Instructions to use codeparrot/codeparrot-small-multi with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use codeparrot/codeparrot-small-multi with Transformers:

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

pipe = pipeline("text-generation", model="codeparrot/codeparrot-small-multi")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small-multi")
model = AutoModelForCausalLM.from_pretrained("codeparrot/codeparrot-small-multi")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codeparrot/codeparrot-small-multi with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/codeparrot/codeparrot-small-multi

SGLang

How to use codeparrot/codeparrot-small-multi 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 "codeparrot/codeparrot-small-multi" \
    --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": "codeparrot/codeparrot-small-multi",
		"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 "codeparrot/codeparrot-small-multi" \
        --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": "codeparrot/codeparrot-small-multi",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use codeparrot/codeparrot-small-multi with Docker Model Runner:
```
docker model run hf.co/codeparrot/codeparrot-small-multi
```

loubnabnl HF Staff commited on Jul 4, 2022

Commit

cd5d83c

1 Parent(s): 8c0a205

Update eval_results.txt

Browse files

Files changed (1) hide show

eval_results.txt +4 -1

eval_results.txt CHANGED Viewed

@@ -1,2 +1,5 @@
 temperature 0.2: {'pass@1': 0.006436314363143637, 'pass@10': 0.017889215840761093, 'pass@100': 0.018292682926829267}
-temperature 0.8: {'pass@1': 0.0042682926829268305, 'pass@10': 0.023148428817837293, 'pass@100': 0.050225398989338275}

+step 150k
 temperature 0.2: {'pass@1': 0.006436314363143637, 'pass@10': 0.017889215840761093, 'pass@100': 0.018292682926829267}
+temperature 0.8: {'pass@1': 0.0042682926829268305, 'pass@10': 0.023148428817837293, 'pass@100': 0.050225398989338275}
+step 250k
+temperature 0.8: {'pass@1': 0.007723577235772357, 'pass@10': 0.03226252154582702, 'pass@100': 0.06587111162129897}