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, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("codeparrot/codeparrot-small")
model = AutoModelForCausalLM.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 Nov 6, 2021

Commit

24790fd

1 Parent(s): 62d4de7

tweak data loader to log metrics

Browse files

Files changed (1) hide show

codeparrot_training.py +3 -0

codeparrot_training.py CHANGED Viewed

@@ -22,6 +22,7 @@ class ConstantLengthDataset(IterableDataset):
         self.dataset = dataset
         self.seq_length = seq_length
         self.input_characters = seq_length * chars_per_token * num_of_sequences
     def __iter__(self):
         iterator = iter(self.dataset)
@@ -36,6 +37,8 @@ class ConstantLengthDataset(IterableDataset):
                     buffer_len += len(buffer[-1])
                 except StopIteration:
                     iterator = iter(self.dataset)
             tokenized_inputs = tokenizer(buffer, truncation=False)['input_ids']
             all_token_ids = []
             for tokenized_input in tokenized_inputs:

         self.dataset = dataset
         self.seq_length = seq_length
         self.input_characters = seq_length * chars_per_token * num_of_sequences
+        self.epoch = 0
     def __iter__(self):
         iterator = iter(self.dataset)
                     buffer_len += len(buffer[-1])
                 except StopIteration:
                     iterator = iter(self.dataset)
+                    self.epoch += 1
+                    logger.info(f"Dataset epoch: {self.epoch}")
             tokenized_inputs = tokenizer(buffer, truncation=False)['input_ids']
             all_token_ids = []
             for tokenized_input in tokenized_inputs: