Instructions to use XeroCodes/lynx-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use XeroCodes/lynx-8b with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/meta-llama-3.1-8b-bnb-4bit") model = PeftModel.from_pretrained(base_model, "XeroCodes/lynx-8b") - Notebooks
- Google Colab
- Kaggle
| base_model: meta-llama/Meta-Llama-3.1-8B-Instruct | |
| datasets: | |
| - sahil2801/CodeAlpaca-20k | |
| language: | |
| - en | |
| library_name: peft | |
| license: apache-2.0 | |
| pipeline_tag: text-generation | |
| ## Lynx-8B | |
| Lynx-8B is a fine-tuned language model based on Meta's LLaMA-3.1-8B, specifically optimized to be an exceptional coding assistant. This model has been fine-tuned using the CodeAlpaca-20k dataset, which enhances its capability to understand and generate code across various programming languages and scenarios. | |
| ## Overview | |
| Lynx-8B leverages the robust foundation of the Meta-Llama-3.1-8B-Instruct model and fine-tunes it with the comprehensive CodeAlpaca-20k dataset. This results in a model that excels in coding tasks, offering precise and context-aware code suggestions, completions, and explanations. | |
| ## Features | |
| - Code Generation: Generate syntactically correct and efficient code snippets. | |
| - Code Completion: Provide context-aware code completions to accelerate your development process. | |
| - Code Explanation: Offer explanations for code snippets, helping you understand complex logic. | |
| - Multi-Language Support: Supports a wide range of programming languages including Python, JavaScript, Java, C++, and more. |