--- license: apache-2.0 model-index: - name: BrainTransformers-3B-Chat results: - task: type: text-generation dataset: name: mmlu type: mmlu metrics: - name: MMLU type: MMLU value: 63.2 - task: type: text-generation dataset: name: bbh type: bbh metrics: - name: BBH type: BBH value: 54.1 - task: type: text-generation dataset: name: arc-challenge type: arc-challenge metrics: - name: ARC-C type: ARC-C value: 54.3 - task: type: text-generation dataset: name: hellaswag type: hellaswag metrics: - name: HellaSwag type: HellaSwag value: 72.8 - task: type: text-generation dataset: name: gsm8k type: gsm8k metrics: - name: GSM8K type: GSM8K value: 76.3 - task: type: code-generation dataset: name: humaneval type: humaneval metrics: - name: HumanEval type: HumanEval value: 40.5 source: name: LumenScopeAI url: https://github.com/LumenScopeAI/BrainTransformers-SNN-LLM --- # BrainTransformers: SNN-LLM Based on BrainTransformers, BrainGPTForCausalLM is a Large Language Model (LLM) implemented using Spiking Neural Networks (SNN). We are excited to announce that an initial version of our technical report is now available in our GitHub repository. This early release allows the community to access our findings while the full report undergoes the arXiv review process. Our comprehensive technical report has been submitted to arXiv and is currently in the "on hold" status, pending review. We will be releasing our findings in stages, with updates and more detailed analyses to follow. The full report will be available on arXiv as soon as the review process is completed. We plan to further optimize the model at the operator level and adapt it for hardware compatibility, enabling BrainGPTForCausalLM to be deployed on more energy-efficient SNN hardware devices. The current open-source version retains some floating-point calculations to ensure computational efficiency. We will continue to optimize this. Some detailed explanations are provided in the comments within the source code. Stay tuned for updates as we continue to refine and expand our research findings. You can try it online at [www.lumenscopeai.com](http://www.lumenscopeai.com/). ## Model Availability - The current pre-trained model parameters have been published on Hugging Face.[LumenscopeAI/BrainTransformers-3B-Chat](https://huggingface.co/LumenscopeAI/BrainTransformers-3B-Chat) ## Repository The github link is: [LumenScopeAI/BrainTransformers-SNN-LLM](https://github.com/LumenScopeAI/BrainTransformers-SNN-LLM) ## Model Performance Below are the performance metrics of our 3B model on various benchmarks: ### General Tasks | Dataset | Performance | |---------|-------------| | MMLU | 63.2 | | MMLU-pro | 33.3 | | MMLU-redux | 61.3 | | BBH | 54.1 | | ARC-C | 54.3 | | Trurhfulqa | 47.1 | | Winogrande | 68.8 | | Hellaswag | 72.8 | ### Math and Science Tasks | Dataset | Performance | |---------|-------------| | GPQA | 25.3 | | Theoremqa | 26.4 | | MATH | 41.0 | | MMLU-stem | 60.2 | | GSM8K | 76.3 | ### Coding and Multilingual Tasks | Dataset | Performance | |---------|-------------| | HumanEval | 40.5 | | HumanEval+ | 34.6 | | MBPP | 55.0 | | MBPP+ | 47.5 | | MultiPL-E | 39.6 | | Multi-Exam | 52.6 | | Multi-Understanding | 73.9 | | Multi-Mathematics | 47.1 | | Multi-Translation | 28.2 | ## Usage ### Generate Text ```python import torch from transformers import AutoTokenizer, BrainGPTForCausalLM model_path = "/path/to/your/model" model = BrainGPTForCausalLM.from_pretrained(model_path) tokenizer = AutoTokenizer.from_pretrained(model_path) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) def generate_text(messages, max_new_tokens=50): text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) model_inputs = tokenizer([text], return_tensors="pt").to(device) with torch.no_grad(): generated_ids = model.generate(**model_inputs, max_new_tokens=max_new_tokens) generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)] return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] # Example usage messages = [ {"role": "system", "content": "You are a knowledgeable assistant."}, {"role": "user", "content": "Explain the Pythagorean theorem."} ] response = generate_text(messages) print(response) ``` ## Acknowledgments The model was trained using ANN-Base-Qwen2, with a total of three training stages, including SNN-specific neuron synaptic plasticity training. The technical report is still being prepared. Please note that SNN models do not support ANN fine-tuning techniques. We are currently developing specialized fine-tuning code tools for SNN models. Our open-source model has achieved leading SOTA results, and we welcome your stars. This repository includes a complete transformers package, which can directly replace the transformers package in your development environment. This allows compatibility with our SNN-Base-LLM without affecting existing usage.