
# InCoder 1B

A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.

The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or BSD-3) from GitHub and GitLab, as well as StackOverflow. Repositories primarily contained Python and JavaScript, but also include code from 28 languages, as well as StackOverflow. 

For more information, see our:

- [Demo](https://huggingface.co/spaces/facebook/incoder-demo)
- [Project site](https://sites.google.com/view/incoder-code-models)
- [Examples](https://sites.google.com/view/incoder-code-models/home/examples)
- [Paper](https://arxiv.org/abs/2204.05999)

A larger, 6B, parameter model is also available at [facebook/incoder-6B](https://huggingface.co/facebook/incoder-6B).

## Requirements

`pytorch`, `tokenizers`, and `transformers`. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer.

```
pip install torch
pip install "tokenizers>=0.12.1"
pip install transformers
```

## Usage

See [https://github.com/dpfried/incoder](https://github.com/dpfried/incoder) for example code.

### Model
`model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B")`

### Tokenizer
`tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-1B")`

(Note: the incoder-1B and incoder-6B tokenizers are identical, so 'facebook/incoder-6B' could also be used.)

When calling `tokenizer.decode`, it's important to pass `clean_up_tokenization_spaces=False` to avoid removing spaces after punctuation. For example:

`tokenizer.decode(tokenizer.encode("from ."), clean_up_tokenization_spaces=False)`

(Note: encoding prepends the `<|endoftext|>` token, as this marks the start of a document to our model. This token can be removed from the decoded output by passing `skip_special_tokens=True` to `tokenizer.decode`.)

## License

CC-BY-NC 4.0

## Credits

The model was developed by Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer and Mike Lewis.

Thanks to Lucile Saulnier, Leandro von Werra, Nicolas Patry, Suraj Patil, Omar Sanseviero, and others at HuggingFace for help with the model release, and to Naman Goyal and Stephen Roller for the code our demo was based on!
{"license": "cc-by-nc-4.0", "tags": ["code", "python", "javascript"]}
transformers pytorch xglm text-generation code python javascript arxiv:2204.05999 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 


# InCoder 1B

A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.

The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or BSD-3) from GitHub and GitLab, as well as StackOverflow. Repositories primarily contained Python and JavaScript, but also include code from 28 languages, as well as StackOverflow. 

For more information, see our:

- Demo
- Project site
- Examples
- Paper

A larger, 6B, parameter model is also available at facebook/incoder-6B.

## Requirements

'pytorch', 'tokenizers', and 'transformers'. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer.



## Usage

See URL for example code.

### Model
'model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B")'

### Tokenizer
'tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-1B")'

(Note: the incoder-1B and incoder-6B tokenizers are identical, so 'facebook/incoder-6B' could also be used.)

When calling 'URL', it's important to pass 'clean_up_tokenization_spaces=False' to avoid removing spaces after punctuation. For example:

'URL(URL("from ."), clean_up_tokenization_spaces=False)'

(Note: encoding prepends the '<|endoftext|>' token, as this marks the start of a document to our model. This token can be removed from the decoded output by passing 'skip_special_tokens=True' to 'URL'.)

## License

CC-BY-NC 4.0

## Credits

The model was developed by Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer and Mike Lewis.

Thanks to Lucile Saulnier, Leandro von Werra, Nicolas Patry, Suraj Patil, Omar Sanseviero, and others at HuggingFace for help with the model release, and to Naman Goyal and Stephen Roller for the code our demo was based on!
# InCoder 1B

A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.

The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or BSD-3) from GitHub and GitLab, as well as StackOverflow. Repositories primarily contained Python and JavaScript, but also include code from 28 languages, as well as StackOverflow. 

For more information, see our:

- Demo
- Project site
- Examples
- Paper

A larger, 6B, parameter model is also available at facebook/incoder-6B. ## Requirements

'pytorch', 'tokenizers', and 'transformers'. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer. ## Usage

See URL for example code. ### Model
'model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B")' ### Tokenizer
'tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-1B")'

(Note: the incoder-1B and incoder-6B tokenizers are identical, so 'facebook/incoder-6B' could also be used.)

When calling 'URL', it's important to pass 'clean_up_tokenization_spaces=False' to avoid removing spaces after punctuation. For example:

'URL(URL("from ."), clean_up_tokenization_spaces=False)'

(Note: encoding prepends the '<|endoftext|>' token, as this marks the start of a document to our model. This token can be removed from the decoded output by passing 'skip_special_tokens=True' to 'URL'.) ## License

CC-BY-NC 4.0 ## Credits

The model was developed by Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer and Mike Lewis.

Thanks to Lucile Saulnier, Leandro von Werra, Nicolas Patry, Suraj Patil, Omar Sanseviero, and others at HuggingFace for help with the model release, and to Naman Goyal and Stephen Roller for the code our demo was based on!
TAGS
#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 # InCoder 1B

A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.

The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or BSD-3) from GitHub and GitLab, as well as StackOverflow. Repositories primarily contained Python and JavaScript, but also include code from 28 languages, as well as StackOverflow. 

For more information, see our:

- Demo
- Project site
- Examples
- Paper

A larger, 6B, parameter model is also available at facebook/incoder-6B. ## Requirements

'pytorch', 'tokenizers', and 'transformers'. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer. ## Usage

See URL for example code. ### Model
'model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B")' ### Tokenizer
'tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-1B")'

(Note: the incoder-1B and incoder-6B tokenizers are identical, so 'facebook/incoder-6B' could also be used.)

When calling 'URL', it's important to pass 'clean_up_tokenization_spaces=False' to avoid removing spaces after punctuation. For example:

'URL(URL("from ."), clean_up_tokenization_spaces=False)'

(Note: encoding prepends the '<|endoftext|>' token, as this marks the start of a document to our model. This token can be removed from the decoded output by passing 'skip_special_tokens=True' to 'URL'.) ## License

CC-BY-NC 4.0 ## Credits

The model was developed by Daniel Fried, Armen Aghajanyan, Jessy Lin, Sida Wang, Eric Wallace, Freda Shi, Ruiqi Zhong, Wen-tau Yih, Luke Zettlemoyer and Mike Lewis.

Thanks to Lucile Saulnier, Leandro von Werra, Nicolas Patry, Suraj Patil, Omar Sanseviero, and others at HuggingFace for help with the model release, and to Naman Goyal and Stephen Roller for the code our demo was based on!
passage: TAGS
#transformers #pytorch #xglm #text-generation #code #python #javascript #arxiv-2204.05999 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #region-us 
# InCoder 1B

A 1B parameter decoder-only Transformer model trained on code using a causal-masked objective, which allows inserting/infilling code as well as standard left-to-right generation.

The model was trained on public open-source repositories with a permissive, non-copyleft, license (Apache 2.0, MIT, BSD-2 or BSD-3) from GitHub and GitLab, as well as StackOverflow. Repositories primarily contained Python and JavaScript, but also include code from 28 languages, as well as StackOverflow. 

For more information, see our:

- Demo
- Project site
- Examples
- Paper

A larger, 6B, parameter model is also available at facebook/incoder-6B.## Requirements

'pytorch', 'tokenizers', and 'transformers'. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer.## Usage

See URL for example code.### Model
'model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B")'