Update README.md

README.md

@@ -29,13 +29,31 @@ tags:
 
 This model is derived from [OpenCoder-1.5B-Base](https://huggingface.co/infly/OpenCoder-1.5B-Base) by applying an additional context extension fine-tuning with an Adjustment of the Base Frequency parameter of RoPE from 10,000 to 500,000. The number of optimization steps is 512 with a batch size of 128 on sequences of 16,384 length. The repository context is composed based on the _Path Distance_ heuristics, more details on which and other aspects including all code used can be found on the [Home Page](https://github.com/sapromak/adaptive-code-completion) of the project. Note that this model is created with the intent to answer specific research questions and __not__ to gain the maximum possible performance on the repository-level code completion setup. Consider it more as a baseline.
 
+The associated research was initialized and conducted by the [JetBrains Research](https://huggingface.co/JetBrains-Research) association.
+
 <div align="center">
-  <img src="https://github.com/sapromak/adaptive-code-completion/blob/main/paper/figures/compilation/beyond-training-window/beyond-training-window-inproject.svg?raw=true" width="70%" alt="Performance" />
+  <img src="https://github.com/sapromak/adaptive-code-completion/blob/main/paper/figures/compilation/beyond-training-window/beyond-training-window-inproject.svg?raw=true" width="100%" alt="Performance" />
   <p>Exact Match on the <em>inproject</em> lines of the <em>large-context</em> subset of the <a href="https://huggingface.co/datasets/JetBrains-Research/lca-project-level-code-completion">Project-Level Code Completion task</a> from the <a href="https://arxiv.org/abs/2406.11612">Long Code Arena benchmark</a>. This checkpoint (solid orange curve) demonstrates its best performance at a context length of 32,768. "1K" refers to 1,024 tokens. The star markers denote the context length used during the repository-level pre-training stage.
 </div>
 
 ## Quickstart
 
 ```python
-# TODO
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sapromak/OpenCoder-1.5B-Base-32K-via-16K"
+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)
 ```