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Model: OpenLLM-France/Lucie-7B-Instruct-v1.1 Source: Original Platform
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README.md
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---
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license: apache-2.0
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pipeline_tag: text-generation
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language:
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- fr
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- en
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tags:
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- openllm-france
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datasets:
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- cmh/alpaca_data_cleaned_fr_52k
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- OpenLLM-France/Croissant-Aligned-Instruct
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- Gael540/dataSet_ens_sup_fr-v1
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- ai2-adapt-dev/flan_v2_converted
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- teknium/OpenHermes-2.5
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- allenai/tulu-3-sft-personas-math
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- allenai/tulu-3-sft-personas-math-grade
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- allenai/WildChat-1M
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base_model:
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- OpenLLM-France/Lucie-7B
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widget:
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- text: |-
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Quelle est la capitale de l'Espagne ? Madrid.
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Quelle est la capitale de la France ?
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example_title: Capital cities in French
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group: 1-shot Question Answering
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training_progress:
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context_length: 32000
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---
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# Model Card for Lucie-7B-Instruct-v1.1
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* [Model Description](#model-description)
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<!-- * [Uses](#uses) -->
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* [Training Details](#training-details)
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* [Training Data](#training-data)
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* [Preprocessing](#preprocessing)
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* [Instruction template](#instruction-template)
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* [Training Procedure](#training-procedure)
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<!-- * [Evaluation](#evaluation) -->
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* [Testing the model](#testing-the-model)
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* [Test with ollama](#test-with-ollama)
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* [Test with vLLM](#test-with-vllm)
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* [Citation](#citation)
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* [Acknowledgements](#acknowledgements)
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* [Contact](#contact)
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## Model Description
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Lucie-7B-Instruct-v1.1 is a fine-tuned version of [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B), an open-source, multilingual causal language model created by OpenLLM-France. It is meant to replace the original Lucie-7B-Instruct model that was released in January 2025.
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Lucie-7B-Instruct is fine-tuned on a mixture of human-templated and synthetic instructions (produced by ChatGPT) and a small set of customized prompts about OpenLLM and Lucie.
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Note that this instruction training is light and is meant to allow Lucie to produce responses of a desired type (answer, summary, list, etc.). Lucie-7B-Instruct-v1.1 would need further training before being implemented in pipelines for specific use-cases or for particular generation tasks such as code generation or mathematical problem solving. It is also susceptible to hallucinations; that is, producing false answers that result from its training. Its performance and accuracy can be improved through further fine-tuning and alignment with methods such as DPO, RLHF, etc.
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Due to its size, Lucie-7B is limited in the information that it can memorize; its ability to produce correct answers could be improved by implementing the model in a retrieval augmented generation pipeline.
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While Lucie-7B-Instruct is trained on sequences of 4096 tokens, its base model, Lucie-7B has a context size of 32K tokens. Based on Needle-in-a-haystack evaluations, Lucie-7B-Instruct-v1.1 has a context window size of 22K tokens. This window could be increased by fine-tuning on longer data samples.
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## Training details
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### Training data
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Lucie-7B-Instruct-v1.1 is trained on the following datasets:
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* [Alpaca-cleaned-fr](https://huggingface.co/datasets/cmh/alpaca_data_cleaned_fr_52k) (French; 51,655 samples)
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* [Croissant-Aligned-Instruct](https://huggingface.co/datasets/OpenLLM-France/Croissant-Aligned-Instruct) (English-French; 20,000 samples taken from 80,000 total)
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* [ENS](https://huggingface.co/datasets/Gael540/dataSet_ens_sup_fr-v1) (French, 394 samples)
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* [FLAN v2 Converted](https://huggingface.co/datasets/ai2-adapt-dev/flan_v2_converted) (English, 78,580 samples)
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* [Open Hermes 2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) (English, 1,000,495 samples)
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* [Oracle](https://github.com/opinionscience/InstructionFr/tree/main/wikipedia) (French, 4,613 samples)
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* [PIAF](https://www.data.gouv.fr/fr/datasets/piaf-le-dataset-francophone-de-questions-reponses/) (French, 1,849 samples)
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* [TULU3 Personas Math](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-math)
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* [TULU3 Personas Math Grade](https://huggingface.co/datasets/allenai/tulu-3-sft-personas-math-grade)
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* [Wildchat](https://huggingface.co/datasets/allenai/WildChat-1M) (French subset; 26,436 samples)
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* Hard-coded prompts concerning OpenLLM and Lucie (based on [allenai/tulu-3-hard-coded-10x](https://huggingface.co/datasets/allenai/tulu-3-hard-coded-10x))
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* French: openllm_french.jsonl (24x10 samples)
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* English: openllm_english.jsonl (24x10 samples)
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One epoch was passed on each dataset except for Croissant-Aligned-Instruct for which we randomly selected 20,000 translation pairs.
