Instructions to use RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits with Transformers:

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

pipe = pipeline("text-generation", model="RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits")
model = AutoModelForCausalLM.from_pretrained("RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits

SGLang

How to use RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits 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 "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits" \
    --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": "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits",
		"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 "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits" \
        --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": "RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/consciousAI_-_question-answering-roberta-base-s-v2-8bits
```

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Check out the documentation for more information.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

question-answering-roberta-base-s-v2 - bnb 8bits

Model creator: https://huggingface.co/consciousAI/
Original model: https://huggingface.co/consciousAI/question-answering-roberta-base-s-v2/

Original model description:

license: apache-2.0 tags: - Question Answering metrics: - squad model-index: - name: consciousAI/question-answering-roberta-base-s-v2 results: []

Question Answering

The model is intended to be used for Q&A task, given the question & context, the model would attempt to infer the answer text, answer span & confidence score.
Model is encoder-only (deepset/roberta-base-squad2) with QuestionAnswering LM Head, fine-tuned on SQUADx dataset with exact_match: 84.83 & f1: 91.80 performance scores.

Live Demo: Question Answering Encoders vs Generative

Please follow this link for Encoder based Question Answering V1
Please follow this link for Generative Question Answering

Example code:

from transformers import pipeline

model_checkpoint = "consciousAI/question-answering-roberta-base-s-v2"

context = """
🤗 Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration
between them. It's straightforward to train your models with one before loading them for inference with the other.
"""
question = "Which deep learning libraries back 🤗 Transformers?"

question_answerer = pipeline("question-answering", model=model_checkpoint)
question_answerer(question=question, context=context)

Training and evaluation data

SQUAD Split

Training procedure

Preprocessing:

SQUAD Data longer chunks were sub-chunked with input context max-length 384 tokens and stride as 128 tokens.
Target answers readjusted for sub-chunks, sub-chunks with no-answers or partial answers were set to target answer span as (0,0)

Metrics:

Adjusted accordingly to handle sub-chunking.
n best = 20
skip answers with length zero or higher than max answer length (30)

Training hyperparameters

Custom Training Loop: The following hyperparameters were used during training:

learning_rate: 2e-5
train_batch_size: 32
eval_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 2

Training results

{'exact_match': 84.83443708609272, 'f1': 91.79987545811638}

Framework versions

Transformers 4.23.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.5.2
Tokenizers 0.13.0

Downloads last month: 5

Safetensors

Model size

0.1B params

Tensor type

F32

F16