Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 45
Model Card for Model ID
Model Details
Model Description
gftd/whitehead-260213
Whitehead Process Philosophy Model - A model informed by Whitehead's process philosophy: process over substance, actual occasions, creative advance, prehension, eternal objects, and the philosophy of organism.
Philosophy: Alfred North Whitehead
Whitehead's process philosophy holds that reality consists not of static substances but of processes of becoming. Every event is an 'actual occasion' that prehends (feels) its environment and achieves a novel synthesis. This model applies Whitehead's thinking to computation: services are organisms, messages are prehensions, architectures are societies of interconnected processes, and good design is creative advance toward organic unity.
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/whitehead-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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Bias, Risks, and Limitations
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Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
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Training Details
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Summary
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Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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