README.md

---
license: cc-by-4.0
dataset_info:
  - config_name: dpo_instruct
    features:
    - name: chosen
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    - name: rejected
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    splits:
      - name: dolci_flan
        num_bytes: 93470441
        num_examples: 30009
      - name: dolci_instruct_dpo_persona_precise_if
        num_bytes: 11675333
        num_examples: 4102
      - name: dolci_instruct_dpo_precise_if
        num_bytes: 56594779
        num_examples: 14224
      - name: dolci_python_algorithms
        num_bytes: 787968960
        num_examples: 184361
      - name: dolci_sciriff
        num_bytes: 43425434
        num_examples: 4247
      - name: nemotron_code
        num_bytes: 244686471
        num_examples: 30054
      - name: nemotron_instruction_following_chat_v1
        num_bytes: 1601476867
        num_examples: 70032
      - name: nemotron_math
        num_bytes: 108803362
        num_examples: 21098
      - name: nemotron_safety
        num_bytes: 100125061
        num_examples: 24085
      - name: nemotron_stem
        num_bytes: 341469602
        num_examples: 65738
      - name: pleias_rag
        num_bytes: 201478603
        num_examples: 7891
      - name: smol_instruct_rewrite
        num_bytes: 2208387
        num_examples: 1787
      - name: smolrewrite
        num_bytes: 10017564
        num_examples: 3016
      - name: smolsummarize
        num_bytes: 26356924
        num_examples: 5949
      - name: when2call
        num_bytes: 36565517
        num_examples: 9000
      - name: xlam
        num_bytes: 224893643
        num_examples: 51632
  - config_name: sft_instruct
    features:
    - name: messages
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    splits:
      - name: dolci_flan
        num_bytes: 107784210
        num_examples: 3472
      - name: dolci_logic_puzzles
        num_bytes: 107784210
        num_examples: 83472 
      - name: dolci_instruct_precise_if
        num_bytes: 263950453
        num_examples: 126271
      - name: dolci_python_algorithms
        num_bytes: 296864669
        num_examples: 186038
      - name: dolci_sciriff
        num_bytes: 22228226
        num_examples: 4460
      - name: nemotron_code
        num_bytes: 950381906
        num_examples: 173047
      - name: nemotron_instruction_following_chat_v1
        num_bytes: 2922631310
        num_examples: 218343
      - name: nemotron_math_v2
        num_bytes: 484546923
        num_examples: 239362
      - name: nemotron_posttraining_v2_math_french
        num_bytes: 15678092
        num_examples: 1000
      - name: nemotron_stem
        num_bytes: 761734450
        num_examples: 348608
      - name: pleias_rag
        num_bytes: 12851037010
        num_examples: 796582
      - name: paradocs
        num_bytes: 63732438
        num_examples: 70000
      - name: smol_instruct_rewrite
        num_bytes: 5381064
        num_examples: 13000
      - name: smolrewrite
        num_bytes: 24776697
        num_examples: 15000
      - name: smolsummarize
        num_bytes: 80854929
        num_examples: 35000
      - name: when2call
        num_bytes: 15722853
        num_examples: 7437
      - name: xlam
        num_bytes: 116359989
        num_examples: 60000
      - name: context_qa_hotpot
        num_bytes: 474609683
        num_examples: 74393
      - name: context_qa_tat
        num_bytes: 26401532
        num_examples: 7195
      - name: croissant_aligned_instruct
        num_bytes: 4144675
        num_examples: 12129
      - name: hardcoded_en
        num_bytes: 200770
        num_examples: 1108
      - name: hardcoded_fr
        num_bytes: 215218
        num_examples: 962
      - name: hermes
        num_bytes: 16907558
        num_examples: 1893
      - name: linagora_personas_math
        num_bytes: 44022031
        num_examples: 16448
  - config_name: sft_thinking
    features:
    - name: messages
      list:
      - name: content
        dtype: string
      - name: role
        dtype: string
    splits:
      - name: dolci_think_sft_persona_precise_if
        num_bytes: 1274545838
        num_examples: 216083
      - name: dolci_think_sft_precise_if
        num_bytes: 1387191105
        num_examples: 118644
      - name: nemotron_agentic_toolcalling
        num_bytes: 6598244005
        num_examples: 293291
      - name: nemotron_instruction_following_chat_v1
        num_bytes: 1274545838
        num_examples: 216083
      - name: nemotron_posttrain_v3_math
        num_bytes: 4886080343
        num_examples: 1251769
      - name: nemotron_posttraining_v2_math_french
        num_bytes: 1262603542
        num_examples: 80000
      - name: nemotron_science_v1_mcq
        num_bytes: 1724960038
        num_examples: 173276
      - name: nemotron_code
        num_bytes: 5413209846
        num_examples: 110796
