Instructions to use yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm with Transformers:

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

pipe = pipeline("text-generation", model="yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm")
model = AutoModelForCausalLM.from_pretrained("yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm

SGLang

How to use yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm 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 "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm" \
    --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": "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm",
		"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 "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm" \
        --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": "yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm with Docker Model Runner:
```
docker model run hf.co/yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm
```

microsoft_CodeGPT-small-java_0_ft_clm

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

Loss: 1.1672
Accuracy: 0.7708

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: 8e-05
train_batch_size: 4
eval_batch_size: 12
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 4
total_train_batch_size: 32
total_eval_batch_size: 24
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.02
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.8842	0.7788	500	1.1934	0.7688
0.7557	1.5576	1000	1.1676	0.7693
0.7033	2.3364	1500	1.1631	0.7705
0.6624	3.1153	2000	1.1650	0.7707
0.6414	3.8941	2500	1.1646	0.7710
0.6187	4.6729	3000	1.1672	0.7708

Framework versions

Transformers 4.53.0
Pytorch 2.7.1+cu126
Datasets 3.6.0
Tokenizers 0.21.2

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

BF16

Model tree for yanghuattt/microsoft_CodeGPT-small-java_0_ft_clm

Base model

microsoft/CodeGPT-small-java

Finetuned

(2)

this model