---
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))