Instructions to use mrm8488/santacoder-finetuned-the-stack-bash-4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 with Transformers:

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

pipe = pipeline("text-generation", model="mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("mrm8488/santacoder-finetuned-the-stack-bash-4", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrm8488/santacoder-finetuned-the-stack-bash-4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrm8488/santacoder-finetuned-the-stack-bash-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-4

SGLang

How to use mrm8488/santacoder-finetuned-the-stack-bash-4 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 "mrm8488/santacoder-finetuned-the-stack-bash-4" \
    --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": "mrm8488/santacoder-finetuned-the-stack-bash-4",
		"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 "mrm8488/santacoder-finetuned-the-stack-bash-4" \
        --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": "mrm8488/santacoder-finetuned-the-stack-bash-4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrm8488/santacoder-finetuned-the-stack-bash-4 with Docker Model Runner:
```
docker model run hf.co/mrm8488/santacoder-finetuned-the-stack-bash-4
```

santacoder-finetuned-the-stack-bash-4

Commit History

Sep 11, 2023

14:30
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Commit History

Sep 11, 2023

Librarian Bot: Add base_model information to model⁠

Feb 2, 2023

Update config.json

Jan 25, 2023

Upload 2 files

Jan 23, 2023

Upload tokenizer

End of training

Training in progress, step 5000

Training in progress, step 4500

Training in progress, step 4000

Training in progress, step 3500

Training in progress, step 3000

Training in progress, step 2500

Training in progress, step 2000

Training in progress, step 1500

Training in progress, step 1000

Training in progress, step 500

Jan 22, 2023

initial commit