akash-dev-ai/EdgePulseAI

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.

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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.

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• 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

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Model Sources

• Base Model: https://huggingface.co/Qwen/Qwen2.5-Coder-14B
• Website: https://EdgePulseAi.com

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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

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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.

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How to Get Started with the Model

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))