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

Libraries

How to use codeparrot/codeparrot-small with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use codeparrot/codeparrot-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "codeparrot/codeparrot-small"
# 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",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

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

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

lvwerra HF Staff commited on Jun 24, 2022

Commit

6d1687a

1 Parent(s): 505bd87

update lvwerra namespace

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -9,8 +9,8 @@ You can load the CodeParrot model and tokenizer directly in `transformers`:
 ```Python
 from transformers import AutoTokenizer, AutoModelWithLMHead
-tokenizer = AutoTokenizer.from_pretrained("lvwerra/codeparrot-small")
-model = AutoModelWithLMHead.from_pretrained("lvwerra/codeparrot-small")
 inputs = tokenizer("def hello_world():", return_tensors="pt")
 outputs = model(**inputs)
@@ -21,13 +21,13 @@ or with a `pipeline`:
 ```Python
 from transformers import pipeline
-pipe = pipeline("text-generation", model="lvwerra/codeparrot-small")
 outputs = pipe("def hello_world():")
 ```
 ## Training
-The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/lvwerra/codeparrot-clean) with the following settings:
 |Config|Value|
 |-------|-----|
@@ -57,6 +57,6 @@ The [pass@k metric](https://huggingface.co/metrics/code_eval) tells the probabil
 ## Resources
-- Dataset: [full](https://huggingface.co/datasets/lvwerra/codeparrot-clean), [train](https://huggingface.co/datasets/lvwerra/codeparrot-clean-train), [valid](https://huggingface.co/datasets/lvwerra/codeparrot-clean-valid)
 - Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
 - Spaces: [generation](), [highlighting]()

 ```Python
 from transformers import AutoTokenizer, AutoModelWithLMHead
+tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
+model = AutoModelWithLMHead.from_pretrained("codeparrot/codeparrot-small")
 inputs = tokenizer("def hello_world():", return_tensors="pt")
 outputs = model(**inputs)
 ```Python
 from transformers import pipeline
+pipe = pipeline("text-generation", model="codeparrot/codeparrot-small")
 outputs = pipe("def hello_world():")
 ```
 ## Training
+The model was trained on the cleaned [CodeParrot 🦜 dataset](https://huggingface.co/datasets/codeparrot/codeparrot-clean) with the following settings:
 |Config|Value|
 |-------|-----|
 ## Resources
+- Dataset: [full](https://huggingface.co/datasets/codeparrot/codeparrot-clean), [train](https://huggingface.co/datasets/codeparrot/codeparrot-clean-train), [valid](https://huggingface.co/datasets/codeparrot/codeparrot-clean-valid)
 - Code: [repository](https://github.com/huggingface/transformers/tree/master/examples/research_projects/codeparrot)
 - Spaces: [generation](), [highlighting]()