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

Libraries

How to use stillerman/santacoder-ruby with Transformers:

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

pipe = pipeline("text-generation", model="stillerman/santacoder-ruby", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stillerman/santacoder-ruby", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("stillerman/santacoder-ruby", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use stillerman/santacoder-ruby with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/stillerman/santacoder-ruby

SGLang

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

Docker Model Runner
How to use stillerman/santacoder-ruby with Docker Model Runner:
```
docker model run hf.co/stillerman/santacoder-ruby
```

stillerman commited on Feb 19, 2023

Commit

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1 Parent(s): 83d6fca

Create README.md

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+## Model
+This model is a fine-tuned version of [BigCode/SantaCoder](https://huggingface.co/bigcode/santacoder) on the Ruby portion of [The Stack](https://huggingface.co/datasets/bigcode/the-stack-dedup).
+## Training
+This model was trained using character-level FIM with [this script](https://github.com/Stillerman/santacoder-finetuning) invoked like this
+```
+train.py --model_path=bigcode/santacoder --dataset_name=bigcode/the-stack-dedup \
+  --subset=data/ruby --data_column content --split=train \
+  --seq_length 2048 --max_steps 4000 --batch_size 3 \
+  --gradient_accumulation_steps 8 --learning_rate 5e-5 \
+  --num_warmup_steps 500 --eval_freq 1000 --save_freq 1000 \
+  --log_freq 1 --num_workers=12 --no_fp16 --streaming \
+  --fim_rate=0.5 --fim_spm_rate=0.5
+```
+on a 40GB A100 for 48 hours.
+## Performance
+[MultiPL-E](https://nuprl.github.io/MultiPL-E/) HumanEval Ruby pass@1 = 0.10