Qwen3 (4B) Instruct
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You will learn how to do data prep, how to train, how to run the model, & how to save it
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Installation
Unsloth
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. 🦥 Unsloth Zoo will now patch everything to make training faster! ==((====))== Unsloth 2025.8.2: Fast Qwen3 patching. Transformers: 4.54.1. \\ /| Tesla T4. Num GPUs = 1. Max memory: 14.741 GB. Platform: Linux. O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 7.5. CUDA Toolkit: 12.4. Triton: 3.2.0 \ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post3. FA2 = False] "-____-" Free license: http://github.com/unslothai/unsloth Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! Unsloth: QLoRA and full finetuning all not selected. Switching to 16bit LoRA.
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We now add LoRA adapters so we only need to update a small amount of parameters!
Unsloth: Making `model.base_model.model.model` require gradients
Data Prep
We now use the Qwen-3 format for conversation style finetunes. We use Maxime Labonne's FineTome-100k dataset in ShareGPT style. Qwen-3 renders multi turn conversations like below:
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
Hey there!<|im_end|>
We use our get_chat_template function to get the correct chat template. We support zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, phi3, llama3, phi4, qwen2.5, gemma3 and more.
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We now use standardize_data_formats to try converting datasets to the correct format for finetuning purposes!
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Let's see how row 100 looks like!
{'conversations': [{'content': 'What is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?',
, 'role': 'user'},
, {'content': 'In programming, the modulus operator is represented by the \'%\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\n\n```python\n# Calculate the modulus\nModulus = a % b\n\nprint("Modulus of the given numbers is: ", Modulus)\n```\n\nIn this code snippet, the variables \'a\' and \'b\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \'%\', we calculate the remainder when \'a\' is divided by \'b\'. The result is then stored in the variable \'Modulus\'. Finally, the modulus value is printed using the \'print\' statement.\n\nFor example, if \'a\' is 10 and \'b\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\n\n```\nModulus of the given numbers is: 2\n```\n\nThis means that the modulus of 10 and 4 is 2.',
, 'role': 'assistant'}],
, 'source': 'infini-instruct-top-500k',
, 'score': 4.774171352386475} We now have to apply the chat template for Qwen-3 onto the conversations, and save it to text.
Map: 0%| | 0/100000 [00:00<?, ? examples/s]
Let's see how the chat template did!
'<|im_start|>user\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?<|im_end|>\n<|im_start|>assistant\nIn programming, the modulus operator is represented by the \'%\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\n\n```python\n# Calculate the modulus\nModulus = a % b\n\nprint("Modulus of the given numbers is: ", Modulus)\n```\n\nIn this code snippet, the variables \'a\' and \'b\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \'%\', we calculate the remainder when \'a\' is divided by \'b\'. The result is then stored in the variable \'Modulus\'. Finally, the modulus value is printed using the \'print\' statement.\n\nFor example, if \'a\' is 10 and \'b\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\n\n```\nModulus of the given numbers is: 2\n```\n\nThis means that the modulus of 10 and 4 is 2.<|im_end|>\n' Unsloth: Tokenizing ["text"] (num_proc=2): 0%| | 0/100000 [00:00<?, ? examples/s]
We also use Unsloth's train_on_completions method to only train on the assistant outputs and ignore the loss on the user's inputs. This helps increase accuracy of finetunes!
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Let's verify masking the instruction part is done! Let's print the 100th row again.