      - name: pleias_rag
        num_bytes: 12812113147
        num_examples: 796582
      - name: opencodereasoning
        num_bytes: 16683924673
        num_examples: 477868
      - name: smoltalk_smolagents_toolcalling
        num_bytes: 207000591
        num_examples: 9079
      - name: synthetic_2_SFT_verified
        num_bytes: 2593211389
        num_examples: 103254
configs:
- config_name: dpo_instruct
  data_files:
  - split: dolci_flan
    path: dpo_instruct/dolci-flan/*.jsonl
  - split: dolci_instruct_dpo_persona_precise_if
    path: dpo_instruct/dolci-instruct-dpo-persona-precise-if/*.jsonl
  - split: dolci_instruct_dpo_precise_if
    path: dpo_instruct/dolci-instruct-dpo-precise-if/*.jsonl
  - split: dolci_python_algorithms
    path: dpo_instruct/dolci-python-algorithms/*.jsonl
  - split: dolci_sciriff
    path: dpo_instruct/dolci-sciriff/*.jsonl
  - split: nemotron_code
    path: dpo_instruct/nemotron-code/*.jsonl
  - split: nemotron_instruction_following_chat_v1
    path: dpo_instruct/nemotron-instruction-following-chat-v1/*.jsonl
  - split: nemotron_math
    path: dpo_instruct/nemotron-math/*.jsonl
  - split: nemotron_safety
    path: dpo_instruct/nemotron-safety/*.jsonl
  - split: nemotron_stem
    path: dpo_instruct/nemotron-stem/*.jsonl
  - split: pleias_rag
    path: dpo_instruct/pleias-rag/*.jsonl
  - split: smol_instruct_rewrite
    path: dpo_instruct/smol-instruct-rewrite/*.jsonl
  - split: smolrewrite
    path: dpo_instruct/smolrewrite/*.jsonl
  - split: smolsummarize
    path: dpo_instruct/smolsummarize/*.jsonl
  - split: when2call
    path: dpo_instruct/when2call/*.jsonl
  - split: xlam
    path: 
    - dpo_instruct/xlam-corrupt-json/*.jsonl
    - dpo_instruct/xlam-remove-argument/*.jsonl
    - dpo_instruct/xlam-remove-tool-call/*.jsonl
    - dpo_instruct/xlam-corrupt-arg-llm/*.jsonl
- config_name: sft_instruct
  data_files:
  - split: dolci_flan
    path: sft_instruct/dolci-flan/*.jsonl
  - split: dolci_logic_puzzles
    path: sft_instruct/dolci-logic-puzzles/*.jsonl
  - split: dolci_instruct_precise_if
    path: sft_instruct/dolci-instruct-precise-if/*.jsonl
  - split: dolci_python_algorithms
    path: sft_instruct/dolci-python-algorithms/*.jsonl
  - split: dolci_sciriff
    path: sft_instruct/dolci-sciriff/*.jsonl
  - split: nemotron_code
    path: sft_instruct/nemotron-code/*.jsonl
  - split: nemotron_instruction_following_chat_v1
    path: sft_instruct/nemotron-instruction-following-chat-v1/*.jsonl
  - split: nemotron_math_v2
    path: sft_instruct/nemotron-math-v2/*.jsonl
  - split: nemotron_posttraining_v2_math_french
    path: sft_instruct/nemotron-posttraining-v2-math-french/*.jsonl
  - split: nemotron_stem
    path: sft_instruct/nemotron-stem/*.jsonl
  - split: pleias_rag
    path: sft_instruct/pleias-rag/*.jsonl
  - split: paradocs
    path: sft_instruct/paradocs/*.jsonl
  - split: smol_instruct_rewrite
    path: sft_instruct/smol-instruct-rewrite/*.jsonl
  - split: smolrewrite
    path: sft_instruct/smolrewrite/*.jsonl
  - split: smolsummarize
    path: sft_instruct/smolsummarize/*.jsonl
  - split: when2call
    path: sft_instruct/when2call/*.jsonl
  - split: xlam
    path: sft_instruct/xlam/*.jsonl
  - split: context_qa_hotpot
    path: sft_instruct/context-qa-hotpot/*.jsonl
  - split: context_qa_tat
    path: sft_instruct/context-qa-tat/*.jsonl
  - split: croissant_aligned_instruct
    path: sft_instruct/croissant-aligned-instruct/*.jsonl
  - split: hardcoded_en
    path: sft_instruct/hardcoded-en/*.jsonl
  - split: hardcoded_fr
    path: sft_instruct/hardcoded-fr/*.jsonl
  - split: hermes
    path: sft_instruct/hermes/*.jsonl
  - split: linagora_personas_math
    path: sft_instruct/linagora-personas-math/*.jsonl
- config_name: sft_thinking
  data_files:
  - split: dolci_think_sft_persona_precise_if
    path: sft_thinking/dolci-think-sft-persona-precise-if/*.jsonl
  - split: dolci_think_sft_precise_if
    path: sft_thinking/dolci-think-sft-precise-if/*.jsonl
  - split: nemotron_agentic_toolcalling
    path: sft_thinking/nemotron-agentic-toolcalling/*.jsonl
  - split: nemotron_instruction_following_chat_v1
    path: sft_thinking/dolci-think-sft-persona-precise-if/*.jsonl
  - split: nemotron_posttrain_v3_math
    path: sft_thinking/nemotron-posttrain-v3-math/*.jsonl
  - split: nemotron_posttraining_v2_math_french
    path: sft_thinking/nemotron-posttraining-v2-math-french/*.jsonl
  - split: nemotron_science_v1_mcq
    path: sft_thinking/nemotron-science-v1-mcq/*.jsonl
  - split: nemotron_code
    path: sft_thinking/nemotron-code/*.jsonl
  - split: pleias_rag
    path: sft_thinking/pleias-rag/*.jsonl
  - split: opencodereasoning
    path: sft_thinking/opencodereasoning/*.jsonl
  - split: smoltalk_smolagents_toolcalling
    path: sft_thinking/smoltalk-smolagents-toolcalling/*.jsonl
  - split: synthetic_2_SFT_verified
    path: sft_thinking/synthetic-2-SFT-verified/*.jsonl

