| --- |
| base_model: Qwen/Qwen2.5-Coder-14B |
| library_name: peft |
| pipeline_tag: text-generation |
| license: apache-2.0 |
| tags: |
| - code |
| - coding-assistant |
| - lora |
| - refactor |
| - bug-fix |
| - optimization |
| - async |
| - concurrency |
| - security |
| - logging |
| - networking |
| - transformers |
| --- |
| |
| # EdgePulse Coder 14B (LoRA) |
|
|
| **EdgePulse Coder 14B** is a production-grade coding assistant fine-tuned using LoRA on top of **Qwen2.5-Coder-14B**. |
| It is designed to handle real-world software engineering workflows with high reliability and correctness. |
|
|
| --- |
|
|
| ## Model Details |
|
|
| ### Model Description |
|
|
| EdgePulse Coder 14B focuses on **practical developer tasks**, trained on a large, strictly validated dataset covering: |
|
|
| - Bug fixing |
| - Code explanation |
| - Refactoring |
| - Optimization |
| - Async & concurrency correction |
| - Logging & observability |
| - Security & defensive coding |
| - Networking & I/O handling |
| - Multi-file context reasoning |
| - Test generation and impact analysis |
|
|
| The model is optimized for **IDE usage**, **CLI workflows**, and **Cursor-like streaming environments**. |
|
|
| --- |
|
|
| - **Developed by:** EdgePulseAI |
| - **Shared by:** EdgePulseAI |
| - **Model type:** Large Language Model (Code-focused) |
| - **Language(s):** Python, JavaScript, TypeScript, Bash (primary), general programming concepts |
| - **License:** Apache-2.0 |
| - **Finetuned from:** Qwen/Qwen2.5-Coder-14B |
|
|
| --- |
|
|
| ## Model Sources |
|
|
| - **Base Model:** https://huggingface.co/Qwen/Qwen2.5-Coder-14B |
| - **Website:** https://EdgePulseAi.com |
|
|
| --- |
|
|
| ## Uses |
|
|
| ### Direct Use |
|
|
| EdgePulse Coder 14B can be used directly for: |
|
|
| - Code explanation |
| - Bug fixing |
| - Refactoring existing code |
| - Generating tests |
| - Improving logging and error handling |
| - Fixing async / concurrency bugs |
| - Secure coding suggestions |
| - Network & I/O robustness |
|
|
| ### Downstream Use |
|
|
| - IDE assistants (VS Code / Cursor-style tools) |
| - CI/CD automation |
| - Code review bots |
| - Developer copilots |
| - Internal engineering tools |
|
|
| ### Out-of-Scope Use |
|
|
| - Medical or legal advice |
| - Autonomous system control |
| - High-risk decision making without human review |
|
|
| --- |
|
|
| ## Bias, Risks, and Limitations |
|
|
| - The model may occasionally produce syntactically correct but logically incorrect code. |
| - Security-sensitive code should always be reviewed by humans. |
| - Performance depends on correct prompt framing and context size. |
|
|
| ### Recommendations |
|
|
| - Use human review for production deployments. |
| - Combine with static analysis and testing tools. |
| - Prefer structured prompts for multi-file tasks. |
|
|
| --- |
|
|
| ## How to Get Started with the Model |
|
|
| ```python |
| from transformers import AutoTokenizer, AutoModelForCausalLM |
| from peft import PeftModel |
| |
| base_model = "Qwen/Qwen2.5-Coder-14B" |
| adapter_model = "edgepulse-ai/EdgePulse-Coder-14B-LoRA" |
| |
| tokenizer = AutoTokenizer.from_pretrained(base_model) |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| base_model, |
| device_map="auto", |
| torch_dtype="auto" |
| ) |
| |
| model = PeftModel.from_pretrained(model, adapter_model) |
| model.eval() |
| |
| prompt = "Fix this bug:\n\ndef add(a,b): return a-b" |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| |
| output = model.generate(**inputs, max_new_tokens=128) |
| print(tokenizer.decode(output[0], skip_special_tokens=True)) |
