Instructions to use facebook/incoder-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use facebook/incoder-1B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="facebook/incoder-1B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("facebook/incoder-1B") model = AutoModelForCausalLM.from_pretrained("facebook/incoder-1B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use facebook/incoder-1B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "facebook/incoder-1B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/incoder-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/facebook/incoder-1B
- SGLang
How to use facebook/incoder-1B with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "facebook/incoder-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/incoder-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "facebook/incoder-1B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "facebook/incoder-1B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use facebook/incoder-1B with Docker Model Runner:
docker model run hf.co/facebook/incoder-1B
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# InCoder 1B
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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.
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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 were primarily on Python and JavaScript, but also include code from 28 languages, as well as StackOverflow.
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For more information, see our:
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- [Demo](https://huggingface.co/spaces/facebook/incoder-demo)
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- [Project site](https://sites.google.com/view/incoder-code-models)
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- [Examples](https://sites.google.com/view/incoder-code-models/home/examples)
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- [Paper](https://github.com/dpfried/incoder/blob/main/paper/InCoder.pdf)
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A larger, 6B, parameter model is also available at [facebook/incoder-6B](https://huggingface.co/facebook/incoder-6B).
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## Requirements
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`pytorch`, `tokenizers`, and `transformers`. Our model requires HF's tokenizers >= 0.12.1, due to changes in the pretokenizer. This version is close to release, but in the meantime you can install directly from source via pip.
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```
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pip install pytorch
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pip install git+https://github.com/huggingface/tokenizers
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pip install git+https://github.com/huggingface/transformers
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```
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## Usage
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See [https://github.com/dpfried/incoder](https://github.com/dpfried/incoder) for example code.
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## Credits
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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.
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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!
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