Instructions to use krytonguard/java-starcoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use krytonguard/java-starcoder with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("bigcode/starcoderbase-1b")
model = PeftModel.from_pretrained(base_model, "krytonguard/java-starcoder")

Transformers

How to use krytonguard/java-starcoder with Transformers:

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

pipe = pipeline("text-generation", model="krytonguard/java-starcoder")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("krytonguard/java-starcoder", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use krytonguard/java-starcoder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/krytonguard/java-starcoder

SGLang

How to use krytonguard/java-starcoder 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 "krytonguard/java-starcoder" \
    --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": "krytonguard/java-starcoder",
		"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 "krytonguard/java-starcoder" \
        --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": "krytonguard/java-starcoder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use krytonguard/java-starcoder with Docker Model Runner:
```
docker model run hf.co/krytonguard/java-starcoder
```

java-starcoder / README.md

krytonguard

End of training

56a61a9 verified 6 months ago

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metadata

library_name: peft
license: bigcode-openrail-m
base_model: bigcode/starcoderbase-1b
tags:
  - base_model:adapter:bigcode/starcoderbase-1b
  - lora
  - transformers
pipeline_tag: text-generation
model-index:
  - name: java-starcoder
    results: []

java-starcoder

This model is a fine-tuned version of bigcode/starcoderbase-1b on an unknown dataset.

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: 5e-05
train_batch_size: 1
eval_batch_size: 1
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
training_steps: 20

Training results

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.9.0
Datasets 4.4.1
Tokenizers 0.22.1