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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

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

Use Docker

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

SGLang

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

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

jlpan commited on Jul 5, 2023

Commit

80a680e

1 Parent(s): d51cd45

Training in progress, step 500

Browse files

Files changed (3) hide show

pytorch_model.bin +1 -1
runs/Jul05_17-12-06_025ba2434497/events.out.tfevents.1688577135.025ba2434497.803.0 +3 -0
training_args.bin +1 -1

pytorch_model.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:2136f3d76da5fc00fef6fe52ef096ac5384957398b294a367e436f0a7e17098a
 size 4600336581

 version https://git-lfs.github.com/spec/v1
+oid sha256:522c6ca94b9be91a82835d2278cf11fb1e088528f7698e73f3ca0c967ff46a40
 size 4600336581

runs/Jul05_17-12-06_025ba2434497/events.out.tfevents.1688577135.025ba2434497.803.0 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:9edbc0032263bc65420f049f7d445cf1acdaa69be535eb9214c07b6c2f1a8e2e
+size 12542

training_args.bin CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:daa3d27362a46465b4df861f423e3efb267eb1bf99c162eda1c052017a37ab1c
 size 3963

 version https://git-lfs.github.com/spec/v1
+oid sha256:e448519d08ccda02738e7a554848d88c1bea2469ac22974cb70ceb7a8d82d8c5
 size 3963