Update README.md

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+datasets:
+- yahma/alpaca-cleaned
+- cmh/alpaca_data_cleaned_fr_52k
+- Magpie-Align/Magpie-Gemma2-Pro-200K-Filtered
+- allenai/WildChat-1M
+language:
+- fr
+- en
+base_model:
+- OpenLLM-France/Lucie-7B
+pipeline_tag: text-generation
+---
+
+## Model Description
+
+Lucie-7B-Instruct is a fine-tuned version of [Lucie-7B](), an open-source, multilingual causal language model created by OpenLLM-France.
+
+Lucie-7B-Instruct is fine-tuned on synthetic instructions produced by ChatGPT and Gemma and a small set of customized prompts about OpenLLM and Lucie.
+
+
+
+
+## Training details
+
+### Training data
+
+Lucie-7B-Instruct is trained on the following datasets:
+* [Alpaca-cleaned](https://huggingface.co/datasets/yahma/alpaca-cleaned) (English; 51604 samples)
+* [Alpaca-cleaned-fr](https://huggingface.co/datasets/cmh/alpaca_data_cleaned_fr_52k) (French; 51655 samples)
+* [Magpie-Gemma](https://huggingface.co/datasets/Magpie-Align/Magpie-Gemma2-Pro-200K-Filtered) (English; 195167 samples)
+* [Wildchat](https://huggingface.co/datasets/allenai/WildChat-1M) (French subset; 26436 samples)
+* 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))
+    * French: openllm_french.jsonl (24x10 samples)
+    * English: openllm_english.jsonl (24x10 samples)
+
+
+### Preprocessing
+* Filtering by language: Magpie-Gemma and Wildchat were filtered to keep only English and French samples, respectively.
+* 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, ...).
+
+
+
+### Training procedure
+
+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:
+* context length: 4096 
+* batch size: 1024
+* max learning rate: 3e-5
+* min learning rate: 3e-6
+
+
+## Testing the model
+
+### Test in python
+
+* [test_transformers_gguf.py](test_transformers_gguf.py): Test GGUF model with `transformers` package (WARNING: loading the model is long)
+
+### Test with ollama
+
+* Download and install [Ollama](https://ollama.com/download)
+* Download the [GGUF model](https://huggingface.co/OpenLLM-France/Lucie-7B-Instruct-v1/resolve/main/Lucie-7B-q4_k_m.gguf)
+* Copy the [`Modelfile`](Modelfile), adpating if necessary the path to the GGUF file (line starting with `FROM`).
+* Run in a shell:
+    * `ollama create -f Modelfile Lucie`
+    * `ollama run Lucie`
+* Once ">>>" appears, type your prompt(s) and press Enter.
+* Optionally, restart a conversation by typing "`/clear`"
+* End the session by typing "`/bye`".
+
+Useful for debug:
+* [How to print input requests and output responses in Ollama server?](https://stackoverflow.com/a/78831840)
+* [Documentation on Modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#parameter)
+   * Examples: [Ollama model library](https://github.com/ollama/ollama#model-library)
+      * Llama 3 example: https://ollama.com/library/llama3.1
+* Add GUI : https://docs.openwebui.com/
+
+### Test with vLLM
+
+#### 1. Run vLLM Docker Container
+
+Use the following command to deploy the model,
+replacing `INSERT_YOUR_HF_TOKEN` with your Hugging Face Hub token.
+
+```bash
+docker run --runtime nvidia --gpus=all \
+    --env "HUGGING_FACE_HUB_TOKEN=INSERT_YOUR_HF_TOKEN" \
+    -p 8000:8000 \
+    --ipc=host \
+    vllm/vllm-openai:latest \
+    --model OpenLLM-France/Lucie-7B-Instruct-v1
+```
+
+#### 2. Test using OpenAI Client in Python
+
+To test the deployed model, use the OpenAI Python client as follows:
+
+```python
+from openai import OpenAI
+
+# Initialize the client
+client = OpenAI(base_url='http://localhost:8000/v1', api_key='empty')
+
+# Define the input content
+content = "Hello Lucie"
+
+# Generate a response
+chat_response = client.chat.completions.create(
+    model="OpenLLM-France/Lucie-7B-Instruct-v1",
+    messages=[
+        {"role": "user", "content": content}
+    ],
+)
+print(chat_response.choices[0].message.content)
+```