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46 lines
1.6 KiB
Markdown
46 lines
1.6 KiB
Markdown
LLaMA is a Large Language Model developed by Meta AI.
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It was trained on more tokens than previous models. The result is that the smallest version with 7 billion parameters has similar performance to GPT-3 with 175 billion parameters.
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This guide will cover usage through the official `transformers` implementation. For 4-bit mode, head over to [GPTQ models (4 bit mode)
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](GPTQ-models-(4-bit-mode).md).
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## Getting the weights
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### Option 1: pre-converted weights
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* Torrent: https://github.com/oobabooga/text-generation-webui/pull/530#issuecomment-1484235789
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* Direct download: https://huggingface.co/Neko-Institute-of-Science
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⚠️ The tokenizers for the sources above and also for many LLaMA fine-tunes available on Hugging Face may be outdated, so I recommend downloading the following universal LLaMA tokenizer:
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```
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python download-model.py oobabooga/llama-tokenizer
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```
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Once downloaded, it will be automatically applied to **every** `LlamaForCausalLM` model that you try to load.
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### Option 2: convert the weights yourself
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1. Install the `protobuf` library:
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```
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pip install protobuf
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```
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2. Use the script below to convert the model in `.pth` format that you, a fellow academic, downloaded using Meta's official link:
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### [convert_llama_weights_to_hf.py](https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py)
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```
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python convert_llama_weights_to_hf.py --input_dir /path/to/LLaMA --model_size 7B --output_dir /tmp/outputs/llama-7b
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```
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3. Move the `llama-7b` folder inside your `text-generation-webui/models` folder.
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## Starting the web UI
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```python
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python server.py --model llama-7b
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```
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