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

Libraries

How to use Vipitis/santacoder-finetuned-the-stack-glsl with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

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

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Vipitis/santacoder-finetuned-the-stack-glsl with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Vipitis/santacoder-finetuned-the-stack-glsl

SGLang

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

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

Vipitis commited on May 6, 2023

Commit

39391f2

1 Parent(s): e7a02df

Update README.md

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@@ -1,3 +1,15 @@
 ---
 license: bigcode-openrail-m
 ---

 ---
 license: bigcode-openrail-m
+datasets:
+- bigcode/the-stack-dedup
+pipeline_tag: text-generation
+tags:
+- code
 ---
+[Santacoder](https://huggingface.co/bigcode/santacoder) finetuned on [Shadertoys](https://huggingface.co/datasets/Vipitis/Shadertoys) for 1000 steps with a batch size of 2 and full sequence length of 2048.
+Origianl finetuning script from found [here](https://github.com/loubnabnl/santacoder-finetuning), adapted version to follow (soon^^).
+Main purpose of this model is to explore if finetuning models improves performance on [ShaderEval](https://huggingface.co/spaces/Vipitis/ShaderEval), results to follow (sooner).
+License carried over from model, and the finetuning dataset holds the same license.