Instructions to use JetBrains-Research/cmg-codereviewer-with-history with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JetBrains-Research/cmg-codereviewer-with-history with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="JetBrains-Research/cmg-codereviewer-with-history")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("JetBrains-Research/cmg-codereviewer-with-history")
model = AutoModelForSeq2SeqLM.from_pretrained("JetBrains-Research/cmg-codereviewer-with-history")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use JetBrains-Research/cmg-codereviewer-with-history with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JetBrains-Research/cmg-codereviewer-with-history"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JetBrains-Research/cmg-codereviewer-with-history",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/JetBrains-Research/cmg-codereviewer-with-history

SGLang

How to use JetBrains-Research/cmg-codereviewer-with-history 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 "JetBrains-Research/cmg-codereviewer-with-history" \
    --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": "JetBrains-Research/cmg-codereviewer-with-history",
		"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 "JetBrains-Research/cmg-codereviewer-with-history" \
        --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": "JetBrains-Research/cmg-codereviewer-with-history",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use JetBrains-Research/cmg-codereviewer-with-history with Docker Model Runner:
```
docker model run hf.co/JetBrains-Research/cmg-codereviewer-with-history
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

CMG/CMC: CodeReviewer (with history)

This is the checkpoint for CodeReviewer model, fine-tuned for the commit message generation (and/or completion) task as part of the paper "From Commit Message Generation to History-Aware Commit Message Completion", ASE 2023.

Details

🔍 For further details, please refer to:

Paper: https://arxiv.org/abs/2308.07655

Repository: https://github.com/JetBrains-Research/commit_message_generation

This model is based on microsoft/codereviewer checkpoint from 📜 Automating Code Review Activities by Large-Scale Pre-training.
This model was trained with commit diffs as well as WITH commit message history.
This model was trained on the CommitChronicle dataset introduced in our study.

Our hyperparameter setting is mostly based on 📜 RACE: Retrieval-augmented Commit Message Generation. The exact values are provided below:

Hyperparameter	Value
Encoder context max length	512
Decoder context max length	512
Number of training epochs	1
Batch size	32
Optimizer	AdamW
Warmup	Linear
Number of warmup steps	100
Peak learning rate	0.00002