qwen-flutter-fused

GenMobiAi — Qwen2.5-Coder-14B Flutter Specialist

GenMobiAi is a fine-tuned version of 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)

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)

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)

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

# 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 - <<EOF
FROM ./genmobiai-q4_k_m.gguf
SYSTEM "You are GenMobiAi, an expert Flutter developer."
PARAMETER temperature 0.3
PARAMETER top_p 0.9
EOF

ollama run genmobiai "Build a Flutter provider for authentication"

Recommended Sampling Parameters

Model Specifications

Architecture

• Model Type: Qwen2ForCausalLM
• Hidden Size: 5,120
• Intermediate Size: 13,824
• Num Layers: 48
• Num Attention Heads: 40
• Num KV Heads: 8
• RoPE Theta: 1,000,000
• Max Position Embeddings: 128,000

Tokenizer

• Type: Qwen2Tokenizer
• Vocab Size: 152,064
• EOS Token: <|im_end|> (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

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)
<tool_call>        (Custom)       → Agentic tool invocation (XML wrapper)
</tool_call>       (Custom)       → Agentic tool response end

Citation

@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 for full text.

Contributing

Issues or improvements?

• Report on GitHub or HF Hub
• Submit Flutter patterns to expand the training dataset
• Improve documentation

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Last Updated: 2025-05-25
Status: Production-Ready
Framework Support: Transformers, MLX-LM, vLLM, llama.cpp, Ollama