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
```

Santacoder finetuned on The-Stack-dedup (GLSL subset) for 1000 steps with a batch size of 2 and full sequence length of 2048. adapted finetuning script found here

Finetuning parameters

python3 train.py --model_path "bigcode/santacoder" \
--dataset_name "bigcode/the-stack-dedup" \
--subset "data/glsl" \
--data_column "content" \
--split "train" \
--seq_length 2048 \
--max_steps 1000 \
--batch_size 2 \
--gradient_accumulation_steps 4 \
--learning_rate 5e-5 \
--num_warmup_steps 100 \
--eval_freq 100 \
--save_freq 100 \
--log_freq 1 \
--output_dir "checkpoint_dir" \
--no_fp16

Main purpose of this model is to explore if finetuning models improves performance on ShaderEval, which reached 0.380 with 300 samples.

License carried over from model, and the finetuning dataset holds the same license.

Downloads last month: 28

Safetensors

Model size

1B params

Tensor type

F32

Model tree for Vipitis/santacoder-finetuned-the-stack-glsl

Base model

bigcode/santacoder

Finetuned

(15)

this model

Dataset used to train Vipitis/santacoder-finetuned-the-stack-glsl

Space using Vipitis/santacoder-finetuned-the-stack-glsl 1

Collection including Vipitis/santacoder-finetuned-the-stack-glsl

models to evaluate

Collection

collecting models I want to evaluate on shadereval-task2: https://github.com/bigcode-project/bigcode-evaluation-harness/pull/173 at fp16!! • 39 items • Updated Nov 17, 2024 • 3

Evaluation results

300 samples, greedy decoding on Shadertoys-fine
self-reported

0.380