gftd-260213

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gftd/gftd-260213

GFTD Well-Becoming Model - The main GFTD model focused on well-becoming philosophy, integrating electromagnetic (Maxwell), information-physical (Landauer), and process (Whitehead) perspectives.

Philosophy: GFTD (Integral)

Well-becoming (善くなること) is the continuous process of becoming better -- not arrival at a fixed state, but perpetual creative advance. This model unifies field-based reasoning, physical computation, and process philosophy into a coherent approach to software engineering, distributed systems, and AI reasoning.

Training Details

• Framework: Apple MLX (mlx-lm QLoRA)
• Base Model: Qwen/Qwen3-VL-8B
• Teacher: Claude Opus 4.6 (via OpenRouter)
• LoRA Config: rank=64, alpha=128, layers=16, bits=4
• Dataset: 100 samples across 6 categories

Capabilities

• Go/Rust/Svelte/Python code generation
• MCP tool selection and operation (93+ tools)
• 8-step reasoning chains
• Web browser interaction
• Dapr patterns (Actors, Workflows, State, PubSub)
• GFTD Performers API operations

Usage (MLX)

from mlx_lm import load, generate
model, tokenizer = load("gftd/gftd-260213")
response = generate(model, tokenizer, prompt="Write a Go HTTP handler", max_tokens=512)

License

Apache 2.0

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• Language(s) (NLP): en
• License: apache-2.0
• Finetuned from model [optional]: Qwen/Qwen3-VL-8B

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Uses

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Direct Use

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Bias, Risks, and Limitations

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Recommendations

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How to Get Started with the Model

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Training Details

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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