  
  
---

![luciole_logo.png](luciole_logo.png)

<!-- inspired from the following template:
https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md
 -->
**Table of Contents**
* [Dataset Description](#dataset-description)
   * [Curation Rationale](#curation-rationale)
   * [Bias, Risks, and Limitations](#bias-risks-and-limitations)
   * [Data Subsets](#data-subsets)
   * [Sample Metadata](#sample-metadata)
* [Downloading the Data](#downloading-the-data)
   * [Available Configurations](#available-configurations)
   * [Loading Examples](#loading-examples)
   * [Accessing Data Through the Directory Hierarchy](#accessing-data-through-the-directory-hierarchy)
* [Details on Data Sources](#details-on-data-sources)
* [Citation](#citation)
* [Acknowledgements](#acknowledgements)
* [Contact](#contact)


## Dataset Description

The Luciole-PostTraining-Dataset-1.1 is a curated collection of open, instruction-style text data designed for language model post training. It includes a mixture of synthetic and non-synthetic instructions for supervised fine-tuning (SFT) as well as pairs of responses designed for preference alignment (e.g., DPO). With the exception of data for safety alignment, the alignment pairs were generated synthetically with a delta learning approach: all pairs were generated with Qwen3-32B and Qwen3-0.6B and the former were labeled as the accepted responses. Safety data were generated with a mixture of Qwen3-14B , Ministral-3-14B-Instruct, and an interim checkpoint of Luciole-8B-Instruct, after SFT. Pairs were judged with both Ministral-14B-Reasoning and Qwen3-14B. A pair was included only if both models agreed on their labels.

