Instructions to use loubnabnl/santacoder-code-to-text with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use loubnabnl/santacoder-code-to-text with Transformers:

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

pipe = pipeline("text-generation", model="loubnabnl/santacoder-code-to-text", trust_remote_code=True)

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

tokenizer = AutoTokenizer.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("loubnabnl/santacoder-code-to-text", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use loubnabnl/santacoder-code-to-text with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/loubnabnl/santacoder-code-to-text

SGLang

How to use loubnabnl/santacoder-code-to-text 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 "loubnabnl/santacoder-code-to-text" \
    --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": "loubnabnl/santacoder-code-to-text",
		"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 "loubnabnl/santacoder-code-to-text" \
        --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": "loubnabnl/santacoder-code-to-text",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use loubnabnl/santacoder-code-to-text with Docker Model Runner:
```
docker model run hf.co/loubnabnl/santacoder-code-to-text
```

santacoder-code-to-text

Commit History

Nov 21, 2024

00:25
Adding `safetensors` variant of this model⁠

SFconvertbot

Verified signature
e7b56d9

Feb 2, 2023

10:16
Update config.json

loubnabnl HF Staff

6d71f0b
10:15
Update README.md

loubnabnl HF Staff

3e2b13c

Jan 24, 2023

19:41
Merge branch 'main' of https://huggingface.co/loubnabnl/santacoder-code-to-text into main⁠

loubnabnl HF Staff

6d92fc0
19:41
update tokenizer

loubnabnl HF Staff

021e008
19:17
Update README.md

loubnabnl HF Staff

da8dfb6
19:17
Update README.md

loubnabnl HF Staff

1c8ac75
19:09
add model

loubnabnl HF Staff

463d3d0
19:09
update gitattr

loubnabnl HF Staff

4b79626
18:50
initial commit

loubnabnl HF Staff

1251d24

Commit History

Nov 21, 2024

Adding `safetensors` variant of this model⁠

Feb 2, 2023

Update config.json

Update README.md

Jan 24, 2023

Merge branch 'main' of https://huggingface.co/loubnabnl/santacoder-code-to-text into main⁠

update tokenizer

Update README.md

Update README.md

add model

update gitattr

initial commit