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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

mrm8488 commited on Feb 9, 2023

Commit

d7394bd

1 Parent(s): 99b9470

Update README.md

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@@ -83,3 +83,15 @@ The following hyperparameters were used during training:
 - Pytorch 1.13.1+cu116
 - Datasets 2.7.1
 - Tokenizers 0.13.2

 - Pytorch 1.13.1+cu116
 - Datasets 2.7.1
 - Tokenizers 0.13.2
+### Citation
+```
+@misc {manuel_romero_2023,
+	author       = { {Manuel Romero} },
+	title        = { santacoder-finetuned-the-stack-swift (Revision 99b9470) },
+	year         = 2023,
+	url          = { https://huggingface.co/mrm8488/santacoder-finetuned-the-stack-swift },
+	doi          = { 10.57967/hf/0348 },
+	publisher    = { Hugging Face }
+}
+```