'<|im_start|>user\nWhat is the modulus operator in programming and how can I use it to calculate the modulus of two given numbers?<|im_end|>\n<|im_start|>assistant\nIn programming, the modulus operator is represented by the \'%\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\n\n```python\n# Calculate the modulus\nModulus = a % b\n\nprint("Modulus of the given numbers is: ", Modulus)\n```\n\nIn this code snippet, the variables \'a\' and \'b\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \'%\', we calculate the remainder when \'a\' is divided by \'b\'. The result is then stored in the variable \'Modulus\'. Finally, the modulus value is printed using the \'print\' statement.\n\nFor example, if \'a\' is 10 and \'b\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\n\n```\nModulus of the given numbers is: 2\n```\n\nThis means that the modulus of 10 and 4 is 2.<|im_end|>\n' Now let's print the masked out example - you should see only the answer is present:
' In programming, the modulus operator is represented by the \'%\' symbol. It calculates the remainder when one number is divided by another. To calculate the modulus of two given numbers, you can use the modulus operator in the following way:\n\n```python\n# Calculate the modulus\nModulus = a % b\n\nprint("Modulus of the given numbers is: ", Modulus)\n```\n\nIn this code snippet, the variables \'a\' and \'b\' represent the two given numbers for which you want to calculate the modulus. By using the modulus operator \'%\', we calculate the remainder when \'a\' is divided by \'b\'. The result is then stored in the variable \'Modulus\'. Finally, the modulus value is printed using the \'print\' statement.\n\nFor example, if \'a\' is 10 and \'b\' is 4, the modulus calculation would be 10 % 4, which equals 2. Therefore, the output of the above code would be:\n\n```\nModulus of the given numbers is: 2\n```\n\nThis means that the modulus of 10 and 4 is 2.<|im_end|>\n' Let's train the model! To resume a training run, set trainer.train(resume_from_checkpoint = True)
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1 \\ /| Num examples = 100,000 | Num Epochs = 1 | Total steps = 60 O^O/ \_/ \ Batch size per device = 2 | Gradient accumulation steps = 4 \ / Data Parallel GPUs = 1 | Total batch size (2 x 4 x 1) = 8 "-____-" Trainable parameters = 66,060,288 of 4,088,528,384 (1.62% trained) `use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.
Unsloth: Will smartly offload gradients to save VRAM!
The sequence you provided is the Fibonacci sequence, where each number is the sum of the two preceding numbers. To continue the sequence, we add the last two numbers: 8 + 5 = 13 So, the next number in the sequence is 13.<|im_end|>
('lora_model/tokenizer_config.json',
, 'lora_model/special_tokens_map.json',
, 'lora_model/chat_template.jinja',
, 'lora_model/vocab.json',
, 'lora_model/merges.txt',
, 'lora_model/added_tokens.json',
, 'lora_model/tokenizer.json') Now if you want to load the LoRA adapters we just saved for inference, set False to True:
Saving to float16 for VLLM
We also support saving to float16 directly. Select merged_16bit for float16 or merged_4bit for int4. We also allow lora adapters as a fallback. Use push_to_hub_merged to upload to your Hugging Face account! You can go to https://huggingface.co/settings/tokens for your personal tokens. See our docs for more deployment options.
GGUF / llama.cpp Conversion
To save to GGUF / llama.cpp, we support it natively now! We clone llama.cpp and we default save it to q8_0. We allow all methods like q4_k_m. Use save_pretrained_gguf for local saving and push_to_hub_gguf for uploading to HF.
Some supported quant methods (full list on our docs page):
q8_0- Fast conversion. High resource use, but generally acceptable.q4_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K.q5_k_m- Recommended. Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K.
[NEW] To finetune and auto export to Ollama, try our Ollama notebook
Likewise, if you want to instead push to GGUF to your Hugging Face account, set if False to if True and add your Hugging Face token and upload location!
Now, use the qwen_finetune.Q8_0.gguf file or qwen_finetune.Q4_K_M.gguf file in llama.cpp.
And we're done! If you have any questions on Unsloth, we have a Discord channel! If you find any bugs or want to keep updated with the latest LLM stuff, or need help, join projects etc, feel free to join our Discord!
Some other resources:
- Train your own reasoning model - Llama GRPO notebook Free Colab
- Saving finetunes to Ollama. Free notebook
- Llama 3.2 Vision finetuning - Radiography use case. Free Colab
- See notebooks for DPO, ORPO, Continued pretraining, conversational finetuning and more on our documentation!
This notebook and all Unsloth notebooks are licensed LGPL-3.0.