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### Preprocessing
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* Filtering by keyword: Examples containing assistant responses were filtered out from the four synthetic datasets if the responses contained a keyword from the list [filter_strings](https://github.com/OpenLLM-France/Lucie-Training/blob/98792a1a9015dcf613ff951b1ce6145ca8ecb174/tokenization/data.py#L2012). This filter is designed to remove examples in which the assistant is presented as model other than Lucie (e.g., ChatGPT, Gemma, Llama, ...).
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### Instruction template:
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Lucie-7B-Instruct-v1.1 was trained on the chat template from Llama 3.1 with the sole difference that `<|begin_of_text|>` is replaced with `<s>`. The resulting template:
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```
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<s><|start_header_id|>system<|end_header_id|>
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{SYSTEM}<|eot_id|><|start_header_id|>user<|end_header_id|>
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{INPUT}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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{OUTPUT}<|eot_id|>
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```
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An example:
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```
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<s><|start_header_id|>system<|end_header_id|>
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You are a helpful assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>
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Give me three tips for staying in shape.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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1. Eat a balanced diet and be sure to include plenty of fruits and vegetables. \n2. Exercise regularly to keep your body active and strong. \n3. Get enough sleep and maintain a consistent sleep schedule.<|eot_id|>
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```
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### Training procedure
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The model architecture and hyperparameters are the same as for [Lucie-7B](https://huggingface.co/OpenLLM-France/Lucie-7B) during the annealing phase with the following exceptions:
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* context length: 4096<sup>*</sup>
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* batch size: 1024
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* max learning rate: 3e-5
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* min learning rate: 3e-6
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<sup>*</sup>As noted above, while Lucie-7B-Instruct is trained on sequences of 4096 tokens, it maintains the capacity of the base model, Lucie-7B, to handle context sizes of up to 32K tokens.
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## Testing the model
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### Test with ollama
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* Download and install [Ollama](https://ollama.com/download)
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* Download the [GGUF model](https://huggingface.co/OpenLLM-France/Lucie-7B-Instruct-v1.1-gguf/blob/main/Lucie-7B-Instruct-v1.1-q4_k_m.gguf)
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* Copy the [`Modelfile`](https://huggingface.co/OpenLLM-France/Lucie-7B-Instruct-v1.1-gguf/blob/main/Modelfile), adapting if necessary the path to the GGUF file (line starting with `FROM`).
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* Run in a shell:
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* `ollama create -f Modelfile Lucie`
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* `ollama run Lucie`
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* Once ">>>" appears, type your prompt(s) and press Enter.
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* Optionally, restart a conversation by typing "`/clear`"
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* End the session by typing "`/bye`".
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Useful for debug:
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* [How to print input requests and output responses in Ollama server?](https://stackoverflow.com/a/78831840)
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* [Documentation on Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#parameter)
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* Examples: [Ollama model library](https://github.com/ollama/ollama#model-library)
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* Llama 3 example: https://ollama.com/library/llama3.1
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* Add GUI : https://docs.openwebui.com/
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### Test with vLLM
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#### 1. Run vLLM Docker Container
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Use the following command to deploy the model,
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replacing `INSERT_YOUR_HF_TOKEN` with your Hugging Face Hub token.
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```bash
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docker run --runtime nvidia --gpus=all \
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--env "HUGGING_FACE_HUB_TOKEN=INSERT_YOUR_HF_TOKEN" \
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-p 8000:8000 \
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--ipc=host \
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vllm/vllm-openai:latest \
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--model OpenLLM-France/Lucie-7B-Instruct-v1.1
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```
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#### 2. Test using OpenAI Client in Python
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To test the deployed model, use the OpenAI Python client as follows:
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```python
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from openai import OpenAI
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# Initialize the client
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client = OpenAI(base_url='http://localhost:8000/v1', api_key='empty')
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# Define the input content
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content = "Hello Lucie"
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# Generate a response
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chat_response = client.chat.completions.create(
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model="OpenLLM-France/Lucie-7B-Instruct-v1.1",
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messages=[
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{"role": "user", "content": content}
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],
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)
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print(chat_response.choices[0].message.content)
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```
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## Citation
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When using the Lucie-7B-Instruct-v1.1 model, please cite the following paper:
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✍ Olivier Gouvert, Julie Hunter, Jérôme Louradour,
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Christophe Cérisara, Evan Dufraisse, Yaya Sy,
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Laura Rivière, Jean-Pierre Lorré (2025).
