Instructions to use Yulwoo/santacoder-finetuned-the-stack-bash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yulwoo/santacoder-finetuned-the-stack-bash with Transformers:

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

pipe = pipeline("text-generation", model="Yulwoo/santacoder-finetuned-the-stack-bash", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yulwoo/santacoder-finetuned-the-stack-bash", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Yulwoo/santacoder-finetuned-the-stack-bash", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yulwoo/santacoder-finetuned-the-stack-bash with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Yulwoo/santacoder-finetuned-the-stack-bash

SGLang

How to use Yulwoo/santacoder-finetuned-the-stack-bash 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 "Yulwoo/santacoder-finetuned-the-stack-bash" \
    --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": "Yulwoo/santacoder-finetuned-the-stack-bash",
		"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 "Yulwoo/santacoder-finetuned-the-stack-bash" \
        --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": "Yulwoo/santacoder-finetuned-the-stack-bash",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yulwoo/santacoder-finetuned-the-stack-bash with Docker Model Runner:
```
docker model run hf.co/Yulwoo/santacoder-finetuned-the-stack-bash
```

santacoder-finetuned-the-stack-bash / training_args.bin

Yulwoo

Training in progress, step 10

00eadb7 about 3 years ago

History Blame Contribute Delete

4.03 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f689276be2d463b3e2a1295ca1e7c2683e326b1bfad2fcd3f916cf088382e917
Size of remote file:: 4.03 kB
SHA256:: ce16c913cbcc14f81089504caf4f81a810a7148f348211813338da74d957ff8f

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.

Yulwoo
/

santacoder-finetuned-the-stack-bash

Detected Pickle imports (8)

Xet Pointer Details