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--- |
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license: other |
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license_name: inf |
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license_link: https://huggingface.co/infly/OpenCoder-1.5B-Base/blob/main/LICENSE |
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language: |
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- en |
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- zh |
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base_model: infly/OpenCoder-1.5B-Base |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- code |
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--- |
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## Description |
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This model is derived from [OpenCoder-1.5B-Base](https://huggingface.co/infly/OpenCoder-1.5B-Base) by applying additional context extension fine-tuning. The repository context is composed using the _Half-memory `.py` irrelevant_ composer, more details on which, along with others, can be found in the [On Pretraining for Project-Level Code Completion](https://openreview.net/forum?id=t9RN9WX4Ic) paper ([arxiv](https://arxiv.org/abs/2510.13697)). Specifically, Section A.1 of the Appendix describes the context composition method, and Table 3 provides a comparison with other composers from the same [collection](https://huggingface.co/collections/JetBrains-Research/repository-level-pre-trained-opencoder-68e938c003be1cfba9c3595e). |
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We publish this checkpoint to support the reproducibility and accessibility of our research results. |
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## Quickstart |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "JetBrains-Research/OpenCoder-1.5B-Half-Memory-Py-Irrelevant" |
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tokenizer_name = "infly/OpenCoder-1.5B-Base" |
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model = AutoModelForCausalLM.from_pretrained(model_name, |
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torch_dtype=torch.bfloat16, |
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device_map="auto", |
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trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True) |
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inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt") |
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outputs = model.generate(**inputs.to(model.device), max_new_tokens=256) |
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result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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print(result) |
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``` |
