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))

akash-dev-ai/EdgePulseAI

EdgePulse Coder 14B (LoRA)