Instructions to use stabilityai/stablecode-completion-alpha-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use stabilityai/stablecode-completion-alpha-3b with Transformers:

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

pipe = pipeline("text-generation", model="stabilityai/stablecode-completion-alpha-3b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablecode-completion-alpha-3b")
model = AutoModelForCausalLM.from_pretrained("stabilityai/stablecode-completion-alpha-3b")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use stabilityai/stablecode-completion-alpha-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "stabilityai/stablecode-completion-alpha-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "stabilityai/stablecode-completion-alpha-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/stabilityai/stablecode-completion-alpha-3b

SGLang

How to use stabilityai/stablecode-completion-alpha-3b 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 "stabilityai/stablecode-completion-alpha-3b" \
    --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": "stabilityai/stablecode-completion-alpha-3b",
		"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 "stabilityai/stablecode-completion-alpha-3b" \
        --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": "stabilityai/stablecode-completion-alpha-3b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use stabilityai/stablecode-completion-alpha-3b with Docker Model Runner:
```
docker model run hf.co/stabilityai/stablecode-completion-alpha-3b
```

This would be a perfect model to fine tune of my dataset!!!

by rombodawg - opened Aug 8, 2023

Discussion

rombodawg

Aug 8, 2023

•

edited Aug 8, 2023

I have a new dataset that is meant to train models for coding without losing their ability to do other functions (Lossless) My dataset consists of roughly 25% coding instruct data and 75% non coding instruct data and i think it would be perfect to further fine tune your model.

If you do end up using my data, feel free to call your model whatever you want, but my suggestion is stablemegacode-instruct-alpha-3b

Dataset link:
https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored

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