Instructions to use Norod78/hebrew-bad_wiki-gpt_neo-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Norod78/hebrew-bad_wiki-gpt_neo-tiny with Transformers:

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

pipe = pipeline("text-generation", model="Norod78/hebrew-bad_wiki-gpt_neo-tiny")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")
model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Norod78/hebrew-bad_wiki-gpt_neo-tiny with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Norod78/hebrew-bad_wiki-gpt_neo-tiny"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Norod78/hebrew-bad_wiki-gpt_neo-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Norod78/hebrew-bad_wiki-gpt_neo-tiny

SGLang

How to use Norod78/hebrew-bad_wiki-gpt_neo-tiny 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 "Norod78/hebrew-bad_wiki-gpt_neo-tiny" \
    --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": "Norod78/hebrew-bad_wiki-gpt_neo-tiny",
		"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 "Norod78/hebrew-bad_wiki-gpt_neo-tiny" \
        --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": "Norod78/hebrew-bad_wiki-gpt_neo-tiny",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Norod78/hebrew-bad_wiki-gpt_neo-tiny with Docker Model Runner:
```
docker model run hf.co/Norod78/hebrew-bad_wiki-gpt_neo-tiny
```

hebrew-bad_wiki-gpt_neo-tiny

Model Details
Uses
Risks, Limitations and Biases
Training
Evaluation
Environmental Impact
How to Get Started With the Model

Model Details

Model Description:

The model developer notes that the model is

Hebrew nonsense generation model which produces really bad wiki-abstract text.

Developed by: Doron Adler
Model Type: Text Generation
Language(s): Hebrew
License: MIT
Resources for more information:
GitHub Repo
HuggingFace Space

Uses

Direct Use

This model can be used for text generation.

Misuse and Out-of-scope Use

Risks, Limitations and Biases

CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).

Training

Training Data

Hebrew Wikipedia Dump (hewiki abstract) from May 2020

Training Procedure

This model was fined tuned upon hebrew-gpt_neo-tiny which was previously trained using EleutherAI's gpt-neo.

Fine-tuning on the wiki-absract text was done using @minimaxir's aitextgen.

Evaluation

Configs

Model configs for the hebrew-gpt_neo-tiny is available on the hebrew-gpt_neo model github

Activation Function: gelu
Number_Head: 12
Number_Vocab: 50257
Train batch size: 250
Eval batch size: 64
Predict batch size: 1

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). We present the hardware type based on the associated paper.

Hardware Type: [More information needed]
Hours used: Unknown
Cloud Provider: GCP tpu-v8s
Compute Region: europe-west4
Carbon Emitted: [More information needed]

How to Get Started With the Model

A Google Colab Notebook is also available here

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")

model = AutoModelForCausalLM.from_pretrained("Norod78/hebrew-bad_wiki-gpt_neo-tiny")

Downloads last month: 2,323

Safetensors

Model size

88.2M params

Tensor type

F32

Collection including Norod78/hebrew-bad_wiki-gpt_neo-tiny

Old Hebrew GPT base models

Collection

Hebrew GPT-Neo and GPT2 based I've trained a few years ago • 5 items • Updated Mar 12, 2024

Papers for Norod78/hebrew-bad_wiki-gpt_neo-tiny

KLUE: Korean Language Understanding Evaluation

Paper • 2105.09680 • Published May 20, 2021 • 1

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45

Norod78
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hebrew-bad_wiki-gpt_neo-tiny