# CodeLlama[[codellama]]

[Code Llama](https://huggingface.co/papers/2308.12950)는 코딩 작업에 특화된 대규모 언어 모델 계열로,  [Llama 2](./llama2)를 기반으로 개발되었습니다. 일반적인 코드, Python 특화, 명령어(지시) 기반 변형 등 다양한 버전으로 제공되며, 모두 7B, 13B, 34B, 70B 매개변수 크기로 사용할 수 있습니다. Code Llama 모델은 코드를 생성하고 설명하며, 코드의 누락된 부분을 채울 수도 있습니다. 이를 인필링(infilling)이라고 합니다. 16K 토큰 길이로 훈련되었지만, 최대 100K 토큰까지 안정적으로 생성하며 긴 컨텍스트도 처리할 수 있습니다.

[Code Llama](https://huggingface.co/collections/meta-llama/code-llama-family-661da32d0a9d678b6f55b933) 컬렉션에서 모든 원본 Code Llama 체크포인트를 찾을 수 있습니다.

> [!TIP]
> 다양한 코딩 작업에 Code Llama를 적용하는 더 많은 예시를 보려면 오른쪽 사이드바의 Code Llama 모델을 클릭하세요.

아래 예시는 [Pipeline](/docs/transformers/v5.5.2/ko/main_classes/pipelines#transformers.Pipeline), [AutoModel](/docs/transformers/v5.5.2/ko/model_doc/auto#transformers.AutoModel), 그리고 명령줄에서 코드를 생성하는 방법을 보여줍니다.

```py
import torch
from transformers import pipeline

pipe = pipeline(
    "text-generation",
    model="meta-llama/CodeLlama-7b-hf",
    torch_dtype=torch.float16,
    device_map=0
)

# 기본 코드 생성
result = pipe("# Function to calculate the factorial of a number\ndef factorial(n):", max_new_tokens=256)
print(result[0]['generated_text'])

# 인필링
infill_result = pipe("def remove_non_ascii(s: str) -> str:\n    \"\"\" \n    return result", max_new_tokens=200)
print(infill_result[0]['generated_text'])
```

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

tokenizer = AutoTokenizer.from_pretrained("meta-llama/CodeLlama-7b-hf")
model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/CodeLlama-7b-hf",
    torch_dtype=torch.float16,
    device_map="auto",
    attn_implementation="sdpa"
)

# 기본 코드 생성
prompt = "# Function to calculate the factorial of a number\ndef factorial(n):"
input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")

output = model.generate(
    **input_ids,
    max_new_tokens=256,
    cache_implementation="static"
)
print(tokenizer.decode(output[0], skip_special_tokens=True))

# 인필링
infill_prompt = "def remove_non_ascii(s: str) -> str:\n    \"\"\" \n    return result"
input_ids = tokenizer(infill_prompt, return_tensors="pt").to(model.device)

filled_output = model.generate(**input_ids, max_new_tokens=200)
filled_text = tokenizer.decode(filled_output[0], skip_special_tokens=True)
print(filled_text)
```

양자화는 가중치를 더 낮은 정밀도로 표현하여 대규모 모델의 메모리 부담을 줄입니다. 더 많은 사용 가능한 양자화 백엔드는 [양자화](../quantization/overview) 개요를 참조하세요.

아래 예시는 [bitsandbytes](../quantization/bitsandbytes)를 사용하여 가중치를 4비트로만 양자화합니다.

```py
# bitsandbytes를 설치합니다.
import torch
from transformers import AutoModelForCausalLM, CodeLlamaTokenizer, BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_quant_type="nf4", bnb_4bit_use_double_quant=True)
tokenizer = CodeLlamaTokenizer.from_pretrained("meta-llama/CodeLlama-34b-hf")
model = AutoModelForCausalLM.from_pretrained(
   "meta-llama/CodeLlama-34b-hf",
   torch_dtype=torch.bfloat16,
   device_map="auto",
   quantization_config=bnb_config
)

prompt = "# Write a Python function to check if a string is a palindrome\ndef is_palindrome(s):"
input_ids = tokenizer(prompt, return_tensors="pt").to("cuda")

output = model.generate(**input_ids, max_new_tokens=200, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

[AttentionMaskVisualizer](https://github.com/huggingface/transformers/blob/beb9b5b02246b9b7ee81ddf938f93f44cfeaad19/src/transformers/utils/attention_visualizer.py#L139)를 사용하면 모델이 어떤 토큰에 주의를 기울일 수 있고 기울일 수 없는지를 더 잘 이해할 수 있습니다.

```py
from transformers.utils.attention_visualizer import AttentionMaskVisualizer

visualizer = AttentionMaskVisualizer("meta-llama/CodeLlama-7b-hf")
visualizer("""def func(a, b):
  return a + b""")
```

    