While Luciole-PostTraining-Dataset-1.1 contains some multilingual data, it is primarily English. This version will be followed with versions containing higher levels of multilingual, and especially French, data.

The Luciole PostTraining Dataset was created by the consortium of the [OpenLLM France](https://openllm-france.fr/) project funded by [BPI France](https://www.bpifrance.fr/) as a part of the [France 2030](https://www.info.gouv.fr/grand-dossier/france-2030) program. Datasets were processed and stored on the [GENCI](https://www.genci.fr/) supercomputer Jean Zay, managed by [IDRIS](http://www.idris.fr/docs/idris/missions).

* License: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode.en)
* Code repository: [Luciole-Training](https://github.com/OpenLLM-France/Luciole-Training/tree/main/data)
* Paper: coming soon

### Curation Rationale

The Luciole-PostTraining-Dataset-1.1 contains only corpora previously published under open licenses or new data generated with open-weights models. It was created in part to facilitate the training of large language models in strict conformance to open-source requirements and European laws on AI development and intellectual property. 

Version 1.1 of the dataset is the first step in a larger project of creating a large, open, multilingual post-training dataset, with a particular focus on French. 

By sharing our resources openly, we aim to further research on, and development of, multilingual language models.  

### Bias, Risks, and Limitations
A large portion of data in the Luciole-PostTraining-Dataset-1.1 was generated with third-party, open-weights models which are liable to introduce unwanted linguistic and cultural biases. Efforts to create post-training data targeting languages and cultures in Europe are ongoing and will require an iterative approach. 

Due to its role of teaching models what behavior counts as "unsafe", safety alignment pairs are susceptible to contain toxic or dangerous content. 

A further limitation of this dataset is that it does not distinguish between variants of different languages. American English and varieties of English spoken in England, for example, are merely labeled as "English". In future work, we hope to focus more on regional linguistic diversity.

### Data Subsets

The Luciole-PostTraining-Dataset-1.1 is divided into four subsets: 
* [`sft_instruct`]: instruction-style data without thinking traces
* [`sft_thinking`]: instruction-style data with thinking traces
* [`dpo_instruct`]: instructions with pairs of accepted and rejected responses, without thinking traces
* [`dpo_thinking`]: instructions with pairs of accepted and rejected responses, with thinking traces (🚧 coming soon)

### Sample Metadata

The `SFT` subsets contain a single [`messages`] field which provides the content of the sample formatted as a conversation following the HuggingFace chat format. 

The `DPO` subsets contain [`chosen`] and [`rejected`] fields, each formatted as a conversation where the content is identical except for the final `assistant` turns.  

## Downloading the Data

### Available Configurations

The dataset is organized into **configurations**, each corresponding to a subset of the data used for a particular training stage, e.g. instruction finetuning.

The list of available configurations can be obtained programmatically:

```python
from datasets import get_dataset_config_names

config_names = get_dataset_config_names(
    "OpenLLM-France/Luciole-PostTraining-Dataset-1.1"
)
print(config_names)
```
```plaintext
['dpo_instruct', 'sft_instruct', 'sft_thinking', 'dpo_thinking']

```
### Loading Examples

<!-- All examples below use `streaming=True`, which is recommended for large-scale processing since the dataset can be streamed without being fully downloaded locally. -->

Load all the samples for a particular configuration, here `dpo_instruct`:

```python
from datasets import load_dataset

dataset = load_dataset(
    "OpenLLM-France/Luciole-PostTraining-Dataset-1.1",
    "dpo_instruct"
)
```

Load the samples for a particular split within a configuration, here `smolrewrite` in `dpo_instruct`:
```python
dataset = load_dataset(
    "OpenLLM-France/Luciole-PostTraining-Dataset-1.1",
    "dpo_instruct",
    split="smolrewrite"
)
```

<!-- ### Accessing Data Through the Directory Hierarchy

In addition to the predefined configurations, data can be loaded directly from specific directories in the dataset hierarchy using the `data_dir` argument.

