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----
-base_model: unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:unsloth/qwen2.5-coder-14b-instruct-bnb-4bit
-- lora
-- sft
-- transformers
-- trl
-- unsloth
----
-
-# Model Card for Model ID
+# Qwen2.5-Coder-14B-Frontend-UI-Architect
 
-<!-- Provide a quick summary of what the model is/does. -->
+A finetuned variant of **Qwen2.5-Coder-14B-Instruct**, specialized for **frontend engineering**, with a strong focus on **React + TypeScript** and **Tailwind CSS**.
 
+The goal of this model is **not** to be a frontier / SOTA LLM, but to be a **practical, fast and reliable workhorse** you can run on **consumer hardware** (via GGUF) while getting UI code quality close to what you would normally expect from paid API models — all fully local and with effectively unlimited usage.
 
+---
 
-## Model Details
-
-### Model Description
+## Why This Model
 
-<!-- Provide a longer summary of what this model is. -->
+Instead of chasing leaderboard scores, this finetune is designed around a few pragmatic goals:
 
+- **Run comfortably on a single consumer GPU or small server** using GGUF quantizations.
+- **Behave like a seasoned frontend engineer** for UI-focused tasks.
+- **Prioritize latency and iteration speed** for rapid UI/UX prototyping.
+- **Produce full, shippable React + TypeScript components**, not just snippets.
+- **Integrate naturally with Tailwind CSS (v4-style utilities)** and modern layout patterns.
 
+If you’re building dashboards, internal tools, marketing pages or app shells and want a local model that “just writes solid UI code” at high speed, this is what this finetune is trying to solve.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+---
 
-### Model Sources [optional]
+## Model Choice: Why Qwen2.5-Coder and Not Qwen 3.0-Instruct?
 
-<!-- Provide the basic links for the model. -->
+I experimented with **Qwen 3.0 Instruct** as a base model and actually **finetuned both** Qwen 3.0 and Qwen2.5-Coder on similar frontend/UI datasets.
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+In practice:
 
-## Uses
+- During training, **Qwen2.5-Coder** showed **cleaner, more stable behavior** on UI-heavy examples (React + TS + Tailwind).
+- On downstream tests (building real pages, dashboards and forms), **Qwen2.5-Coder consistently produced better structured UI code**:
+  - Fewer hallucinated patterns,
+  - More realistic component boundaries,
+  - Better alignment with TypeScript + Tailwind usage.
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+Even though Qwen 3.0 is the newer family, for this **very specific niche (frontend UI codegen)**, the **coder-oriented 2.5 base** simply gave better practical results after finetuning. That’s why this release is based on **Qwen2.5-Coder-14B-Instruct** instead of Qwen 3.0.
 
-### Direct Use
+---
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+## Overview
 
-[More Information Needed]
+**Qwen2.5-Coder-14B-Frontend-UI-Architect** is tuned for high-quality code generation across:
 
-### Downstream Use [optional]
+- **React** (Vite/CRA/Next-style patterns adapted to vanilla React)
+- **TypeScript** (strict-oriented, explicit typing where it adds clarity)
+- **Tailwind CSS v4** (atomic-first, utility-driven styling)
+- **Component architecture & layout composition**
+- **State management** (Zustand, Context API, basic Jotai patterns)
+- **UI/UX best practices** (visual hierarchy, spacing, responsiveness, basic accessibility)
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+It is particularly good at acting as a **UI prototyping companion**: give it a clear description of the page, and it will return a realistic, ready-to-tweak implementation.
 
-[More Information Needed]
+---
 
-### Out-of-Scope Use
+## What It Does Well
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+### Frontend / UI Generation
 
-[More Information Needed]
+- Full **React + TypeScript** component files, ready to drop into a project.
+- **Layouts, dashboards, forms, modals, settings pages**, landing pages.
+- Tailwind-style utility usage with attention to **spacing, typography and alignment**.
+- Sensible **component decomposition** (breaking larger pages into subcomponents when asked).
+- **Responsive behavior** using common breakpoint patterns (e.g. `sm`, `md`, `lg`, `xl`).
 
-## Bias, Risks, and Limitations
+### Architecture & Reasoning
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+- Suggests **file/folder structures** for scalable UI projects.
+- Proposes **design system primitives** (buttons, cards, inputs, layout shells, etc.).
+- Can refactor an existing UI into **cleaner, more composable components**.
+- Offers **naming, props design and variant ideas** for design-system-level components.
 
