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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

reshinthadith commited on Aug 7, 2023

Commit

e9af0ee

1 Parent(s): b2ce6ac

Update README.md

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README.md +5 -3

README.md CHANGED Viewed

@@ -11,7 +11,9 @@ license: cc-by-sa-4.0
 ## Model Description
-`StableCode-Completion-Alpha-3B` is a 3 billion parameter decoder-only code completion model pre-trained on diverse set of programming languages that topped the stackoverflow developer survey. ## Usage
 The model is intended to do single/multiline code completion from a long context window upto 16k tokens.
 Get started generating code with `StableCode-Completion-Alpha-3B` by using the following code snippet:
@@ -38,7 +40,7 @@ print(tokenizer.decode(tokens[0], skip_special_tokens=True))
 * **Developed by**: Code.AI Team @ [Stability AI](https://stability.ai/)
 * **Model type**: `StableCode-Completion-Alpha-3B` models are auto-regressive language models based on the transformer decoder architecture.
-* **Language(s)**: English, Code
 * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
 * **License**: Model checkpoints are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under this license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use.
 * **Contact**: For questions and comments about the model, please email `lm@stability.ai`
@@ -60,7 +62,7 @@ print(tokenizer.decode(tokens[0], skip_special_tokens=True))
 ### Training Dataset
-The first pre-training stage relies on 300B tokens sourced from various top programming languages occuring in the stackoverflow developer survey. We then finetune it on a longer context augmentation of `starcoder-data.
 ### Training Procedure

 ## Model Description
+`StableCode-Completion-Alpha-3B` is a 3 billion parameter decoder-only code completion model pre-trained on diverse set of programming languages that topped the stackoverflow developer survey.
+## Usage
 The model is intended to do single/multiline code completion from a long context window upto 16k tokens.
 Get started generating code with `StableCode-Completion-Alpha-3B` by using the following code snippet:
 * **Developed by**: Code.AI Team @ [Stability AI](https://stability.ai/)
 * **Model type**: `StableCode-Completion-Alpha-3B` models are auto-regressive language models based on the transformer decoder architecture.
+* **Language(s)**: Code
 * **Library**: [GPT-NeoX](https://github.com/EleutherAI/gpt-neox)
 * **License**: Model checkpoints are licensed under the Creative Commons license ([CC BY-SA-4.0](https://creativecommons.org/licenses/by-sa/4.0/)). Under this license, you must give [credit](https://creativecommons.org/licenses/by/4.0/#) to Stability AI, provide a link to the license, and [indicate if changes were made](https://creativecommons.org/licenses/by/4.0/#). You may do so in any reasonable manner, but not in any way that suggests the Stability AI endorses you or your use.
 * **Contact**: For questions and comments about the model, please email `lm@stability.ai`
 ### Training Dataset
+The first pre-training stage relies on 300B tokens sourced from various top programming languages occuring in the stackoverflow developer survey in the `starcoder-data` dataset. We then finetune it on a longer context augmentation of `starcoder-data` dataset.
 ### Training Procedure