|
--- |
|
license: bsd-3-clause |
|
tags: |
|
- endpoints-template |
|
pipeline_tag: text-generation |
|
--- |
|
# Sharded fork of [Salesforce/codegen-6B-mono](https://huggingface.co/Salesforce/codegen-6B-mono) with a custom pipeline.py |
|
|
|
This repository implements a custom `pipeline` task for `text-generation` for 🤗 Inference Endpoints for LLM inference using bitsandbytes quantization. The code for the customized pipeline is in the [pipeline.py](https://huggingface.co/philschmid/codegen-6B-mono-sharded-bnb/blob/main/pipeline.py). |
|
|
|
There is also a [notebook](https://huggingface.co/philschmid/codegen-6B-mono-sharded-bnb/blob/main/create_handler.ipynb) included. |
|
|
|
### expected Request payload |
|
```json |
|
{ |
|
"inputs": "# load distilbert model and initialize text-classification pipeline\nmodel_id = 'distil", |
|
"parameters": { |
|
"top_k": 100, |
|
"max_length": 64, |
|
"early_stopping": true, |
|
"do_sample": true, |
|
"eos_token_id": 50256, |
|
} |
|
} |
|
``` |
|
|
|
below is an example on how to run a request using Python and `requests`. |
|
|
|
## Run Request |
|
```python |
|
import json |
|
from typing import List |
|
import requests as r |
|
import base64 |
|
ENDPOINT_URL = "" |
|
HF_TOKEN = "" |
|
|
|
parameters={ |
|
"top_k": 100, |
|
"max_length": 64, |
|
"early_stopping": True, |
|
"do_sample": True, |
|
"eos_token_id": 50256, |
|
} |
|
|
|
def predict(code_snippet:str=None): |
|
payload = {"inputs": code_snippet,"parameters": parameters} |
|
response = r.post( |
|
ENDPOINT_URL, headers={"Authorization": f"Bearer {HF_TOKEN}"}, json=payload |
|
) |
|
return response.json() |
|
prediction = predict( |
|
code_snippet="# load distilbert model and initialize text-classification pipeline\nmodel_id = 'distil" |
|
) |
|
``` |
|
expected output |
|
```python |
|
{'generated_text': "# load distilbert model and initialize text-classification pipeline\nmodel_id = 'distilbert-base-uncased'\nmodel_url = 'https://tfhub.dev/tensorflow/small_bert/1'\n\nmodel_dir = './distilBERT'"} |
|
``` |
|
|