-[More Information Needed]
+### Practical Speed & Local Usage
 
-### Recommendations
+Running via **GGUF** in tools like LM Studio, it is tuned to be **fast enough for interactive use**:
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+- Short prompts return full pages in a single generation.
+- Great for “**one screen at a time**” workflows.
+- Well-suited as a **local UI copilot** for iterative front-end work.
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+---
 
-## How to Get Started with the Model
+## Limitations & Recommended Usage Patterns
 
-Use the code below to get started with the model.
+This model sits on top of the Qwen2.5 14B architecture and inherits its **context and scaling limitations**. A few practical notes:
 
-[More Information Needed]
+### 1. Best for *building* interfaces, not scanning huge codebases
 
-## Training Details
+- Works best when you ask it to **create or refactor a single page / screen at a time**.
+- It is **not intended as a repo-wide code-understanding model** for massive monorepos.
+- For large refactors, feed it **just the relevant components** and a **clear description** of the target state.
 
-### Training Data
+### 2. Use a “token system” for multi-page apps
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+For complex products, a good pattern is:
 
-[More Information Needed]
+1. Define a small **token vocabulary** in your docs or prompt, e.g.:  
+   - `[DASHBOARD]` – main analytics view  
+   - `[SETTINGS_ACCOUNT]` – account settings page  
+   - `[SETTINGS_BILLING]` – billing page  
+   - `[ADMIN_USERS]` – user management view  
 
-### Training Procedure
+2. When prompting the model, **remind it which token you’re working on** and provide a short spec, for example:
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+   > “Generate the React + TS page for [SETTINGS_BILLING]: …”
 
-#### Preprocessing [optional]
+3. Keep each generation centered on **one token/page at a time**, and stitch things together in your codebase.
 
-[More Information Needed]
+This keeps the context clean and helps the model stay **consistent and focused**, while still supporting high-quality, reusable UIs.
 
+### 3. Refactoring vs. long-context editing
 
-#### Training Hyperparameters
+- You *can* paste existing components and ask for **refactors, cleanup, or modernized layout**.
+- For very long files, consider:
+  - Sending only the parts you actually want to change.
+  - Asking for **stepwise changes** (e.g., first refactor layout, then add accessibility tweaks).
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### 4. General model limitations
 
-#### Speeds, Sizes, Times [optional]
+- Not a frontier model; for very complex multi-step reasoning or huge refactors, **SOTA hosted models will still be stronger**.
+- Can occasionally:
+  - Over-abstract components when you over-emphasize “architecture”.
+  - Invent imports or hooks if you’re not explicit about your tech stack.
+- Works best when the prompt is **concrete about libraries and constraints** (React version, routing, state management, etc.).
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+If you run into consistent failure patterns, I would genuinely appreciate detailed reports (see **Feedback** below).
 
-[More Information Needed]
+---
 
-## Evaluation
+## Training Details
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+- **Base model:** `Qwen/Qwen2.5-Coder-14B-Instruct`  
+- **Finetuning method:** QLoRA (via Unsloth)  
+- **Trainable parameters:** ~68.8M (≈0.46%)  
+- **Epochs:** 2  
+- **Training examples:** 12,805  
+- **Effective batch size:** 16  
+- **Hardware:** A100-40GB (Unsloth-accelerated pipeline)
 
-### Testing Data, Factors & Metrics
+The dataset is heavily biased toward:
 
-#### Testing Data
+- **Full React + TS component files** (not fragments).
+- **Tailwind-based layouts**.
+- Clear, real-world-style UI specs (dashboards, settings, CRUD, internal tools, etc.).
 
-<!-- This should link to a Dataset Card if possible. -->
+### Loss Curve (Summary)
 
-[More Information Needed]
+This is a simple snapshot of the training loss:
 
-#### Factors
+| Step | Loss |
+|------|------|
+| 20   | 1.24 |
+| 100  | 0.35 |
+| 500  | 0.29 |
+| 1000 | 0.25 |
+| Final | **0.26** |
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+Loss converged smoothly with no visible instability. A small held-out set of UI-oriented instructions showed stable behavior (sensible structure, no collapse). The main focus was **practical behavior** over chasing the lowest possible loss.
 
-[More Information Needed]
+---
 
-#### Metrics
+## Files & Formats
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+### HuggingFace (safetensors)
 
-[More Information Needed]
+Typical HF layout:
 
-### Results
+- `config.json`  
+- `tokenizer.json`  
+- `tokenizer_config.json`  
+- `vocab.json`  
+- `merges.txt`  
+- `model-00001-of-00006.safetensors` … `model-00006-of-00006.safetensors`  
+- `model.safetensors.index.json`  
 
-[More Information Needed]
+### GGUF (for llama.cpp, LM Studio, etc.)
 
-#### Summary
+Converted via a Qwen2-compatible fork of `convert_hf_to_gguf.py` from `llama.cpp`.
 
+Available variants:
 
+- `qwen2.5-coder-14b-frontend-ui-architect-q4_K_M.gguf`
+- `qwen2.5-coder-14b-frontend-ui-architect-q5_K_M.gguf`
+- `qwen2.5-coder-14b-frontend-ui-architect-q8_0.gguf`
+- `qwen2.5-coder-14b-frontend-ui-architect-f16.gguf`
 
-## Model Examination [optional]
+Pick the quantization that best fits your hardware and latency needs. For most consumer GPUs and recent CPUs, **q4_K_M** is a good starting point.
 