## 참고사항[[notes]]

- 인필링 기능은 7B 및 13B 기반 모델에서만 사용할 수 있으며, Python, Instruct, 34B 또는 70B 모델에서는 사용할 수 없습니다.
- 코드를 채워 넣고 싶은 부분에 `` 토큰을 사용하세요. 토크나이저는 이 토큰을 분할하여 [원본 훈련 패턴](https://github.com/facebookresearch/codellama/blob/cb51c14ec761370ba2e2bc351374a79265d0465e/llama/generation.py#L402) 을 따르는 입력 문자열로 변환합니다. 이는 직접 패턴을 준비하는 것보다 더 안정적입니다.
    ```py
    from transformers import LlamaForCausalLM, CodeLlamaTokenizer

    tokenizer = CodeLlamaTokenizer.from_pretrained("meta-llama/CodeLlama-7b-hf")
    model = LlamaForCausalLM.from_pretrained("meta-llama/CodeLlama-7b-hf")
    PROMPT = '''def remove_non_ascii(s: str) -> str:
        """ 
        return result
    '''
    input_ids = tokenizer(PROMPT, return_tensors="pt")["input_ids"]
    generated_ids = model.generate(input_ids, max_new_tokens=128)

    filling = tokenizer.batch_decode(generated_ids[:, input_ids.shape[1]:], skip_special_tokens = True)[0]
    print(PROMPT.replace("", filling))
    ```
- 추가 훈련이나 미세 조정에는 `bfloat16`을 사용하고 추론에는 `float16`을 사용하세요.
- `BOS` 문자는 접두사나 접미사를 인코딩할 때 인필링 작업에 사용되지 않으며, 각 프롬프트의 맨 앞에서만 사용됩니다.
- 토크나이저는 [SentencePiece](https://github.com/google/sentencepiece)를 기반으로 하는 byte-pair 인코딩 모델입니다. 디코딩 과정에서 첫 번째 토큰이 단어의 시작인 경우(예를 들어 "Banana"), 토크나이저는 문자열에 접두사 공백을 추가하지 않습니다.

## CodeLlamaTokenizer[[transformers.CodeLlamaTokenizer]]

#### transformers.CodeLlamaTokenizer[[transformers.CodeLlamaTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/code_llama/tokenization_code_llama.py#L41)

Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.

This uses notably ByteFallback and no normalization.

```python
>>> from transformers import CodeLlamaTokenizer

>>> tokenizer = CodeLlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
>>> tokenizer.encode("Hello this is a test")
[1, 15043, 445, 338, 263, 1243]
```

If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.

This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v5.5.2/ko/main_classes/tokenizer#transformers.TokenizersBackend) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. The default configuration match that of
[meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/blob/main/tokenizer_config.json)
which supports prompt infilling.

get_special_tokens_masktransformers.CodeLlamaTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/tokenization_utils_base.py#L1304[{"name": "token_ids_0", "val": ": list[int]"}, {"name": "token_ids_1", "val": ": list[int] | None = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- **token_ids_0** -- List of IDs for the (possibly already formatted) sequence.
- **token_ids_1** -- Unused when `already_has_special_tokens=True`. Must be None in that case.
- **already_has_special_tokens** -- Whether the sequence is already formatted with special tokens.0A list of integers in the range [0, 1]1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with `already_has_special_tokens=True` to build a mask over an
already-formatted sequence. In that case, we compute the mask by checking membership in `all_special_ids`.

**Parameters:**

clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`) : Whether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces.

unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token.

prefix_token (`str`, *optional*, defaults to `"▁"`) : Prefix token used for infilling.

middle_token (`str`, *optional*, defaults to `"▁"`) : Middle token used for infilling.

suffix_token (`str`, *optional*, defaults to `"▁"`) : Suffix token used for infilling.

eot_token (`str`, *optional*, defaults to `"▁"`) : End of text token used for infilling.

fill_token (`str`, *optional*, defaults to `""`) : The token used to split the input between the prefix and suffix.

additional_special_tokens (`list[str]`, *optional*) : Additional special tokens used by the tokenizer.

add_bos_token (`bool`, *optional*, defaults to `True`) : Whether to add a beginning of sequence token at the start of sequences.

add_eos_token (`bool`, *optional*, defaults to `False`) : Whether to add an end of sequence token at the end of sequences.

use_default_system_prompt (`bool`, *optional*, defaults to `False`) : Whether or not the default system prompt for Llama should be used.

add_prefix_space (`bool`, *optional*) : Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word.

vocab (`str`, `dict` or `list`, *optional*) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.

merges (`str` or `list`, *optional*) : Custom merges list. If not provided, merges are loaded from merges_file.

vocab_file (`str`, *optional*) : [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that contains the vocabulary necessary to instantiate a tokenizer.

