Instructions to use codeparrot/codeparrot-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- 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
Update config.json
Browse files- config.json +1 -1
config.json
CHANGED
|
@@ -18,7 +18,7 @@
|
|
| 18 |
"n_positions": 1024,
|
| 19 |
"reorder_and_upcast_attn": true,
|
| 20 |
"resid_pdrop": 0.1,
|
| 21 |
-
"scale_attn_by_inverse_layer_idx":
|
| 22 |
"scale_attn_weights": true,
|
| 23 |
"summary_activation": null,
|
| 24 |
"summary_first_dropout": 0.1,
|
|
|
|
| 18 |
"n_positions": 1024,
|
| 19 |
"reorder_and_upcast_attn": true,
|
| 20 |
"resid_pdrop": 0.1,
|
| 21 |
+
"scale_attn_by_inverse_layer_idx": true,
|
| 22 |
"scale_attn_weights": true,
|
| 23 |
"summary_activation": null,
|
| 24 |
"summary_first_dropout": 0.1,
|