--- license: apache-2.0 language: - en tags: - flutter - dart - code-generation - mobile-development - qwen - qwen2.5-coder - mlx - transformers - vllm - text-generation - agentic - agent library_name: transformers pipeline_tag: text-generation base_model: Qwen/Qwen2.5-Coder-14B-Instruct datasets: - flutter_docs_alpaca --- # GenMobiAi — Qwen2.5-Coder-14B Flutter Specialist **GenMobiAi** is a fine-tuned version of [Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) specialized for Flutter and Dart development. Optimized for agentic code generation, mobile development, and multi-framework orchestration. ## Overview **Type**: Code Generation + Agentic AI **Parameters**: 14.77B **Architecture**: Qwen2ForCausalLM (48 layers) **Context Length**: 128,000 tokens **Quantization**: 4-bit MLX (group_size=64) **Training Method**: QLoRA fine-tuning via MLX-LM **Training Data**: 311 Flutter/Dart samples from flutter.dev + pub.dev **License**: Apache 2.0 ## Key Features ### Flutter Code Generation - **Widgets**: StatelessWidget, StatefulWidget, custom widgets, Material 3 design - **State Management**: Provider, Riverpod, GetX, BLoC, MobX patterns - **Async Dart**: Futures, Streams, isolates, error handling - **Architecture**: MVVM, Clean Architecture, Repository pattern ### Pub.dev Package Intelligence - HTTP clients (Dio, http with interceptors) - Local storage (hive, shared_preferences) - Animations (flutter_animate, lottie) - Testing (widget tests, unit tests with mockito) ### Agentic Capabilities - ChatML format with tool-call support (LangGraph-compatible) - Multi-message context preservation - Structured JSON tool responses ## Quick Start ### Transformers (CPU/GPU) ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch tokenizer = AutoTokenizer.from_pretrained("your-org/genmobiai-qwen2.5-coder-14b-flutter") model = AutoModelForCausalLM.from_pretrained( "your-org/genmobiai-qwen2.5-coder-14b-flutter", torch_dtype=torch.bfloat16, device_map="auto" ) messages = [ {"role": "system", "content": "You are GenMobiAi, an expert Flutter developer."}, {"role": "user", "content": "Create a Riverpod provider for a shopping cart."} ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([text], return_tensors="pt").to(model.device) output = model.generate(**inputs, max_new_tokens=1024, temperature=0.3, top_p=0.9) print(tokenizer.decode(output[0], skip_special_tokens=True)) ``` ### MLX-LM (Apple Silicon, recommended) ```bash python -m mlx_lm.generate \ --model path/to/genmobiai-qwen2.5-coder-14b-flutter \ --prompt "Write a Flutter Counter widget with SharedPreferences persistence" \ --max-tokens 1024 \ --temp 0.3 ``` ### vLLM (High-Throughput) ```python from vllm import LLM, SamplingParams llm = LLM("path/to/genmobiai-qwen2.5-coder-14b-flutter", max_model_len=8192) outputs = llm.generate( ["<|im_start|>user\nWrite a Flutter auth provider<|im_end|>\n"], SamplingParams(temperature=0.3, top_p=0.9, max_tokens=1024) ) print(outputs[0].outputs[0].text) ``` ### Ollama ```bash # Convert to GGUF first python -m llama_cpp.server --model path/genmobiai-q4_k_m.gguf --port 8000 # Or use Modelfile ollama create genmobiai -f - <` (151645) - **PAD Token**: `<|endoftext|>` (151643) - **Special Tokens**: ChatML (`<|im_start|>`, `<|im_end|>`) + tool-call markers ### Quantization (MLX) - **Bits**: 4 - **Group Size**: 64 - **Reduces Size**: ~28GB (BF16) → ~8.3GB (4-bit) ## Training Configuration **Dataset**: 311 Flutter/Dart samples (279 train / 32 eval) **Method**: QLoRA via MLX-LM on Apple Silicon **LoRA Rank**: 8 **Trainable Layers**: 16 of 48 **Batch Size**: 1 | **Grad Accumulation**: 2 **Learning Rate**: 1e-5 **Max Seq Length**: 1,024 **Iterations**: 1,000 **Estimated Training Time**: 4–8 hours (M3/M4 24GB) ## Hardware Requirements | Hardware | Memory | Inference Speed | Use Case | |----------|--------|-----------------|----------| | Apple M3/M4 (MLX) | 16GB+ | 100+ tok/s @ 4K | Development | | RTX 4090 (BF16) | 24GB | 200+ tok/s | Production | | H100 (batched) | 80GB | 1000+ tok/s | Server | | CPU (GGUF Q4) | 32GB | 10–15 tok/s | Edge | ## Capabilities & Use Cases ### Flutter Development - ✅ Widget scaffolding (Material 3, Cupertino, adaptive) - ✅ State management patterns (Provider, Riverpod, GetX, BLoC) - ✅ REST API integration (Dio, http, interceptors) - ✅ Local storage (hive, shared_preferences, file I/O) - ✅ Testing (widget tests, unit tests, integration tests) - ✅ Platform channels & native integration ### Code Quality - Null safety best practices - MVVM + Clean Architecture patterns - Error handling & logging - Performance optimization tips - Documentation & inline comments ### Agentic Features - Tool-call support via XML-wrapped JSON - Multi-message context preservation - Chat template integration (ChatML) - LangGraph workflow compatibility ## Limitations 1. **Dataset Size**: 311 samples may cause hallucinations on less-documented packages 2. **Quantization Artifacts**: 4-bit rounding in floating-point operations 3. **Vision Tokens**: Vocabulary includes image tokens (inactive) from multimodal base 4. **Context in Practice**: MLX 4-bit inference optimal at 4K–8K tokens on 24GB 5. **No Formal Benchmarks**: Performance validated empirically, not on standard evals 6. **Dart 3+ Features**: records, sealed classes partially covered ## Special Tokens ``` <|endoftext|> (ID: 151643) → Padding / Fallback EOS <|im_start|> (ID: 151644) → ChatML message start <|im_end|> (ID: 151645) → ChatML message end (Primary EOS) (Custom) → Agentic tool invocation (XML wrapper) (Custom) → Agentic tool response end ``` ## Citation ```bibtex @misc{genmobiai2025, title = {GenMobiAi: Qwen2.5-Coder-14B Fine-tuned for Flutter/Dart Development}, author = {GenMobiAi Contributors}, year = {2025}, url = {https://huggingface.co/your-org/genmobiai-qwen2.5-coder-14b-flutter}, license = {Apache 2.0} } @misc{qwen2_5_coder, title = {Qwen2.5-Coder: A Capable Code Language Model}, author = {Alibaba Cloud}, year = {2024}, url = {https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct} } ``` ## License This model is licensed under the **Apache License 2.0**. - **Base Model**: Qwen2.5-Coder-14B-Instruct by Alibaba Cloud (Apache 2.0) - **Fine-tuning & Specialization**: GenMobiAi Contributors (Apache 2.0) - **Training Data**: flutter.dev (BSD 3-Clause), pub.dev packages (per-package), Flutter GitHub (BSD 3-Clause) See [LICENSE](./LICENSE) for full text. ## Contributing Issues or improvements? - Report on [GitHub](https://github.com/your-org/genmobiai) or [HF Hub](https://huggingface.co/your-org/genmobiai-qwen2.5-coder-14b-flutter) - Submit Flutter patterns to expand the training dataset - Improve documentation --- **Last Updated**: 2025-05-25 **Status**: Production-Ready **Framework Support**: Transformers, MLX-LM, vLLM, llama.cpp, Ollama