Update README.md

README.md

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 ---
-license: mit
----
+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_length=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
+
+                                                                                      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
+                                                                                                                      ```
+                                                                                                                      
+                                                                                                                      ### 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_length=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)
+                                                                                                                                          - **Documentation:** [Full Documentation](https://docs.cosmicstack.ai)
+                                                                                                                                          - **Community:** Join our [Discord community](https://discord.gg/cosmicstack)
+                                                                                                                                          
+                                                                                                                                          ## Model Card Contact
+                                                                                                                                          
+                                                                                                                                          For questions, feedback, or improvements to this model card:
+                                                                                                                                          - **Email:** models@cosmicstack.ai
+                                                                                                                                          - **Issues:** [GitHub Issues](https://github.com/cosmicstack/cosmic-lightning/issues)
+                                                                                                                                          
+                                                                                                                                          ---
+                                                                                                                                          
+                                                                                                                                          **Version:** 1.0
+                                                                                                                                          **Last Updated:** October 2026
+                                                                                                                                          **License:** Apache 2.0
+                                                                                                                      )
+                                                                                                          )
+                                                                                      )
+                                          )
+                          )