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---
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)) |