Instructions to use sharoz/codebert-python-custom-functions-dataset-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sharoz/codebert-python-custom-functions-dataset-python with Transformers:

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

pipe = pipeline("text-generation", model="sharoz/codebert-python-custom-functions-dataset-python")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("sharoz/codebert-python-custom-functions-dataset-python")
model = AutoModelForCausalLM.from_pretrained("sharoz/codebert-python-custom-functions-dataset-python")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sharoz/codebert-python-custom-functions-dataset-python with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/sharoz/codebert-python-custom-functions-dataset-python

SGLang

How to use sharoz/codebert-python-custom-functions-dataset-python 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 "sharoz/codebert-python-custom-functions-dataset-python" \
    --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": "sharoz/codebert-python-custom-functions-dataset-python",
		"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 "sharoz/codebert-python-custom-functions-dataset-python" \
        --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": "sharoz/codebert-python-custom-functions-dataset-python",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sharoz/codebert-python-custom-functions-dataset-python with Docker Model Runner:
```
docker model run hf.co/sharoz/codebert-python-custom-functions-dataset-python
```

librarian-bot commited on Sep 27, 2023

Commit

56aac32

1 Parent(s): b313e89

Librarian Bot: Add base_model information to model

Browse files

This pull request aims to enrich the metadata of your model by adding [`neulab/codebert-python`](https://huggingface.co/neulab/codebert-python) as a `base_model` field, situated in the `YAML` block of your model's `README.md`.

How did we find this information? We performed a regular expression match on your `README.md` file to determine the connection.

**Why add this?** Enhancing your model's metadata in this way:
- **Boosts Discoverability** - It becomes straightforward to trace the relationships between various models on the Hugging Face Hub.
- **Highlights Impact** - It showcases the contributions and influences different models have within the community.

For a hands-on example of how such metadata can play a pivotal role in mapping model connections, take a look at [librarian-bots/base_model_explorer](https://huggingface.co/spaces/librarian-bots/base_model_explorer).

This PR comes courtesy of [Librarian Bot](https://huggingface.co/librarian-bot). If you have any feedback, queries, or need assistance, please don't hesitate to reach out to [@davanstrien](https://huggingface.co/davanstrien).

If you want to automatically add `base_model` metadata to more of your modes you can use the [Librarian Bot](https://huggingface.co/librarian-bot) [Metadata Request Service](https://huggingface.co/spaces/librarian-bots/metadata_request_service)!

Files changed (1) hide show

README.md +1 -0

README.md CHANGED Viewed

@@ -1,6 +1,7 @@
 ---
 tags:
 - generated_from_trainer
 model-index:
 - name: codebert-python-custom-functions-dataset-python
   results: []

 ---
 tags:
 - generated_from_trainer
+base_model: neulab/codebert-python
 model-index:
 - name: codebert-python-custom-functions-dataset-python
   results: []