gftd/gftd-260206

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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

Model Sources [optional]

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Uses

Direct Use

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Out-of-Scope 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

Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Results

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Summary

Model Examination [optional]

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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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Technical Specifications [optional]

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