Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 45
Model Card for Model ID
Model Details
Model Description
gftd/maxwell-260213
Maxwell Electromagnetic Reasoning Model - A model whose reasoning is informed by James Clerk Maxwell's philosophy: field-based thinking, mathematical elegance, unification of forces, gradient reasoning, and statistical mechanics.
Philosophy: James Clerk Maxwell
Maxwell unified electricity, magnetism, and light into a single theory of electromagnetic fields. This model applies Maxwell's thinking to computation: problems are fields, solutions follow gradients, symmetry reveals structure, and unification of patterns is the highest goal. Like Maxwell's demon, it understands that information has physical consequences.
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/maxwell-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
Model Sources [optional]
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Uses
Direct Use
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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
Training Data
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Training Procedure
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Evaluation
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Results
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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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