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Model: eamiller34/Qwen2.5-Coder-7B-Swift-Lm
Source: Original Platform

README.md

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+---
+license: other
+license_name: qwen-license
+license_link: https://github.com/QwenLM/Qwen/blob/main/LICENSE
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+model_creator: erikmiller
+model_name: Qwen2.5-Coder-7B-Swift-Lm
+tags:
+- swift
+- swift6
+- ios
+- macos
+- code
+- qwen-2.5
+- gguf
+- ollie
+- clawfi
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+## Model Summary
+Qwen2.5-Coder-7B-Swift-Lm is a fine-tuned version of the Qwen2.5-Coder-7B-Instruct model, specifically optimized for high-performance Swift development.
+
+## Key Features
+Modern Swift Proficiency: Deeply familiar with Swift 6 Concurrency (Actors, Sendable) and the Observation framework (@Observable).
+
+Native SDK Knowledge: Fine-tuned to understand the nuances of SwiftUI, AppKit, and Combine.
+
+Local Performance: Optimized for GGUF format to ensure low-latency inference on Apple Silicon (Metal) via tools like LM Studio.
+
+## Training Details
+Dataset: 5,602 high-quality, curated Swift examples.
+
+Fine-tuning Method: QLoRA (Rank=16, Alpha=32).
+
+Target Modules: q_proj, v_proj.
+
+Objective: Improving code generation accuracy and reducing common pitfalls like forced-unwrapping.
+
+## Training Results
+
+Final Training Loss: 0.369
+
+Training Epochs: 1
+
+Loss Curve: The model showed a steady, controlled descent from an initial loss of 12.45 down to 0.36, indicating successful convergence on the Swift-specific dataset without aggressive overfitting.
+
+## Usage Instructions
+LM Studio / GGUF
+Download the .gguf file from the Files and versions tab.
+
+In LM Studio, load the model and enable GPU Offload to Max.
+
+Set your system prompt to: "You are a Senior iOS Architect specializing in clean, safe, and modern Swift code."
+
+Connecting to Cursor
+Start the Local Server in LM Studio (default: http://localhost:1234).
+
+In Cursor Settings, add a custom OpenAI API with the URL http://localhost:1234/v1.
+
+Select this model to get fine-tuned Swift suggestions directly in your project.