--- library_name: transformers license: apache-2.0 datasets: - HoangHa/Pensez-v0.1 language: - en - fr base_model: - Qwen/Qwen2.5-7B-Instruct ---
# Pensez: Less Data, Better Reasoning – Rethinking French LLM [**About**](#about) | [**How to Run Locally**](#run-locally) | [**Models and Datasets**](#models-and-datasets) | [**Benchmarks**](#benchmarks) | [**Training Details**](#training-details) ![image/png](https://cdn-uploads.huggingface.co/production/uploads/630a5ef0e81e1dea2cedcec0/lbFwSuyLkixvcLWcMs7ZV.png)
## About Pensez is a bilingual (French-English) reasoning model designed to maximize efficiency with significantly reduced training data. The model leverages a curated dataset focusing on daily reasoning tasks and scientific questions to enhance performance. Key strategies for improved reasoning: - **Concise reasoning** for simple tasks to prevent overthinking. - **Extended reasoning** for complex domains like mathematics, coding, and science. - **Special tokens (`...`)** to explicitly guide the model’s reasoning process. These optimizations result in superior reasoning capabilities while maintaining robust general understanding compared to models like [DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co./deepseek-ai/DeepSeek-R1-Distill-Qwen-7B). ## Models and Datasets ### Model Versions Pensez is built upon [Qwen 2.5 Instruct 7B](https://huggingface.co./Qwen/Qwen2.5-7B-Instruct) and trained over five epochs. | Model | Backbone | Size | Download Link | |---------------|----------------------------------------|------|---------------| | Pensez-v0.1-e1 | Qwen2.5-7B-Instruct | 7B | [🤗 Pensez-v0.1-e1](https://huggingface.co./HoangHa/Pensez-v0.1-e1) | | Pensez-v0.1-e2 | Qwen2.5-7B-Instruct | 7B | [🤗 Pensez-v0.1-e2](https://huggingface.co./HoangHa/Pensez-v0.1-e2) | | Pensez-v0.1-e3 | Qwen2.5-7B-Instruct | 7B | [🤗 Pensez-v0.1-e3](https://huggingface.co./HoangHa/Pensez-v0.1-e3) | | Pensez-v0.1-e4 | Qwen2.5-7B-Instruct | 7B | [🤗 Pensez-v0.1-e4](https://huggingface.co./HoangHa/Pensez-v0.1-e4) | | Pensez-v0.1-e5 | Qwen2.5-7B-Instruct | 7B | [🤗 Pensez-v0.1-e5](https://huggingface.co./HoangHa/Pensez-v0.1-e5) | ### Dataset Pensez was trained on the hand-curated [Pensez v0.1](https://huggingface.co./datasets/HoangHa/Pensez-v0.1) dataset containing 2,000 samples (1,000 French, 1,000 English). | Dataset | Description | Size | Link | |--------------|----------------------|-------|-------| | Pensez v0.1 | SFT Training Dataset | 2K samples | [🤗 Pensez v0.1](https://huggingface.co./datasets/HoangHa/Pensez-v0.1) | ## Benchmarks Pensez was evaluated on French-specific benchmarks, demonstrating strong reasoning ability and improved task-specific performance: | Benchmark | Pensez-v0.1-e5 | DeepSeek-R1-Distill-Qwen-7B | Qwen2.5-7B-Instruct | |-----------|---------------|-----------------------------|----------------------| | Math-hard (fr) | 0.3458 | 0.3403 | 0.2253 | | MMLU (fr) | 0.5766 | 0.4961 | 0.6612 | | BoolQA (fr) | 0.9157 | 0.7079 | 0.9382 | | Trivia (en) | 0.4421 | 0.2711 | 0.5316 | | HellaSwag (en) | 0.5050 | 0.3540 | 0.5258 | **Key Observations:** - Pensez outperforms Qwen2.5-7B-Instruct in reasoning tasks. - Comparable to DeepSeek-R1-Distill-Qwen-7B in reasoning while maintaining strong understanding. - Reduced degradation in knowledge-based tasks.
