| --- |
| language: en |
| license: apache-2.0 |
| library_name: transformers |
| tags: |
| - llm |
| - language-model |
| - open-source |
| - instruction-following |
| widget: |
| - text: "What is machine learning?" |
| example_title: "Explanation" |
| - text: "Write a Python function to reverse a string." |
| example_title: "Code Generation" |
| - text: "Summarize: The rapid advancement of AI has transformed industries worldwide, enabling automation, improved decision-making, and new forms of human-computer interaction." |
| example_title: "Summarization" |
| --- |
| |
| # Cosmic Lightning |
|
|
| **A blazingly fast, open-source language model optimized for efficiency and versatility.** |
|
|
| Cosmic Lightning is a compact yet capable language model designed for efficient inference across a wide range of tasks—from code generation and summarization to reasoning and creative writing. Built with a focus on speed and adaptability, it delivers strong performance without demanding extensive computational resources. |
|
|
| ## Model Details |
|
|
| - **Model Type:** Transformer-based Language Model |
| - **Architecture:** Optimized causal language model |
| - **Parameters:** Compact architecture optimized for edge and cloud inference |
| - **Training Data:** Diverse multilingual and code datasets |
| - **Language(s):** English, with support for multilingual context |
| - **License:** Apache 2.0 |
| - **Developed by:** Cosmic Stack |
| - **Repository:** [Hugging Face Hub](https://huggingface.co/cosmicstack/cosmic-lightning) |
|
|
| ## Intended Use |
|
|
| ### Direct Use |
|
|
| Cosmic Lightning is designed for: |
| - **Text Generation:** Generate coherent, contextually relevant text for various domains |
| - **Code Generation:** Write code snippets, functions, and scripts |
| - **Summarization:** Condense documents, articles, and long-form content |
| - **Question Answering:** Answer factual and analytical questions |
| - **Instruction Following:** Execute tasks from natural language prompts |
|
|
| ### Example Usage |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| import torch |
| |
| # Load the model and tokenizer |
| model_name = "cosmicstack/cosmic-lightning" |
| tokenizer = AutoTokenizer.from_pretrained(model_name) |
| model = AutoModelForCausalLM.from_pretrained( |
| model_name, |
| torch_dtype=torch.float16, |
| device_map="auto", |
| ) |
| |
| # Generate text |
| prompt = "Explain quantum computing in simple terms:" |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| |
| with torch.no_grad(): |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=256, |
| temperature=0.7, |
| top_p=0.9, |
| do_sample=True, |
| ) |
| |
| response = tokenizer.decode(outputs[0], skip_special_tokens=True) |
| print(response) |
| ``` |
|
|
| ### Downstream Use |
|
|
| You can fine-tune Cosmic Lightning for domain-specific tasks: |
|
|
| ```python |
| from transformers import Trainer, TrainingArguments |
| |
| # Requires a prepared tokenized training dataset named train_dataset. |
| training_args = TrainingArguments( |
| output_dir="./cosmic-lightning-finetuned", |
| num_train_epochs=3, |
| per_device_train_batch_size=4, |
| learning_rate=2e-5, |
| save_strategy="epoch", |
| ) |
| |
| trainer = Trainer( |
| model=model, |
| args=training_args, |
| train_dataset=train_dataset, |
| ) |
| |
| trainer.train() |
| ``` |
|
|
| ## Performance |
|
|
| Cosmic Lightning achieves strong results on standard benchmarks: |
| - Efficient inference: Optimized for both latency and throughput |
| - Versatile reasoning: Handles diverse task types with consistent quality |
| - Resource-efficient: Runs on consumer-grade GPUs and CPUs |
|
|
| ## Bias, Risks, and Limitations |
|
|
| ### Known Limitations |
|
|
| - **Knowledge Cutoff:** Training data has a knowledge cutoff; may not reflect recent events or developments |
