures/needle-in-a-haystack/Lucie-7B-annealing.png) 
-
-
-## Disclaimer
-
-Lucie-7B is a language model trained solely to predict the most probable next word in a sequence. Despite efforts to filter the [Lucie Training Dataset](https://huggingface.co/datasets/OpenLLM-France/Lucie-Training-Dataset), it is possible that Lucie-7B encountered strings containing toxic or offensive language during its training and as a result, it may generate strings of similar quality. To limit such behavior, it is advised to fine-tune Lucie-7B through instruction and/or preference tuning (DPO, RLHF, etc.).
-
-## Citation
-
-TODO
-
-
-## 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), and
-Yaya Sy (LORIA).
-
-We thank 
-Anastasia Stasenko (OpSci/Pleias), 
-Clément Bénesse (Opsci), 
-Guokan Shang (MBZUAI), 
-Ismaïl Harrando (LINAGORA), 
-Joël Gombin (Opsci), 
-Jordan Ricker (Opsci), 
-Olivier Ferret (CEA), 
-Pierre-Carl Langlais (OpSci/Pleias), 
-and 
-Rachel Bawden (INRIA),
-for their helpful input.
-
-## Contact
-
-[email protected]