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transformers/docs/source/en/model_doc/llama.md
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transformers/docs/source/en/model_doc/llama.md
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<!--Copyright 2022 The HuggingFace Team. All rights reserved.
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Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with
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the License. You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on
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*This model was released on 2023-02-27 and added to Hugging Face Transformers on 2023-03-16.*
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<div style="float: right;">
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<div class="flex flex-wrap space-x-1">
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<img alt="PyTorch" src="https://img.shields.io/badge/PyTorch-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
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<img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
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<img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
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</div>
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</div>
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# Llama
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[Llama](https://huggingface.co/papers/2302.13971) is a family of large language models ranging from 7B to 65B parameters. These models are focused on efficient inference (important for serving language models) by training a smaller model on more tokens rather than training a larger model on fewer tokens. The Llama model is based on the GPT architecture, but it uses pre-normalization to improve training stability, replaces ReLU with SwiGLU to improve performance, and replaces absolute positional embeddings with rotary positional embeddings (RoPE) to better handle longer sequence lengths.
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You can find all the original Llama checkpoints under the [Huggy Llama](https://huggingface.co/huggyllama) organization.
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> [!TIP]
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> Click on the Llama models in the right sidebar for more examples of how to apply Llama to different language tasks.
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The example below demonstrates how to generate text with [`Pipeline`] or the [`AutoModel`], and from the command line.
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<hfoptions id="usage">
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<hfoption id="Pipeline">
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```py
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import torch
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from transformers import pipeline
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pipeline = pipeline(
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task="text-generation",
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model="huggyllama/llama-7b",
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dtype=torch.float16,
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device=0
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)
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pipeline("Plants create energy through a process known as")
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```
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</hfoption>
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<hfoption id="AutoModel">
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```py
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained(
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"huggyllama/llama-7b",
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)
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model = AutoModelForCausalLM.from_pretrained(
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"huggyllama/llama-7b",
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dtype=torch.float16,
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device_map="auto",
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attn_implementation="sdpa"
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)
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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</hfoption>
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<hfoption id="transformers CLI">
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```bash
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echo -e "Plants create energy through a process known as" | transformers run --task text-generation --model huggyllama/llama-7b --device 0
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```
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</hfoption>
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</hfoptions>
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Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
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The example below uses [torchao](../quantization/torchao) to only quantize the weights to int4.
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```py
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# pip install torchao
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import torch
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from transformers import TorchAoConfig, AutoModelForCausalLM, AutoTokenizer
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quantization_config = TorchAoConfig("int4_weight_only", group_size=128)
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model = AutoModelForCausalLM.from_pretrained(
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"huggyllama/llama-30b",
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dtype=torch.bfloat16,
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device_map="auto",
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quantization_config=quantization_config
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)
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tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-30b")
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input_ids = tokenizer("Plants create energy through a process known as", return_tensors="pt").to(model.device)
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output = model.generate(**input_ids, cache_implementation="static")
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print(tokenizer.decode(output[0], skip_special_tokens=True))
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```
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Use the [AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139) to better understand what tokens the model can and cannot attend to.
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```py
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from transformers.utils.attention_visualizer import AttentionMaskVisualizer
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visualizer = AttentionMaskVisualizer("huggyllama/llama-7b")
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visualizer("Plants create energy through a process known as")
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```
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<div class="flex justify-center">
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/llama-attn-mask.png"/>
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</div>
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## Notes
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- The tokenizer is a byte-pair encoding model based on [SentencePiece](https://github.com/google/sentencepiece). During decoding, if the first token is the start of the word (for example, "Banana"), the tokenizer doesn't prepend the prefix space to the string.
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## LlamaConfig
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[[autodoc]] LlamaConfig
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## LlamaTokenizer
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[[autodoc]] LlamaTokenizer
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- save_vocabulary
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## LlamaTokenizerFast
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[[autodoc]] LlamaTokenizerFast
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- build_inputs_with_special_tokens
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- get_special_tokens_mask
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- create_token_type_ids_from_sequences
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- update_post_processor
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- save_vocabulary
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## LlamaModel
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[[autodoc]] LlamaModel
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- forward
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## LlamaForCausalLM
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[[autodoc]] LlamaForCausalLM
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- forward
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## LlamaForSequenceClassification
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[[autodoc]] LlamaForSequenceClassification
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- forward
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## LlamaForQuestionAnswering
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[[autodoc]] LlamaForQuestionAnswering
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- forward
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## LlamaForTokenClassification
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[[autodoc]] LlamaForTokenClassification
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- forward
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