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.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
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README.md ADDED
@@ -0,0 +1,134 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ base_model: Qwen/Qwen2.5-Coder-14B
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ license: apache-2.0
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+ tags:
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+ - code
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+ - coding-assistant
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+ - lora
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+ - refactor
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+ - bug-fix
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+ - optimization
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+ - async
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+ - concurrency
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+ - security
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+ - logging
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+ - networking
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+ - transformers
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+ ---
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+
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+ # EdgePulse Coder 14B (LoRA)
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+
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+ **EdgePulse Coder 14B** is a production-grade coding assistant fine-tuned using LoRA on top of **Qwen2.5-Coder-14B**.
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+ It is designed to handle real-world software engineering workflows with high reliability and correctness.
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+
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+ ---
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ EdgePulse Coder 14B focuses on **practical developer tasks**, trained on a large, strictly validated dataset covering:
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+
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+ - Bug fixing
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+ - Code explanation
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+ - Refactoring
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+ - Optimization
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+ - Async & concurrency correction
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+ - Logging & observability
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+ - Security & defensive coding
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+ - Networking & I/O handling
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+ - Multi-file context reasoning
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+ - Test generation and impact analysis
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+
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+ The model is optimized for **IDE usage**, **CLI workflows**, and **Cursor-like streaming environments**.
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+
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+ ---
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+
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+ - **Developed by:** EdgePulseAI
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+ - **Shared by:** EdgePulseAI
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+ - **Model type:** Large Language Model (Code-focused)
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+ - **Language(s):** Python, JavaScript, TypeScript, Bash (primary), general programming concepts
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+ - **License:** Apache-2.0
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+ - **Finetuned from:** Qwen/Qwen2.5-Coder-14B
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+
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+ ---
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+
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+ ## Model Sources
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+
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+ - **Base Model:** https://huggingface.co/Qwen/Qwen2.5-Coder-14B
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+ - **Website:** https://EdgePulseAi.com
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+
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+ ---
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+
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+ ## Uses
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+
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+ ### Direct Use
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+
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+ EdgePulse Coder 14B can be used directly for:
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+
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+ - Code explanation
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+ - Bug fixing
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+ - Refactoring existing code
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+ - Generating tests
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+ - Improving logging and error handling
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+ - Fixing async / concurrency bugs
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+ - Secure coding suggestions
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+ - Network & I/O robustness
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+
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+ ### Downstream Use
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+
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+ - IDE assistants (VS Code / Cursor-style tools)
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+ - CI/CD automation
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+ - Code review bots
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+ - Developer copilots
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+ - Internal engineering tools
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+
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+ ### Out-of-Scope Use
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+
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+ - Medical or legal advice
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+ - Autonomous system control
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+ - High-risk decision making without human review
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+
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+ ---
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+
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+ ## Bias, Risks, and Limitations
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+
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+ - The model may occasionally produce syntactically correct but logically incorrect code.
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+ - Security-sensitive code should always be reviewed by humans.
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+ - Performance depends on correct prompt framing and context size.
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+
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+ ### Recommendations
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+
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+ - Use human review for production deployments.
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+ - Combine with static analysis and testing tools.
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+ - Prefer structured prompts for multi-file tasks.
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+
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+ ---
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+
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+ ## How to Get Started with the Model
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+
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+ ```python
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+ from transformers import AutoTokenizer, AutoModelForCausalLM
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+ from peft import PeftModel
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+
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+ base_model = "Qwen/Qwen2.5-Coder-14B"
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+ adapter_model = "edgepulse-ai/EdgePulse-Coder-14B-LoRA"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(base_model)
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+
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+ model = AutoModelForCausalLM.from_pretrained(
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+ base_model,
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+ device_map="auto",
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+ torch_dtype="auto"
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+ )
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+
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+ model = PeftModel.from_pretrained(model, adapter_model)
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+ model.eval()
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+
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+ prompt = "Fix this bug:\n\ndef add(a,b): return a-b"
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+ inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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+
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+ output = model.generate(**inputs, max_new_tokens=128)
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+ print(tokenizer.decode(output[0], skip_special_tokens=True))
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