Unsloth Just Made Fine-Tuning LLMs a Free-Tier Task.

It looks like you've provided a detailed guide on fine-tuning Qwen3-8B for Python coding tasks using LoRA (Low-Rank Adaptation) with an 18k dataset. Let's summarize and finalize your steps:

Summary of Steps

Step 1: Load the Model and Prepare for Training

1. Load Qwen3-8B: Initialize the model in a low-memory footprint mode by using LoRA.
2. Configure Gradient Checkpointing: Use Unsloth’s custom gradient checkpointing to optimize memory usage.

python
 
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3model_name = "Qwen/Qwen-3_8B"
4tokenizer = AutoTokenizer.from_pretrained(model_name)
5model = AutoModelForCausalLM.from_pretrained(
6    model_name,
7    torch_dtype=torch.float16,
8    device_map="auto",
9)
10
11# Apply LoRA
12from peft import LoraConfig, get_peft_model
13
14lora_config = LoraConfig(
15    r=8,
16    lora_alpha=32,
17    target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
18    bias="none",
19)
20model
21
22[Read the full article at Towards AI - Medium](https://pub.towardsai.net/unsloth-just-made-fine-tuning-llms-a-free-tier-task-9ce05a931b75?source=rss----98111c9905da---4)
23
24---
25
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