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

File size: 1,270 Bytes

56a61a9

---
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: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# java-starcoder

This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/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