Kaggle Mistral V0.3 (7B) 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.
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==((====))== Unsloth: Fast Mistral patching release 2024.5 \\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform = Linux. O^O/ \_/ \ Pytorch: 2.3.0+cu121. CUDA = 7.5. CUDA Toolkit = 12.1. \ / Bfloat16 = FALSE. Xformers = 0.0.26.post1. FA = False. "-____-" Free Apache license: http://github.com/unslothai/unsloth
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You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama.LlamaTokenizer'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
We now add LoRA adapters so we only need to update 1 to 10% of all parameters!
Unsloth 2024.5 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.
Data Prep
We now use the ChatML format for conversation style finetunes. We use Open Assistant conversations in ShareGPT style. ChatML renders multi turn conversations like below:
<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What's the capital of France?<|im_end|>
<|im_start|>assistant
Paris.
[NOTE] To train only on completions (ignoring the user's input) read our docs here
We use our get_chat_template function to get the correct chat template. We support zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old and our own optimized unsloth template.
Normally one has to train <|im_start|> and <|im_end|>. We instead map <|im_end|> to be the EOS token, and leave <|im_start|> as is. This requires no additional training of additional tokens.
Note ShareGPT uses {"from": "human", "value" : "Hi"} and not {"role": "user", "content" : "Hi"}, so we use mapping to map it.
For text completions like novel writing, try this notebook.
Unsloth: Will map <|im_end|> to EOS = </s>. You are using the default legacy behaviour of the <class 'transformers.models.llama.tokenization_llama_fast.LlamaTokenizerFast'>. This is expected, and simply means that the `legacy` (previous) behavior will be used so nothing changes for you. If you want to use the new behaviour, set `legacy=False`. This should only be set if you understand what it means, and thoroughly read the reason why this was added as explained in https://github.com/huggingface/transformers/pull/24565
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Let's see how the ChatML format works by printing the 5th element
[{'from': 'human',
, 'value': 'What is the typical wattage of bulb in a lightbox?'},
, {'from': 'gpt',
, 'value': 'The typical wattage of a bulb in a lightbox is 60 watts, although domestic LED bulbs are normally much lower than 60 watts, as they produce the same or greater lumens for less wattage than alternatives. A 60-watt Equivalent LED bulb can be calculated using the 7:1 ratio, which divides 60 watts by 7 to get roughly 9 watts.'},
, {'from': 'human',
, 'value': 'Rewrite your description of the typical wattage of a bulb in a lightbox to only include the key points in a list format.'}] <|im_start|>user What is the typical wattage of bulb in a lightbox?<|im_end|> <|im_start|>assistant The typical wattage of a bulb in a lightbox is 60 watts, although domestic LED bulbs are normally much lower than 60 watts, as they produce the same or greater lumens for less wattage than alternatives. A 60-watt Equivalent LED bulb can be calculated using the 7:1 ratio, which divides 60 watts by 7 to get roughly 9 watts.<|im_end|> <|im_start|>user Rewrite your description of the typical wattage of a bulb in a lightbox to only include the key points in a list format.<|im_end|>
If you're looking to make your own chat template, that also is possible! You must use the Jinja templating regime. We provide our own stripped down version of the Unsloth template which we find to be more efficient, and leverages ChatML, Zephyr and Alpaca styles.
More info on chat templates on our wiki page!
/usr/local/lib/python3.10/dist-packages/multiprocess/popen_fork.py:66: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock. self.pid = os.fork()
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max_steps is given, it will override any value given in num_train_epochs
GPU = Tesla T4. Max memory = 14.748 GB. 4.52 GB of memory reserved.
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1 \\ /| Num examples = 9,033 | Num Epochs = 1 O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4 \ / Total batch size = 8 | Total steps = 60 "-____-" Number of trainable parameters = 41,943,040
870.2993 seconds used for training. 14.5 minutes used for training. Peak reserved memory = 6.289 GB. Peak reserved memory for training = 1.769 GB. Peak reserved memory % of max memory = 42.643 %. Peak reserved memory for training % of max memory = 11.995 %.
Unsloth: Will map <|im_end|> to EOS = <|im_end|>. The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
['<|im_start|>user\nContinue the fibonacci sequence: 1, 1, 2, 3, 5, 8,<|im_end|> \n<|im_start|>assistant\nThe next number in the Fibonacci sequence is 13.\n\nThe Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, starting from 0 and 1. The sequence goes: 0, 1, 1, ']
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 attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
<|im_start|>user Continue the fibonacci sequence: 1, 1, 2, 3, 5, 8,<|im_end|> <|im_start|>assistant 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, starting from 0 and 1. The sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2
('lora_model/tokenizer_config.json',
, 'lora_model/special_tokens_map.json',
, 'lora_model/tokenizer.json') Now if you want to load the LoRA adapters we just saved for inference, set False to True:
The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results. Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
<|im_start|>user What is a famous tall tower in Paris?<|im_end|> <|im_start|>assistant The Eiffel Tower is a famous tall tower in Paris. It is one of the most recognizable landmarks in the world and is a popular tourist destination. The tower was built in 1889 for the World's Fair and is named after Gustave Eiffel, the engineer who designed and built it. The tower stands at a height of 324 meters (1,063 feet) and is made of iron. It is located on the Champ de Mars, a large public park in Paris.<|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 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.
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.
