Instructions to use SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code with Transformers:

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

pipe = pipeline("text-generation", model="SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code")
model = AutoModelForCausalLM.from_pretrained("SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code

SGLang

How to use SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code 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 "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code" \
    --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": "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code",
		"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 "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code" \
        --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": "SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code with Docker Model Runner:
```
docker model run hf.co/SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code
```

graphCodeBert-finetuned-cXg-nl-to-code

This model is a fine-tuned version of microsoft/graphcodebert-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 8.5374
Rouge1: 0.0803
Rouge2: 0.0056
Rougel: 0.0590
Bleu: 0.7208
Meteor: 0.1076
Codebleu: 0.2064

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Framework versions

Transformers 4.42.4
Pytorch 2.3.1+cu121
Datasets 2.20.0
Tokenizers 0.19.1

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for SashaSheykina/graphCodeBert-finetuned-cXg-nl-to-code

Base model

microsoft/graphcodebert-base

Finetuned

(49)

this model