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
```

ncoop57 commited on Aug 7, 2023

Commit

fdc3e2e

1 Parent(s): 494b1bf

Update README.md

Browse files

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README.md +4 -2

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ 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.
@@ -21,7 +21,7 @@ Get started generating code with `StableCode-Completion-Alpha-3B` by using the f
 from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablecode-completion-alpha-3b")
 model = AutoModelForCausalLM.from_pretrained(
-  "stabilityai/stablelm-base-alpha-3b-v2",
   trust_remote_code=True,
   torch_dtype="auto",
 )
@@ -74,6 +74,8 @@ The model is pre-trained on the dataset mixes mentioned above in mixed-precision
 ### Intended Use
 ### Limitations and bias

 ## 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 were the top used languages based on the 2023 stackoverflow developer survey.
 ## Usage
 The model is intended to do single/multiline code completion from a long context window upto 16k tokens.
 from transformers import AutoModelForCausalLM, AutoTokenizer
 tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablecode-completion-alpha-3b")
 model = AutoModelForCausalLM.from_pretrained(
+  "stabilityai/stablecode-completion-alpha-3b",
   trust_remote_code=True,
   torch_dtype="auto",
 )
 ### Intended Use
+These models are intended to be used by developers and researchers as foundational models for application-specific fine-tuning.
 ### Limitations and bias
+The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model-generated text. We recommend that users exercise caution when using these models in production systems. Do not use the models for any applications that may cause harm or distress to individuals or groups.