Phi 4 Conversational
To run this, press "Runtime" and press "Run all" on a free Tesla T4 Google Colab instance!
To install Unsloth on your local device, follow our guide. This notebook is licensed LGPL-3.0.
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 2025.1.5: Fast Llama patching. Transformers: 4.47.1. \\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux. O^O/ \_/ \ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0 \ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post1. FA2 = False] "-____-" Free Apache license: http://github.com/unslothai/unsloth Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
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We now add LoRA adapters for parameter efficient finetuning - this allows us to only efficiently train 1% of all parameters.
Unsloth 2025.1.5 patched 40 layers with 40 QKV layers, 40 O layers and 40 MLP layers.
Data Prep
We now use the Phi-4 format for conversation style finetunes. We use Maxime Labonne's FineTome-100k dataset in ShareGPT style. But we convert it to HuggingFace's normal multiturn format ("role", "content") instead of ("from", "value")/ Phi-4 renders multi turn conversations like below:
<|im_start|>user<|im_sep|>Hello!<|im_end|>
<|im_start|>assistant<|im_sep|>Hi! How can I help?<|im_end|>
<|im_start|>user<|im_sep|>What is 2+2?<|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, phi4, llama3 and more.
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We now use standardize_sharegpt to convert ShareGPT style datasets into HuggingFace's generic format. This changes the dataset from looking like:
{"from": "system", "value": "You are an assistant"}
{"from": "human", "value": "What is 2+2?"}
{"from": "gpt", "value": "It's 4."}
to
{"role": "system", "content": "You are an assistant"}
{"role": "user", "content": "What is 2+2?"}
{"role": "assistant", "content": "It's 4."}
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We look at how the conversations are structured for item 5:
[{'content': 'How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?',
, 'role': 'user'},
, {'content': 'Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.',
, 'role': 'assistant'}] And we see how the chat template transformed these conversations.
'<|im_start|>user<|im_sep|>How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|im_end|><|im_start|>assistant<|im_sep|>Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'
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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.
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We verify masking is actually done:
'<|im_start|>user<|im_sep|>How do astronomers determine the original wavelength of light emitted by a celestial body at rest, which is necessary for measuring its speed using the Doppler effect?<|im_end|><|im_start|>assistant<|im_sep|>Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'
' Astronomers make use of the unique spectral fingerprints of elements found in stars. These elements emit and absorb light at specific, known wavelengths, forming an absorption spectrum. By analyzing the light received from distant stars and comparing it to the laboratory-measured spectra of these elements, astronomers can identify the shifts in these wavelengths due to the Doppler effect. The observed shift tells them the extent to which the light has been redshifted or blueshifted, thereby allowing them to calculate the speed of the star along the line of sight relative to Earth.<|im_end|>'
We can see the System and Instruction prompts are successfully masked!
GPU = Tesla T4. Max memory = 14.748 GB. 10.043 GB of memory reserved.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 100,000 | Num Epochs = 1 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 \ / Total batch size = 8 | Total steps = 30 "-____-" Number of trainable parameters = 65,536,000
959.8611 seconds used for training. 16.0 minutes used for training. Peak reserved memory = 12.361 GB. Peak reserved memory for training = 2.318 GB. Peak reserved memory % of max memory = 83.815 %. Peak reserved memory for training % of max memory = 15.717 %.
The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.
['<|im_start|>user<|im_sep|>Continue the fibonacci sequence: 1, 1, 2, 3, 5, 8,<|im_end|><|im_start|>assistant<|im_sep|>The next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. In this case, the sequence starts with 1 and 1, and each subsequent number is the sum of the']
You can also use a TextStreamer for continuous inference - so you can see the generation token by token, instead of waiting the whole time!
The next number in the Fibonacci sequence is 13. The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. In this case, the sequence starts with 1 and 1, and each subsequent number is the sum of the two preceding numbers. So, the next number after 8 is 13, which is the sum of 5 and 8.<|im_end|>
('lora_model/tokenizer_config.json',
, 'lora_model/special_tokens_map.json',
, '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:
The Eiffel Tower is a tall tower located in the capital of France, Paris. It stands at a height of 324 meters (1,063 feet) and was completed in 1889. The tower was designed by Gustave Eiffel and his team for the 1889 Exposition Universelle (World's Fair) to celebrate the 100th anniversary of the French Revolution. It is made of wrought iron and consists of three levels that are accessible to visitors. The Eiffel Tower is one of the most recognizable landmarks in the world and attracts millions of tourists each year.<|im_end|>
You can also use Hugging Face's AutoPeftModelForCausalLM. Only use this if you do not have unsloth installed. It can be hopelessly slow, since 4bit model downloading is not supported, and Unsloth's inference is 2x faster.
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 in our Docs):
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
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:
- Looking to use Unsloth locally? Read our Installation Guide for details on installing Unsloth on Windows, Docker, AMD, Intel GPUs.
- Learn how to do Reinforcement Learning with our RL Guide and notebooks.
- Read our guides and notebooks for Text-to-speech (TTS) and vision model support.
- Explore our LLM Tutorials Directory to find dedicated guides for each model.
- Need help with Inference? Read our Inference & Deployment page for details on using vLLM, llama.cpp, Ollama etc.
