Instructions to use jpacifico/Lucie-Boosted-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jpacifico/Lucie-Boosted-7B-Instruct with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="jpacifico/Lucie-Boosted-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jpacifico/Lucie-Boosted-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("jpacifico/Lucie-Boosted-7B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jpacifico/Lucie-Boosted-7B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jpacifico/Lucie-Boosted-7B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Lucie-Boosted-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/jpacifico/Lucie-Boosted-7B-Instruct

SGLang

How to use jpacifico/Lucie-Boosted-7B-Instruct with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "jpacifico/Lucie-Boosted-7B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Lucie-Boosted-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "jpacifico/Lucie-Boosted-7B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jpacifico/Lucie-Boosted-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use jpacifico/Lucie-Boosted-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/jpacifico/Lucie-Boosted-7B-Instruct
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Lucie-Boosted-7B-Instruct

Post-training optimization of the foundation model OpenLLM-France/Lucie-7B-Instruct
DPO fine-tuning using the jpacifico/french-orca-dpo-pairs-revised RLHF dataset.
Training in French also enhances the model's overall performance.
Lucie-7B has a context size of 32K tokens

OpenLLM Leaderboard

coming soon

MT-Bench

coming soon

Usage

You can run this model using this Colab notebook

You can also run Lucie-Boosted using the following code:

import transformers
from transformers import AutoTokenizer

# Format prompt
message = [
    {"role": "system", "content": "You are a helpful assistant chatbot."},
    {"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)

# Create pipeline
pipeline = transformers.pipeline(
    "text-generation",
    model=new_model,
    tokenizer=tokenizer
)

# Generate text
sequences = pipeline(
    prompt,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    num_return_sequences=1,
    max_length=200,
)
print(sequences[0]['generated_text'])

Limitations

The Lucie-Boosted model is a quick demonstration that the Lucie foundation model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanism.

Developed by: Jonathan Pacifico, 2025
Model type: LLM
Language(s) (NLP): French, English
License: Apache-2.0

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	8.22
IFEval (0-Shot)	25.66
BBH (3-Shot)	10.26
MATH Lvl 5 (4-Shot)	0.76
GPQA (0-shot)	2.24
MuSR (0-shot)	3.40
MMLU-PRO (5-shot)	7.00

Downloads last month: 3

Safetensors

Model size

7B params

Tensor type

F16

Model tree for jpacifico/Lucie-Boosted-7B-Instruct

Quantizations

2 models

Dataset used to train jpacifico/Lucie-Boosted-7B-Instruct

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

25.660
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

10.260
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

0.760
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

2.240
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

3.400
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

7.000