HoangHa
/

Pensez-v0.1-e1

@@ -1,199 +1,164 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
 ## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- HoangHa/Pensez-v0.1
+language:
+- en
+- fr
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
 ---
+<div align="center">
+# 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)
+</div>
+## 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 (`<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](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.
+<details>
+<summary>Click for detailed benchmark results</summary>
+| 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    |
+</details>
+## 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]() | Loss curves: [Wandb](https://wandb.ai/hahuyhoanghhh41/llamafactory?nw=nwuserhahuyhoanghhh41)
+## 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={},
+}
+```
+## 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/)