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[The Lucie-7B LLM and the Lucie Training Dataset:
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Open resources for multilingual language generation](https://arxiv.org/abs/2503.12294). arxiv:2503.12294.
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```bibtex
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@misc{openllm2025lucie,
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title={The Lucie-7B LLM and the Lucie Training Dataset: Open resources for multilingual language generation},
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author={Olivier Gouvert and Julie Hunter and Jérôme Louradour and Christophe Cerisara and Evan Dufraisse and Yaya Sy and Laura Rivière and Jean-Pierre Lorré and OpenLLM-France community},
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year={2025},
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eprint={2503.12294},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2503.12294},
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}
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```
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## Acknowledgements
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This work was performed using HPC resources from GENCI–IDRIS (Grant 2024-GC011015444). We gratefully acknowledge support from GENCI and IDRIS and from Pierre-François Lavallée (IDRIS) and Stephane Requena (GENCI) in particular.
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Lucie-7B-Instruct-v1.1 was created by members of [LINAGORA](https://labs.linagora.com/) and the [OpenLLM-France](https://www.openllm-france.fr/) community, including in alphabetical order:
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Olivier Gouvert (LINAGORA),
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Ismaïl Harrando (LINAGORA/SciencesPo),
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Julie Hunter (LINAGORA),
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Jean-Pierre Lorré (LINAGORA),
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Jérôme Louradour (LINAGORA),
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Michel-Marie Maudet (LINAGORA), and
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Laura Rivière (LINAGORA).
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We thank
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Clément Bénesse (Opsci),
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Christophe Cerisara (LORIA),
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Émile Hazard (Opsci),
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Evan Dufraisse (CEA List),
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Guokan Shang (MBZUAI),
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Joël Gombin (Opsci),
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Jordan Ricker (Opsci),
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and
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Olivier Ferret (CEA List)
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for their helpful input.
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Finally, we thank the entire OpenLLM-France community, whose members have helped in diverse ways.
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## Contact
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contact@openllm-france.fr
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30
config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 0,
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"eos_token_id": 267,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 12288,
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"max_position_embeddings": 32000,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pad_token_id": 3,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 20000000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.3",
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"use_cache": true,
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"vocab_size": 65024
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}
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"temperature": 0.6,
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"max_length": 4096,
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"top_p": 0.9,
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"transformers_version": "4.36.1"
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}
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||||
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||||
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|
||||
}
|
||||
}
|
||||
30
special_tokens_map.json
Normal file
30
special_tokens_map.json
Normal file
@@ -0,0 +1,30 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
129218
tokenizer.json
Normal file
129218
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
76
tokenizer_config.json
Normal file
76
tokenizer_config.json
Normal file
@@ -0,0 +1,76 @@
|
||||
{
|
||||
"add_bos_token": true,
|
||||
"add_eos_token": false,
|
||||
"added_tokens_decoder": {
|
||||
"0": {
|
||||
"content": "<s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"1": {
|
||||
"content": "</s>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"2": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"3": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"265": {
|
||||
"content": "<|start_header_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"266": {
|
||||
"content": "<|end_header_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"267": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": ["<|start_header_id|>", "<|end_header_id|>", "</s>"],
|
||||
"bos_token": "<s>",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|eot_id|>",
|
||||
"legacy": true,
|
||||
"model_max_length": 1000000000000000000000000000000,
|
||||
"pad_token": "<pad>",
|
||||
"sp_model_kwargs": {},
|
||||
"spaces_between_special_tokens": false,
|
||||
"tokenizer_class": "LlamaTokenizer",
|
||||
"unk_token": "<unk>",
|
||||
"use_default_system_prompt": false,
|
||||
"chat_template": "{{- bos_token }}\n{%- for message in messages %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}\n{%- endif %}"
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user