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*This model was released on 2023-02-27 and added to Hugging Face Transformers on 2023-03-16 and contributed by [zphang](https://huggingface.co/zphang) and [BlackSamorez](https://huggingface.co/BlackSamorez).*

<div style="float: right;">
    <div class="flex flex-wrap space-x-1">
        <img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat">
        <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white">
        <img alt="Tensor parallelism" src="https://img.shields.io/badge/Tensor%20parallelism-06b6d4?style=flat&logoColor=white">
    </div>
</div>

# Llama

[LLaMA: Open and Efficient Foundation Language Models](https://huggingface.co/papers/2302.13971) presents a series of foundation language models sized from 7B to 65B parameters. Trained on trillions of tokens, LLaMA demonstrates the capability to achieve state-of-the-art results using only publicly available datasets. Specifically, LLaMA-13B surpasses GPT-3 (175B) on most benchmarks, while LLaMA-65B competes with top models like Chinchilla-70B and PaLM-540B. All models are released to the research community.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(task="text-generation", model="huggyllama/llama-7b", dtype="auto",)
pipeline("Plants create energy through a process known as photosynthesis.")
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("huggyllama/llama-7b")
model = AutoModelForCausalLM.from_pretrained("huggyllama/llama-7b", dtype="auto",)

inputs = tokenizer("Plants create energy through a process known as photosynthesis.", return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0]))
```

</hfoption>
</hfoptions>

## Usage tips

- The tokenizer is a byte-pair encoding model based on SentencePiece. During decoding, if the first token starts a word (like "Banana"), the tokenizer doesn't prepend the prefix space to the string.

## LlamaConfig

[[autodoc]] LlamaConfig

## LlamaTokenizer

[[autodoc]] LlamaTokenizer
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - save_vocabulary

## LlamaTokenizerFast

[[autodoc]] LlamaTokenizerFast
    - build_inputs_with_special_tokens
    - get_special_tokens_mask
    - create_token_type_ids_from_sequences
    - update_post_processor
    - save_vocabulary

## LlamaModel

[[autodoc]] LlamaModel
    - forward

## LlamaForCausalLM

[[autodoc]] LlamaForCausalLM
    - forward

## LlamaForSequenceClassification

[[autodoc]] LlamaForSequenceClassification
    - forward

## LlamaForQuestionAnswering

[[autodoc]] LlamaForQuestionAnswering
    - forward

## LlamaForTokenClassification

[[autodoc]] LlamaForTokenClassification
    - forward

