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 (<think>...</think>) 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.

Models and Datasets

Model Versions

Pensez is built upon Qwen 2.5 Instruct 7B and trained over five epochs.

Model	Backbone	Size	Download Link
Pensez-v0.1-e1	Qwen2.5-7B-Instruct	7B	🤗 Pensez-v0.1-e1
Pensez-v0.1-e2	Qwen2.5-7B-Instruct	7B	🤗 Pensez-v0.1-e2
Pensez-v0.1-e3	Qwen2.5-7B-Instruct	7B	🤗 Pensez-v0.1-e3
Pensez-v0.1-e4	Qwen2.5-7B-Instruct	7B	🤗 Pensez-v0.1-e4
Pensez-v0.1-e5	Qwen2.5-7B-Instruct	7B	🤗 Pensez-v0.1-e5

Dataset

Pensez was trained on the hand-curated 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

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)
Liger Kernel (Reference)
DeepSpeed 3 (Reference)
NEFTune Noise (Reference) 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 | Loss curves: Wandb

Citation

bibtex

@misc{dao2025alphamazeenhancinglargelanguage,
      title={Pensez: Less Data, Better Reasoning – Rethinking French LLM},
      author={Ha Huy Hoang},
      year={2025},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={},
}

HoangHa/Pensez-v0.1-e4

Pensez: Less Data, Better Reasoning – Rethinking French LLM

About