33b9a9c4c9
* Create install.md * Update install_cpu.md * Delete original docs/mddocs/Overview/install_cpu.md * Update install_cpu.md * Update install_gpu.md * update llm.md and install.md * Update docs in KeyFeatures * Review and fix typos * Fix on folded NOTE * Small fix * Small fix * Remove empty known_issue.md * Small fix * Small fix * Further fix * Fixes * Fix --------- Co-authored-by: Yuwen Hu <yuwen.hu@intel.com>
66 lines
3 KiB
Markdown
66 lines
3 KiB
Markdown
# IPEX-LLM in 5 minutes
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You can use IPEX-LLM to run any [*Hugging Face Transformers*](https://huggingface.co/docs/transformers/index) PyTorch model. It automatically optimizes and accelerates LLMs using low-precision (INT4/INT5/INT8) techniques, modern hardware accelerations and latest software optimizations.
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Hugging Face transformers-based applications can run on IPEX-LLM with one-line code change, and you'll immediately observe significant speedup<sup><a href="#footnote-perf" id="ref-perf">[1]</a></sup>.
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Here, let's take a relatively small LLM model, i.e [open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2), and IPEX-LLM INT4 optimizations as an example.
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## Load a Pretrained Model
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Simply use one-line `transformers`-style API in `ipex-llm` to load `open_llama_3b_v2` with INT4 optimization (by specifying `load_in_4bit=True`) as follows:
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```python
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from ipex_llm.transformers import AutoModelForCausalLM
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model = AutoModelForCausalLM.from_pretrained(pretrained_model_name_or_path="openlm-research/open_llama_3b_v2",
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load_in_4bit=True)
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```
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> [!TIP]
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> [open_llama_3b_v2](https://huggingface.co/openlm-research/open_llama_3b_v2) is a pretrained large language model hosted on Hugging Face. `openlm-research/open_llama_3b_v2` is its Hugging Face model id. `from_pretrained` will automatically download the model from Hugging Face to a local cache path (e.g. ``~/.cache/huggingface``), load the model, and converted it to `ipex-llm` INT4 format.
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>
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> It may take a long time to download the model using API. You can also download the model yourself, and set `pretrained_model_name_or_path` to the local path of the downloaded model. This way, `from_pretrained` will load and convert directly from local path without download.
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## Load Tokenizer
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You also need a tokenizer for inference. Just use the official `transformers` API to load `LlamaTokenizer`:
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```python
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from transformers import LlamaTokenizer
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tokenizer = LlamaTokenizer.from_pretrained(pretrained_model_name_or_path="openlm-research/open_llama_3b_v2")
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```
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## Run LLM
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Now you can do model inference exactly the same way as using official `transformers` API:
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```python
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import torch
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with torch.inference_mode():
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prompt = 'Q: What is CPU?\nA:'
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# tokenize the input prompt from string to token ids
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input_ids = tokenizer.encode(prompt, return_tensors="pt")
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# predict the next tokens (maximum 32) based on the input token ids
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output = model.generate(input_ids,
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max_new_tokens=32)
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# decode the predicted token ids to output string
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output_str = tokenizer.decode(output[0], skip_special_tokens=True)
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print(output_str)
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```
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------
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<div>
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<p>
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<sup><a href="#ref-perf" id="footnote-perf">[1]</a>
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Performance varies by use, configuration and other factors. <code><span>ipex-llm</span></code> may not optimize to the same degree for non-Intel products. Learn more at <a href="https://www.Intel.com/PerformanceIndex">www.Intel.com/PerformanceIndex</a>.
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</sup>
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</p>
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</div>
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