from strands import Agent
from strands_mlx import MLXModel

# Load base model + this adapter
model = MLXModel(
    model_id="mlx-community/Qwen3-1.7B-4bit",
    adapter_path="cagataycali/qwen3-strands-adapter"  # Your HF repo
)

agent = Agent(model=model)

# Now it knows Strands!
agent("How do I create a Strands tool?")
# → Accurate answer with working code examples

agent("What model providers does Strands support?")
# → Complete list: Bedrock, Anthropic, OpenAI, Ollama, MLX, etc.

📚 Training Details

Dataset:

105 question-answer pairs about Strands SDK
Generated using embedded Strands documentation
Avg 1,621 tokens per example
Complete tool call cycles captured (user → assistant(tool_calls) → tool(results) → assistant(final))

Training Configuration:

Base Model: mlx-community/Qwen3-1.7B-4bit
Method: LoRA (Low-Rank Adaptation)
Iterations: 200 (conservative to prevent overfitting)
Learning Rate: 1e-5
LoRA Rank: 8
LoRA Scale: 16.0
Training Time: 14 minutes on Apple Silicon
Hardware: M-series Mac

Key Training Strategy:

Conservative iteration count to avoid catastrophic forgetting
mask_prompt=True - Only train on assistant completions
Gradient checkpointing for memory efficiency
ChatML format with proper tool call structure

🔧 Advanced Usage

With Tools

python

from strands import Agent
from strands_mlx import MLXModel

model = MLXModel(
    model_id="mlx-community/Qwen3-1.7B-4bit",
    adapter_path="cagataycali/qwen3-strands-adapter"
)

# Tool calling still works perfectly!
agent = Agent(
    model=model,
    tools=["calculator", "shell", "file_read"]
)

agent("Calculate 15 * 7 and explain Strands")
# → Uses calculator AND provides Strands knowledge

Swap Adapters

python

# Load different adapters for different domains
strands_model = MLXModel(
    "mlx-community/Qwen3-1.7B-4bit",
    adapter_path="cagataycali/qwen3-strands-adapter"
)

coding_model = MLXModel(
    "mlx-community/Qwen3-1.7B-4bit",
    adapter_path="your-coding-adapter"
)

📖 What It Learned

The adapter was trained on comprehensive Strands documentation covering:

Installation & Setup
- pip installation methods
- Virtual environment setup
- Model provider configuration
Core Concepts
- Agent creation patterns
- Tool creation (@tool decorator, hot reload)
- Session managers
- System prompts
Model Providers
- Bedrock (AWS)
- Anthropic
- OpenAI
- Ollama (local)
- MLX (Apple Silicon)
- Gemini, LiteLLM, GitHub Models
Advanced Features
- Multi-agent systems (Swarm, Workflow)
- MLX training pipeline
- MCP server integration
- Direct tool calls

🎓 Training Your Own Domain Expert

Want to create your own domain-specific adapter? Here's how:

python

from strands import Agent
from strands_mlx import MLXModel, MLXSessionManager
from strands_mlx.tools import mlx_trainer, dataset_splitter

# 1. Collect training data
session = MLXSessionManager(session_id="my_domain")
model = MLXModel("mlx-community/Qwen3-1.7B-4bit")
agent = Agent(model=model, session_manager=session)

# Have conversations (100-200 examples recommended)
agent("Teach me about X")
# Data auto-saved to ~/.strands/mlx_training_data/my_domain.jsonl

# 2. Split dataset
dataset_splitter(
    input_path="~/.strands/mlx_training_data/my_domain.jsonl",
    train_ratio=0.8,
    valid_ratio=0.1,
    test_ratio=0.1
)

# 3. Train
mlx_trainer(
    action="train",
    config={
        "model": "mlx-community/Qwen3-1.7B-4bit",
        "data": "~/.strands/mlx_training_data/my_domain",
        "iters": 200,
        "learning_rate": 1e-5,
        "batch_size": 1,
        "grad_checkpoint": True
    }
)

# 4. Use it!
trained = MLXModel("mlx-community/Qwen3-1.7B-4bit", adapter_path="./adapter")

🔬 Benchmarks

Question: "How do I create a Strands agent with tools?"

Base Model Response:

I'll help you create a strands agent. Let me use the system_monitor tool...
<tool_call>{"name": "system_monitor", ...}</tool_call>  ← Hallucinated tool

Trained Model Response:

from strands import Agent

agent = Agent(
    system_prompt="You are a helpful assistant",
    tools=["calculator", "shell", "file_read"]
)

agent("Calculate 2+2")

📦 Files in This Adapter

adapters.safetensors - Trained LoRA weights
adapter_config.json - LoRA configuration
0000100_adapters.safetensors - Checkpoint at iter 100
0000200_adapters.safetensors - Checkpoint at iter 200

🛠️ Requirements

pip install strands-agents>=0.2.0 strands-mlx>=0.2.1

📝 License

Same as base model (Apache 2.0)

🙏 Acknowledgments

Built with Strands Agents SDK
Base model: Qwen3-1.7B-4bit
Training framework: MLX

🔗 Links

Strands SDK: https://github.com/strands-agents/sdk-python
Strands MLX: https://github.com/cagataycali/strands-mlx
Base Model: https://huggingface.co/mlx-community/Qwen3-1.7B-4bit

cagataydev/qwen3-strands-adapter

Qwen3-1.7B Strands Adapter

🎯 What This Does

📊 Training Results

🚀 Quick Start

📚 Training Details

🔧 Advanced Usage

With Tools

Swap Adapters

📖 What It Learned

🎓 Training Your Own Domain Expert

🔬 Benchmarks

📦 Files in This Adapter

🛠️ Requirements

📝 License

🙏 Acknowledgments

🔗 Links

cagataydev/
qwen3-strands-adapter