Click for detailed benchmark results | Tasks | Pensez v0.1 e1 | Pensez v0.1 e2 | Pensez v0.1 e3 | Pensez v0.1 e4 | Pensez v0.1 e5 | Qwen 7B instruct | R1 distil | |------------------------------------------------|---------------|---------------|---------------|---------------|---------------|-----------------|-----------| | leaderboard_math_hard_fr | 0.0918 | 0.2547 | 0.2783 | 0.3035 | 0.3458 | 0.2253 | 0.3403 | | leaderboard_math_algebra_hard_fr | 0.1029 | 0.3914 | 0.3971 | 0.5114 | 0.5000 | 0.4229 | 0.4771 | | leaderboard_math_counting_and_prob_hard_fr | 0.0765 | 0.1378 | 0.1939 | 0.2041 | 0.2398 | 0.1224 | 0.2347 | | leaderboard_math_geometry_hard_fr | 0.0388 | 0.1019 | 0.1408 | 0.1359 | 0.1748 | 0.1019 | 0.2330 | | leaderboard_math_num_theory_hard_fr | 0.1198 | 0.2581 | 0.3502 | 0.3548 | 0.4332 | 0.3180 | 0.3963 | | leaderboard_math_prealgebra_hard_fr | 0.1681 | 0.4425 | 0.4690 | 0.4956 | 0.5841 | 0.3274 | 0.4867 | | leaderboard_math_precalculus_hard_fr | 0.0357 | 0.0714 | 0.1190 | 0.1190 | 0.1429 | 0.0595 | 0.2143 | | leaderboard_mmlu_fr | 0.3806 | 0.3329 | - | - | 0.5766 | 0.6612 | 0.4961 | | french_bench_arc_challenge | 0.5047 | 0.5021 | 0.4919 | 0.4859 | 0.4842 | 0.5518 | 0.3447 | | french_bench_boolqa | 0.9326 | 0.9326 | 0.9326 | 0.9270 | 0.9157 | 0.9382 | 0.7079 | | french_bench_fquadv2 | 0.4325 | 0.4400 | 0.4412 | 0.4375 | 0.4387 | 0.4800 | 0.2988 | | french_bench_hellaswag | 0.4970 | 0.5055 | 0.5092 | 0.5058 | 0.5050 | 0.5258 | 0.3540 | | french_bench_trivia | 0.4763 | 0.4763 | 0.4553 | 0.4395 | 0.4421 | 0.5316 | 0.2711 |
## Run Locally You can run Pensez using Hugging Face’s `transformers` library: ```python import torch from transformers import AutoTokenizer, AutoModelForCausalLM model_path = "HoangHa/Pensez-v0.1-e5" # Load tokenizer and model tokenizer = AutoTokenizer.from_pretrained(model_path) model = AutoModelForCausalLM.from_pretrained( model_path, torch_dtype=torch.float16, device_map="auto" ) # Example input messages = [{"role": "user", "content": "Bonjour!"}] input_ids = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to("cuda") generated_ids = model.generate(input_ids, max_new_tokens=2500, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(generated_ids[0], skip_special_tokens=True, clean_up_tokenization_space=True) print(f"Réponse: {response}") ``` ## Training Details Pensez was trained with: - **Packing Inputs Without Cross-Contamination Attention** ([Reference](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing)) - **Liger Kernel** ([Reference](https://github.com/linkedin/Liger-Kernel)) - **DeepSpeed 3** ([Reference](https://github.com/deepspeedai/DeepSpeed)) - **NEFTune Noise** ([Reference](https://arxiv.org/abs/2310.05914)) for robustness. | **Parameter** | **Value** | |--------------|----------| | Epochs | 5 | | Global Batch Size | 200 | | Learning Rate | 1e-5 | | Scheduler | Cosine | | Optimizer | AdamW | | Warmup Ratio | 0.05 | | Weight Decay | 0.01 | | Max Sequence Length | 16,384 | More details: [Training Config](https://huggingface.co./HoangHa/Pensez-v0.1-e5/blob/main/fr_full_sft.yaml) | Loss curves: [Wandb](https://wandb.ai/hahuyhoanghhh41/llamafactory?nw=nwuserhahuyhoanghhh41) ## Citation ```bibtex @misc{ha2025pensezreasoningfrenchllm, title={Pensez: Less Data, Better Reasoning – Rethinking French LLM}, author={Ha Huy Hoang}, year={2025}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={}, } ``` ## Acknowledgement - [llama-factory](https://github.com/hiyouga/LLaMA-Factory) - [Deepseek R1](https://github.com/deepseek-ai/DeepSeek-R1) - [Qwen 2.5](https://github.com/QwenLM/Qwen2.5) - [NEFTune Noise](https://arxiv.org/abs/2310.05914) - [Packing Inputs Without Cross-Contamination Attention](https://github.com/MeetKai/functionary/tree/main/functionary/train/packing) - [Liger Kernel](https://github.com/linkedin/Liger-Kernel) - [Deepspeed](https://github.com/deepspeedai/DeepSpeed) - [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) - [Hyperbolic](https://hyperbolic.xyz/) - [Modal](https://modal.com/)