| - **Factual Accuracy:** While generally reliable, the model can generate plausible-sounding but incorrect information (hallucinations) |
| - **Context Length:** Performance may degrade on very long documents near the token limit |
| - **Domain Specificity:** Best performance on general tasks; domain-specific knowledge may be limited |
|
|
| ### Recommendations |
|
|
| - **Fact-Checking:** Verify factual claims, especially for high-stakes applications |
| - **Prompt Engineering:** Use clear, specific prompts for better results |
| - **Temperature Tuning:** Adjust temperature based on task—lower for factuality, higher for creativity |
| - **Evaluation:** Always evaluate outputs for your specific use case |
|
|
| ### Out-of-Scope Uses |
|
|
| - **Autonomous High-Stakes Decisions:** Do not rely solely on this model for critical decisions (medical, legal, financial) |
| - **Generating Harmful Content:** Not intended for generating hate speech, violence, or illegal content |
| - **Impersonation:** Should not be used to impersonate real individuals or organizations |
|
|
| ## Training Details |
|
|
| ### Training Data |
|
|
| Cosmic Lightning was trained on a diverse corpus including: |
| - High-quality web text and documentation |
| - Code repositories and technical documentation |
| - Multilingual text resources |
|
|
| The model underwent careful curation to ensure quality and diversity while maintaining safety standards. |
|
|
| ### Training Procedure |
|
|
| - **Optimization:** Standard language modeling with next-token prediction |
| - **Distributed Training:** Optimized for efficient large-scale training |
| - **Evaluation:** Continuous evaluation on standard benchmarks during training |
|
|
| ## Hardware Requirements |
|
|
| ### Minimum Requirements |
|
|
| - **GPU Memory:** 8GB VRAM (FP16) for inference; 16GB+ for fine-tuning |
| - **RAM:** 4GB system RAM |
| - **Storage:** ~5-10GB for model weights and dependencies |
|
|
| ### Recommended Setup |
|
|
| - **GPU:** NVIDIA GPU with CUDA support (H100, A100, RTX 4090 for optimal performance) |
| - **Framework:** PyTorch 2.0+ |
| - **Transformers:** Version 4.30+ |
|
|
| ## How to Get Started |
|
|
| ### Installation |
|
|
| ```bash |
| pip install transformers torch accelerate |
| ``` |
|
|
| ### Quick Start |
|
|
| ```python |
| from transformers import pipeline |
| |
| # Use the high-level pipeline API |
| generator = pipeline("text-generation", model="cosmicstack/cosmic-lightning") |
| |
| result = generator( |
| "Once upon a time", |
| max_new_tokens=100, |
| num_return_sequences=1, |
| ) |
| print(result[0]["generated_text"]) |
| ``` |
|
|
| ### Using with Ollama (Local Inference) |
|
|
| ```bash |
| ollama pull cosmicstack/cosmic-lightning |
| ollama run cosmicstack/cosmic-lightning "Explain photosynthesis" |
| ``` |
|
|
| ## Citation |
|
|
| If you use Cosmic Lightning in your work, please cite: |
|
|
| ```bibtex |
| @misc{cosmic-lightning-2026, |
| title={Cosmic Lightning: A Compact Language Model for Efficient Inference}, |
| author={Cosmic Stack}, |
| year={2026}, |
| publisher={Hugging Face} |
| } |
| ``` |
|
|
| ## Environmental Impact |
|
|
| Training this model required significant computational resources. For details on environmental impact and carbon emissions, see our [environmental impact report](https://cosmicstack.ai/sustainability). |
|
|
| ## More Information |
|
|
| - **GitHub:** [Cosmic Stack GitHub](https://github.com/cosmicstack-labs) |
| - **Documentation:** [Full Documentation](https://cosmicstack.ai) |
|
|
|
|
| ## Model Card Contact |
|
|
| For questions, feedback, or improvements to this model card: |
| - **Email:** hello@cosmicstack.ai |
| - **Issues:** [GitHub Issues](https://github.com/cosmicstack-labs) |
|
|
| --- |
|
|
| **Version:** 1.0 |
| **Last Updated:** October 2026 |
| **License:** Apache 2.0 |
|
|
| ```` |
| |
