| | --- |
| | 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)) |