The complete organization of the dataset is described in [`data_hierarchy.txt`](data_hierarchy.txt).

For example, to load all Python-related data:

```python
dataset = load_dataset(
    "OpenLLM-France/Luciole-Training-Dataset",
    data_dir="data/**/python"
)

dataset = load_dataset(
    "OpenLLM-France/Luciole-PostTraining-Dataset-1.1",
    data_dir=
)
```

This approach can be useful for selecting data subsets that span multiple configurations or that are not exposed as dedicated configuration names. -->

## Details on Data Sources

To preprocess the split datasets, we checked for the presence of names of LLMs and companies (Claude, Amazon, etc.) as well as for Chinese and Russian. Given that most of the data was generated with open source models from Qwen and DeepSeek, there was a preponderance of Chinese and Russian script intermingled with our languages of interest. Samples containing any of these were removed entirely. The prepocessing scripts can be found in [this folder](https://github.com/OpenLLM-France/Luciole-Training/tree/main/data/processing/posttraining) of the ``Luciole-Training`` repository. 

All datasets sourced for different post training phases for Luciole Instruct 1.1 models are listed in the table below, according to data category. Note that for certain datasets we upload our preprocessed version here in its entirety, but only used a randomly selected subsample during training. These are indicated by a weight in <span style="color: cyan;">cyan</span>, the proportion of the total data which is equal to the number of samples indicated. All of the datasets are in English, except where indicated in <span style="color: violet;">violet</span>.  

<!-- Julie: Feel free to change the section emojis in the table if something doesn't make sense -->