-<!-- Relevant interpretability work for the model goes here -->
+---
 
-[More Information Needed]
+## Usage
+
+### Python (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "codavidgarcia/qwen2.5-coder-14b-frontend-ui-architect"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype=torch.float16,
+    device_map="auto"
+)
+
+prompt = (
+    "You are a senior frontend engineer.
+
+"
+    "Build a clean, production-ready React + TypeScript page using Tailwind CSS.
+"
+    "Requirements:
+"
+    "- Dashboard layout with sidebar and top bar
+"
+    "- Cards for key metrics
+"
+    "- Responsive behavior for mobile and desktop
+"
+    "- Return a full file, no pseudo-code.
+"
+)
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=800,
+    do_sample=True,
+    temperature=0.3,
+    top_p=0.9
+)
+
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### LM Studio (GGUF)
+
+1. Download one of the `*.gguf` files.  
+2. In **LM Studio**: go to **Local Models → Add model** and load the GGUF file.  
+3. Suggested configuration:
+   - **Context length:** 8192
+   - **Temperature:** 0.2–0.4
+   - **Repetition penalty:** 1.05–1.15
+
+4. Use a focused system / instruction prompt, for example:
+
+```text
+You are a senior frontend engineer.
+
+Always:
+- Produce full, production-ready React + TypeScript components.
+- Use Tailwind CSS utility classes (v4-style).
+- Prefer clear layout hierarchy, good spacing and readable typography.
+- Avoid pseudo-code. Return concrete code that can be pasted into a project.
+```
+
+Then provide your page or component requirements in the user message.
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## Recommended Prompting Patterns
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+A few patterns that tend to work well in practice:
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+### 1. React Page / Screen
 
-## Technical Specifications [optional]
+```text
+You are a senior frontend engineer.
 
-### Model Architecture and Objective
+Build a clean, production-ready React + TypeScript page:
+- Use Tailwind CSS for all styling.
+- Return a single full `.tsx` file.
+- Include a default export component.
+- No comments, no placeholder lorem ipsum – use realistic labels.
+```
 
-[More Information Needed]
+### 2. Component Library / Design System Primitives
 
-### Compute Infrastructure
+```text
+Act as a frontend architect.
 
-[More Information Needed]
+Design a small component system for a product dashboard:
+- Button, Card, PageShell, Sidebar, TopBar
+- Show props and variants for each component
+- Use React + TypeScript + Tailwind
+- Suggest a folder structure for these components
+```
 
-#### Hardware
+### 3. Refactor an Existing Component
 
-[More Information Needed]
+```text
+You are a senior frontend engineer.
 
-#### Software
+Refactor the following React + TypeScript component:
+- Improve the layout and spacing using Tailwind.
+- Keep the existing behavior.
+- Extract repeated pieces into smaller components if it helps readability.
 
-[More Information Needed]
+[PASTE COMPONENT HERE]
+```
 
-## Citation [optional]
+Feel free to adapt these patterns to your own stack and conventions.
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+---
 
-**BibTeX:**
+## Feedback & Improvements
 
-[More Information Needed]
+This is a relatively small, targeted finetune, and I’m quite happy with how much UI behavior it managed to internalize given the scale. That said, **I absolutely welcome feedback**.
 
-**APA:**
+If you:
 
-[More Information Needed]
+- Notice systematic mistakes,
+- See opportunities for better architectural defaults,
+- Or have specific failure cases (prompt + input + expected vs actual output),
 
-## Glossary [optional]
+please open an **Issue or Discussion** on the HuggingFace repo. Concrete examples are extremely valuable for future finetuning rounds and for improving the prompt recommendations.
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+---
 
-[More Information Needed]
+## License
 
-## More Information [optional]
+- **Base model:** Qwen/Qwen2.5-Coder-14B-Instruct — follows the original Qwen license.
+- **Finetuning code, configuration, and additional assets:** released under the **MIT License**.
+- **Finetuned weights:** inherit any obligations from the base model’s license. Please refer to Qwen’s official license terms for usage in commercial or sensitive contexts.
 
-[More Information Needed]
+Attribution to the original finetune author (`codavidgarcia`) is appreciated but not strictly required beyond what the underlying licenses demand.
 
-## Model Card Authors [optional]
+---
 
-[More Information Needed]
+## Contact
 
-## Model Card Contact
+For questions, feedback, or suggestions:
 
-[More Information Needed]
-### Framework versions
+- Open an **Issue** or **Discussion** on the HuggingFace model page.
 
-- PEFT 0.18.0
+If you end up using this model in your own tools, dashboards, or UI builders, I’d genuinely love to hear how it performs for you and what could be improved in the next iteration.