Quantifying the Carbon Emissions of Machine Learning
Paper • 1910.09700 • Published • 45
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Model Details
Model Description
Research purpose only.
gftd/gftd-260206
GFTD Distilled Model — Claude Opus 4.6 knowledge distilled into Qwen3-8B via QLoRA on Apple MLX.
Training
- Base: Qwen/Qwen3-8B (8.2B params)
- Teacher: Claude Opus 4.6 (via OpenRouter)
- Method: MLX QLoRA (rank=32, layers=8, 200 iters)
- Dataset: 100 samples across 6 categories (all Opus-generated)
- Final Loss: 0.1820
- Eval Score: 100%
Categories
| Category | Samples |
|---|---|
| Code Generation (Go/Rust/Svelte/Python) | 30 |
| MCP Tool Usage | 20 |
| Agent Task Decomposition | 15 |
| Web Browser Interaction | 10 |
| Dapr Patterns | 15 |
| Performers API | 10 |
Usage
License
Apache 2.0
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- Language(s) (NLP): en
- License: apache-2.0
- Finetuned from model [optional]: Qwen/Qwen3-8B
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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
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Training Details
Training Data
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Training Procedure
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Evaluation
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Factors
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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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Model size
0.6B params
Tensor type
BF16
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