|                            | **Thinking** | **Instruct**  | **Instruct <br> DPO** |  **Thinking <br> DPO** 🚧 WIP |
|----------------------------|----------|-----------|------------|------------|
| 🗨️ **Chat/IF**                    |          |           |            |  |
| [Nemotron-Instruction-Following-Chat-v1](https://huggingface.co/datasets/nvidia/Nemotron-Instruction-Following-Chat-v1) | 72.9K  | 340K | 68.3K |  - |
| [Dolci-Think-SFT (Persona Precise IF)](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B)  |   216K   | -  | -| - |
| [Dolci-Think-SFT (Precise IF)](https://huggingface.co/datasets/allenai/Dolci-Think-SFT-7B) |  118.6K   | -  | -| - |
| [Dolci-Instruct-SFT (Precise IF)](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT)|       -   | 126K  | -| - |
| [Dolci-Instruct-DPO (Precise IF)](https://huggingface.co/datasets/allenai/Dolci-Instruct-DPO) |       -   | - | 14.7K | - |
| [Dolci-Instruct-DPO (Persona Precise IF)](https://huggingface.co/datasets/allenai/Dolci-Instruct-DPO)  |       -   | - | 4K| - |
| [smol-instruct-rewrite](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)  |      -    | 13K |  1.7K | - |
| [smolrewrite](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)                 | - | 15K     |   3K   |  -|
| [smolsummarize](https://huggingface.co/datasets/HuggingFaceTB/smoltalk)               | - | 35K   |  5.9K   | - |
| 🧮 **Math**                       |          |           |            |  |
| [Nemotron Post-training v3 low no tools (Nemotron-Math-v2)](https://huggingface.co/datasets/nvidia/Nemotron-Math-v2)  | 1.25M |  -    |  -      | - |
| [SYNTHETIC-2-SFT-Verified](https://huggingface.co/datasets/PrimeIntellect/SYNTHETIC-2-SFT-verified)  | 103K |  -    |      -      |  - |
| [Nemotron-Post-Training-Dataset-v2 (math)](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2)  | -   |    239K       |   18.6K <span style="color: cyan;">(0.88)</span>  |  -|
| [Nemotron-Post-Training-Dataset-v2 (multilingual)](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) <span style="color: violet;">(French)</span>  | 80K  |      80K        |  - |  - |
| [LINAGORA Personas Math](https://huggingface.co/datasets/OpenLLM-France/personas_math_english)      | -  |    16K        |   -  |-  |
| 🤖 **Code**                       |          |           |            |  |
| [OpenCodeReasoning](https://huggingface.co/datasets/nvidia/OpenCodeReasoning) | 477K   |     -      |     -       | - |
| [Nemotron-Post-Training-Dataset-v2 (code)](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) | 10K | 173K     |   -   | - |
| [Dolci-Instruct-SFT (python algorithms)](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT) | -   |  186K      |   12.8K  <span style="color: cyan;">(0.07)</span>    | - |
| 🔬 **STEM**    |          |           |            |  |
| [Nemtron-Science-v1 (MCQ)](https://huggingface.co/datasets/nvidia/Nemotron-Science-v1)   | 173K  |      -     |      -      | - |                   
| [Nemotron-Post-Training-Dataset-v2 (stem)](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2) | -  |  348K      |     65K      |  -|
| [Dolci-Instruct-SFT (SCRIFF)](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT)               | -   |    4K       |    4K        | - |
| 🌎 **Translation**                       |          |           |            |  |
| [Croissant-Aligned-Instruct](https://huggingface.co/datasets/OpenLLM-France/Croissant-Aligned-Instruct) (English, French)  |- |  12K      |   -    | - |
| [Paradocs](https://huggingface.co/datasets/jhu-clsp/paradocs) <span style="color: violet;">(English, French)</span>            | -  |    70K     |  -      | - |
| 🌳 **NLI**                       |          |           |            |  |
| [Dolci-Instruct-SFT (FLAN)](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT)  | - |     83K      |    17K <span style="color: cyan;">(0.55)</span>       | - |
| [Dolci-Instruct-SFT (Logic Puzzles)](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT)  | - |      160K     |      -      |  - |
| 🔧 **Tools**                       |          |           |            |  |
| [smoltalk2 (smallagents toolcalling traces think)](https://huggingface.co/datasets/HuggingFaceTB/smoltalk2/viewer/SFT/smolagents_toolcalling_traces_think) | 9K  |    -       |      -      | - |
| [Nemotron-agentic-toolcalling](https://huggingface.co/datasets/nvidia/Nemotron-SFT-Agentic-v2)                  |29K |     -      |     -       | - |
| [HERMES](https://huggingface.co/datasets/NousResearch/hermes-function-calling-v1)   | - |   7K     |      -      | - |
| [xlam](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)      | - |     60K      |    -        | - |
| [When2Call](https://huggingface.co/datasets/nvidia/When2Call)  | -  |    7K       |    9K <span style="color: cyan;">(0.5)</span>       | - |
| [xlam corrupt json](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)                   | - |    -       |      956 <span style="color: cyan;">(0.06)</span>    | - |  
| [xlam remove required argument](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)                   | - |    -       |    2.9K <span style="color: cyan;">(0.7)</span>      | - | 
| [xlam remove tool call](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)        | - |    -       |    2.9K  <span style="color: cyan;">(0.15)</span>     | - | 
| [corrupt argument llm](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k)     | - |    -       |      2.9K <span style="color: cyan;">(0.1)</span>    | - | 
| 📚 **RAG**                       |          |           |            |  |
| [PleiasRAG](https://huggingface.co/datasets/PleIAs/SYNTH)  | 99.5K <span style="color: cyan;">(0.125)</span>   |     99.5K <span style="color: cyan;">(0.125)</span>     |      6K      | - |  
| [ContextQA_hotpot_QA](https://huggingface.co/datasets/OpenLLM-France/Luciole_RAG/viewer/hotpotqa) | -  |    74K       |     -       | - |
| [ContextQA_TAT_QA](https://huggingface.co/datasets/OpenLLM-France/Luciole_RAG/viewer/tatqa) | -  |   7K     |      -      | - |
| 🚸 **Safety**                       |          |           |            |  |
| [Nemotron content safety reasoning]() <span style="color: violet;">(English, French, German, Spanish, Italian)</span>| - |      -     |      24K      | - |
| 👷🏽‍♀️ **Hardcoded**                       |          |           |            |  |
| [Hardcoded EN](https://huggingface.co/datasets/OpenLLM-France/Luciole-PostTraining-Dataset-1.1/tree/main/sft_instruct/hardcoded-en)                  |-|      962     |       -     | - |
| [Hardcoded FR](https://huggingface.co/datasets/OpenLLM-France/Luciole-PostTraining-Dataset-1.1/tree/main/sft_instruct/hardcoded-fr)                  | -  |     1.1K      |     -       | - |
| **Total**                       |    2.6M      |     2.1M      |    284K        | -  |