**Returns:**

`A list of integers in the range [0, 1]`

1 for a special token, 0 for a sequence token.
#### save_vocabulary[[transformers.CodeLlamaTokenizer.save_vocabulary]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/tokenization_utils_tokenizers.py#L509)

## CodeLlamaTokenizerFast[[transformers.CodeLlamaTokenizer]]

#### transformers.CodeLlamaTokenizer[[transformers.CodeLlamaTokenizer]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/models/code_llama/tokenization_code_llama.py#L41)

Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.

This uses notably ByteFallback and no normalization.

```python
>>> from transformers import CodeLlamaTokenizer

>>> tokenizer = CodeLlamaTokenizer.from_pretrained("hf-internal-testing/llama-tokenizer")
>>> tokenizer.encode("Hello this is a test")
[1, 15043, 445, 338, 263, 1243]
```

If you want to change the `bos_token` or the `eos_token`, make sure to specify them when initializing the model, or
call `tokenizer.update_post_processor()` to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (https://huggingface.co/docs/tokenizers/api/post-processors) documentation.

This tokenizer inherits from [PreTrainedTokenizerFast](/docs/transformers/v5.5.2/ko/main_classes/tokenizer#transformers.TokenizersBackend) which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods. The default configuration match that of
[meta-llama/CodeLlama-7b-Instruct-hf](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf/blob/main/tokenizer_config.json)
which supports prompt infilling.

get_special_tokens_masktransformers.CodeLlamaTokenizer.get_special_tokens_maskhttps://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/tokenization_utils_base.py#L1304[{"name": "token_ids_0", "val": ": list[int]"}, {"name": "token_ids_1", "val": ": list[int] | None = None"}, {"name": "already_has_special_tokens", "val": ": bool = False"}]- **token_ids_0** -- List of IDs for the (possibly already formatted) sequence.
- **token_ids_1** -- Unused when `already_has_special_tokens=True`. Must be None in that case.
- **already_has_special_tokens** -- Whether the sequence is already formatted with special tokens.0A list of integers in the range [0, 1]1 for a special token, 0 for a sequence token.

Retrieve sequence ids from a token list that has no special tokens added.

For fast tokenizers, data collators call this with `already_has_special_tokens=True` to build a mask over an
already-formatted sequence. In that case, we compute the mask by checking membership in `all_special_ids`.

**Parameters:**

clean_up_tokenization_spaces (`str`, *optional*, defaults to `False`) : Whether to cleanup spaces after decoding, cleanup consists in removing potential artifacts like extra spaces.

unk_token (`str`, *optional*, defaults to `""`) : The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this token instead.

bos_token (`str`, *optional*, defaults to `""`) : The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

eos_token (`str`, *optional*, defaults to `""`) : The end of sequence token.

prefix_token (`str`, *optional*, defaults to `"▁"`) : Prefix token used for infilling.

middle_token (`str`, *optional*, defaults to `"▁"`) : Middle token used for infilling.

suffix_token (`str`, *optional*, defaults to `"▁"`) : Suffix token used for infilling.

eot_token (`str`, *optional*, defaults to `"▁"`) : End of text token used for infilling.

fill_token (`str`, *optional*, defaults to `""`) : The token used to split the input between the prefix and suffix.

additional_special_tokens (`list[str]`, *optional*) : Additional special tokens used by the tokenizer.

add_bos_token (`bool`, *optional*, defaults to `True`) : Whether to add a beginning of sequence token at the start of sequences.

add_eos_token (`bool`, *optional*, defaults to `False`) : Whether to add an end of sequence token at the end of sequences.

use_default_system_prompt (`bool`, *optional*, defaults to `False`) : Whether or not the default system prompt for Llama should be used.

add_prefix_space (`bool`, *optional*) : Whether or not to add an initial space to the input. This allows to treat the leading word just as any other word.

vocab (`str`, `dict` or `list`, *optional*) : Custom vocabulary dictionary. If not provided, vocabulary is loaded from vocab_file.

merges (`str` or `list`, *optional*) : Custom merges list. If not provided, merges are loaded from merges_file.

vocab_file (`str`, *optional*) : [SentencePiece](https://github.com/google/sentencepiece) file (generally has a .model extension) that contains the vocabulary necessary to instantiate a tokenizer.

**Returns:**

`A list of integers in the range [0, 1]`

1 for a special token, 0 for a sequence token.
#### update_post_processor[[transformers.CodeLlamaTokenizer.update_post_processor]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/tokenization_utils_tokenizers.py#L522)

Updates the underlying post processor with the current `bos_token` and `eos_token`.
#### save_vocabulary[[transformers.CodeLlamaTokenizer.save_vocabulary]]

[Source](https://github.com/huggingface/transformers/blob/v5.5.2/src/transformers/tokenization_utils_tokenizers.py#L509)