metadata
license: other
license_name: inf
license_link: https://huggingface.co/infly/OpenCoder-1.5B-Base/blob/main/LICENSE
language:
- en
- zh
base_model: infly/OpenCoder-1.5B-Base
pipeline_tag: text-generation
library_name: transformers
tags:
- code
Description
This model is derived from 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 paper (arxiv). 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.
We publish this checkpoint to support the reproducibility and accessibility of our research results.
Quickstart
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "JetBrains-Research/OpenCoder-1.5B-Half-Memory-Py-Irrelevant"
tokenizer_name = "infly/OpenCoder-1.5B-Base"
model = AutoModelForCausalLM.from_pretrained(model_name,
torch_dtype=torch.bfloat16,
device_map="auto",
trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(tokenizer_name, trust_remote_code=True)
inputs = tokenizer("# write a quick sort algorithm", return_tensors="pt")
outputs = model.generate(**inputs.to(model.device), max_new_tokens=256)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)