### Token Counts

For each subset, the charts below provide a breakdown by token count showing the relative proportions of each data category. The counts for the instruct datasets are based on outputs from the [Luciole instruct tokenizer](https://huggingface.co/OpenLLM-France/Luciole-1B-Instruct-1.1/blob/main/chat_template.jinja), and thinking datasets from the [Luciole thinking tokenizer]() (🚧 coming soon). 

<table border="0">
  <tr>
    <td><img src="barchart_try2_think_sft.png" alt="Figure 1" width="90%"></td>
    <td><img src="barchart_try2_inst_sft.png" alt="Figure 2" width="100%"></td>
  </tr>
  <tr>
    <td><img src=""width="100%"></td>
    <td><img src="barchart_try2_inst_dpo.png" alt="Figure 2" width="100%"></td>
  </tr>
</table>


## Citation

✍ Paper coming soon!


## Acknowledgements

We gratefully acknowledge BPI France for funding the OpenLLM France project under the call "Communs numériques pour l’intelligence artificielle générative" ("Digital commons for generative artificial intelligence").

Processing and storage of the Luciole-PostTraining-Dataset-1.1 was made possible by computing AI and storage resources by GENCI at IDRIS thanks to the grants AD011014561, A0201016189, and AS011016445 on the supercomputer Jean Zay. We gratefully acknowledge support from GENCI and IDRIS and from Stephane Requena (GENCI) and Pierre-François Lavallée (IDRIS) in particular. 

The Luciole-PostTraining-Dataset-1.1 was created by members of [LINAGORA](https://labs.linagora.com/) and [OpenLLM-France](https://openllm-france.fr/), including, in alphabetical order:  
 
Akshay Chaturvedi (LINAGORA)    
Liam Duignan (CEA List)    
Olivier Ferret (CEA List)  
Olivier Gouvert (LINAGORA)  
Émile Hasard (OpSci)  
Julie Hunter (LINAGORA)  
Jean-Pierre Lorré (LINAGORA)  
Jérôme Louradour (LINAGORA)     
Kate Thompson (LINAGORA)   
Dev Jerusha Anish Udayan (TALK'R)   
Matteo van Ypersele (LINAGORA)

We thank the support team from NVIDIA for their technical and high-level guidance throughout the project, especially: Meriem Bendris, Julia Gusak, Anna Louise Ollerenshaw, Hagit Paz, Christelle Piechurski, Oleg Sudakov.    

We are also greatful to the partners of the [OpenLLM-France](https://www.openllm-france.fr/) consortium for their valuable input, with particular thanks to (in alphabetical order):  

Clément Bénesse (OpSci)  
Gabriel Lauzzana (LORIA)   
Nathaniël de Leeuw (CEA)    
Michel-Marie Maudet (LINAGORA)  

We would also like to thank the numerous open data projects that have guided us in the process of creating this dataset, including projects by [Pleias](https://pleias.fr/), [Nvidia](https://www.nvidia.com/en-eu/), [Hugging Face](https://huggingface.co/) and [Allen AI](https://allenai.org/). 

Finally, we thank the entire OpenLLM-France community, whose members have helped in diverse ways. 


## Contact

contact@openllm-france.fr