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A complete Jupyter Notebook tutorial demonstrating how to add function calling capabilities to a Turkish reasoning model using Unsloth and QLoRA technologies on an H100 GPU, covering the entire workflow from environment setup to model deployment.
Section 01
A complete Jupyter Notebook tutorial demonstrating how to add function calling capabilities to a Turkish reasoning model using Unsloth and QLoRA technologies on an H100 GPU, covering the entire workflow from environment setup to model deployment.
Section 02
Section 03
This is a complete fine-tuning tutorial for Turkish large language models, aiming to add function calling capabilities to a base reasoning model. The project uses the Unsloth framework combined with QLoRA technology, optimized specifically for the NVIDIA H100 GPU, fully leveraging the bfloat16 and Flash Attention 2 features of the Hopper architecture.
The base model used is AlicanKiraz0/Kizagan-E4B-Turkish-Reasoning-Model, a Turkish model focused on reasoning capabilities. The fine-tuned model will have both function calling and general Turkish dialogue capabilities.
Section 04
Unsloth is one of the most popular optimization frameworks in the field of large model fine-tuning currently. Its core advantages include:
Section 05
QLoRA is the mainstream method for Parameter-Efficient Fine-Tuning (PEFT) currently. Its working principle is:
The LoRA parameters configured in this project are r=32 and alpha=32, which are relatively high configurations and can learn more complex patterns.
Section 06
Flash Attention is an IO-aware exact attention algorithm. Through block computation and recomputation strategies, it reduces the memory complexity of standard attention from O(N²) to O(N). On the H100, this can bring a 2-3x improvement in training speed.
Section 07
The project uses a mixed dataset strategy, which is a key consideration in function calling fine-tuning:
Section 08
| Dataset | Ratio | Purpose |
|---|---|---|
| Turkish Hermes Function Calling | 85% | Function calling training data, containing 144K Turkish prompt/completion pairs |
| Turkish LLM v10 Training | 15% | General Turkish dialogue data to maintain basic language capabilities |
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