Wizcoderr/qwen3-14b-flutter-fused

Qwen3 14B Flutter Fused

GenMobiAi presents a fine-tuned and optimized version of Qwen3-14B-Instruct, specialized for Flutter and Dart development with agentic code generation capabilities.

Key Specs:

• —Parameters: 14B
• —Architecture: Qwen3ForCausalLM (40 layers)
• —Context Length: 40,960 tokens
• —Quantization: 4-bit (group_size=64)
• —Model Type: qwen3
• —Data Type: bfloat16
• —License: Community License

Key Features

Flutter & Dart Development

• —Widgets - StatelessWidget, StatefulWidget, Material 3 components
• —State Management - Provider, Riverpod, GetX, BLoC, MobX patterns
• —Async Programming - Futures, Streams, isolates, async/await
• —Architecture - MVVM, Clean Architecture, feature-first structures
• —Testing - Widget tests, unit tests with mockito, integration tests

Package Ecosystem Intelligence

• —HTTP Clients - Dio, http, chopper with interceptors
• —Local Storage - Hive, shared_preferences, sqflite, isar
• —Animations - flutter_animate, lottie, rive integration
• —UI Libraries - GetX UI, Velocity UI, responsive frameworks
• —Testing - Golden tests, behavior-driven testing (BDD)

Agentic Capabilities

• —ChatML Format - Tool-call support for multi-step workflows
• —Context Preservation - Multi-message conversation handling
• —JSON Tool Responses - LangGraph-compatible structured outputs
• —Streaming - Real-time token generation for UI responsiveness

Quick Start Examples

Transformers (CPU/GPU)

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("path/to/qwen3-14b-flutter-fused")
model = AutoModelForCausalLM.from_pretrained(
    "path/to/qwen3-14b-flutter-fused",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are GenMobiAi, an expert Flutter developer assistant."},
    {"role": "user", "content": "Create a responsive Flutter dashboard with dark mode support"}
]

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/qwen3-14b-flutter-fused \
  --prompt "Write a Flutter Provider pattern for cart management" \
  --max-tokens 1024 \
  --temp 0.3 \
  --top-p 0.9

vLLM (Production Serving)

from vllm import LLM, SamplingParams

llm = LLM("path/to/qwen3-14b-flutter-fused", max_model_len=4096)
outputs = llm.generate(
    ["<|im_start|>user\nCreate a Riverpod async data provider for user authentication<|im_end|>\n"],
    SamplingParams(temperature=0.3, top_p=0.9, max_tokens=1024, repetition_penalty=1.05)
)
print(outputs[0].outputs[0].text)

Recommended Sampling Parameters

Hardware Requirements

Model Architecture

• —Hidden Size: 5,120
• —Intermediate Size: 17,408
• —Num Layers: 40
• —Num Attention Heads: 40
• —Num KV Heads: 8
• —Max Position Embeddings: 40,960
• —Head Dimension: 128
• —Vocab Size: 151,936
• —Attention Bias: False
• —RMS Norm Epsilon: 1e-6

Special Tokens

[151643]    → BOS Token (Beginning of Sequence)
[151645]    → EOS Token (End of Sequence)
<|im_start|> → ChatML message start
<|im_end|>   → ChatML message end

Conversion & Deployment

Convert to GGUF (CPU Inference)

python scripts/mlx_to_gguf.py ./models/qwen3-14b-flutter-fused \
  -o qwen3-14b-flutter.gguf \
  -q q4_k_m

GGML Quantization Options

• —q4_k_m - 4-bit K-means (recommended balance)
• —q5_0 - 5-bit quantization (higher quality)
• —q8_0 - 8-bit quantization (best quality, larger size)
• —q3_k_m - 3-bit K-means (smallest, lower quality)

Use with Ollama

# After GGUF conversion
ollama create qwen3-flutter -f Modelfile
ollama run qwen3-flutter "Create a BLoC pattern for form validation"

Fine-Tuning

LoRA Fine-tuning with MLX

python -m mlx_lm.lora \
  --model path/to/qwen3-14b-flutter-fused \
  --data train_data.jsonl \
  --lora-layers 8 \
  --batch-size 1 \
  --iters 500

With Hugging Face Trainer

from transformers import TrainingArguments, SFTTrainer
from peft import LoraConfig

lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none"
)

training_args = TrainingArguments(
    output_dir="./flutter-finetuned",
    num_train_epochs=3,
    per_device_train_batch_size=1,
    learning_rate=1e-5,
    bf16=True
)

trainer = SFTTrainer(
    model="path/to/qwen3-14b-flutter-fused",
    args=training_args,
    peft_config=lora_config,
    train_dataset=train_dataset
)
trainer.train()

Limitations & Considerations

1. 1.Training Data Scale - Trained on Flutter-specific examples; may not generalize to non-mobile domains
2. 2.Quantization Artifacts - 4-bit quantization may introduce minor precision loss
3. 3.Context Window - 40,960 tokens; optimal for 4K-8K contexts
4. 4.Version Coverage - Reflects Flutter/Dart features up to training date
5. 5.Hallucinations - May generate plausible-sounding but incorrect package APIs
6. 6.Dependencies - Assumes familiarity with popular Flutter ecosystem packages

Troubleshooting

Out of Memory

# Reduce max_new_tokens or use smaller batch_size
output = model.generate(**inputs, max_new_tokens=512)  # Instead of 1024

Slow Generation

• —Use MLX on Apple Silicon (50-100x faster)
• —Enable flash-attention if available
• —Use smaller maxnewtokens
• —Consider vLLM for batched inference

Quality Issues

• —Lower temperature (0.2-0.4 for deterministic code)
• —Increase top_p (0.85-0.95)
• —Add more context in system prompt
• —Use multi-turn conversations for refinement

Citation

@misc{genmobiai2025_qwen3,
  title   = {Qwen3 14B Flutter Fused: Fine-tuned for Flutter/Dart Development},
  author  = {GenMobiAi Contributors},
  year    = {2025},
  url     = {https://huggingface.co/Wizcoderr/qwen3-14b-flutter-fused},
  license = {Community License}
}

Resources

• —Qwen3 Documentation
• —Flutter Official Docs
• —Dart Programming
• —MLX-LM
• —LLaMA.cpp

Support & Feedback

For issues, questions, or suggestions:

• —Open an issue on GitHub
• —Check existing discussions
• —Share your use cases and results

Made with ❤️ for the Flutter community