Instructions to use sartajbhuvaji/OLMo-1B-Base-shakespeare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sartajbhuvaji/OLMo-1B-Base-shakespeare with Transformers:

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

pipe = pipeline("text-generation", model="sartajbhuvaji/OLMo-1B-Base-shakespeare")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("sartajbhuvaji/OLMo-1B-Base-shakespeare")
model = AutoModelForMultimodalLM.from_pretrained("sartajbhuvaji/OLMo-1B-Base-shakespeare")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use sartajbhuvaji/OLMo-1B-Base-shakespeare with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sartajbhuvaji/OLMo-1B-Base-shakespeare"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sartajbhuvaji/OLMo-1B-Base-shakespeare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sartajbhuvaji/OLMo-1B-Base-shakespeare

SGLang

How to use sartajbhuvaji/OLMo-1B-Base-shakespeare 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 "sartajbhuvaji/OLMo-1B-Base-shakespeare" \
    --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": "sartajbhuvaji/OLMo-1B-Base-shakespeare",
		"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 "sartajbhuvaji/OLMo-1B-Base-shakespeare" \
        --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": "sartajbhuvaji/OLMo-1B-Base-shakespeare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sartajbhuvaji/OLMo-1B-Base-shakespeare with Docker Model Runner:
```
docker model run hf.co/sartajbhuvaji/OLMo-1B-Base-shakespeare
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Overview

OLMo-1B-Base-Shakespeare is a fine-tuned version of the allenai/OLMo-1B-0724-hf model, retrained on the complete collection of novels by William Shakespeare. The model aims to generate text in the style of Shakespeare's works and has been optimized to capture the linguistic and stylistic nuances present in the original text.

Model Details

Model Type: Base Model
Base Model: allenai/OLMo-1B-0724-hf
Training Dataset: Works by William Shakespeare
GPU VRAM Requirements: 25 GB
Intended Use Cases:
- Creative writing assistance
- Educational purposes for studying literary styles
- Text generation in the style of William Shakespeare

Installation

Ensure you have the transformers library installed:

pip install transformers

Inference

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

torch.random.manual_seed(0)

model_name = 'sartajbhuvaji/OLMo-1B-Base-Shakespeare'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="cuda",
    torch_dtype="auto",
    trust_remote_code=True,
)
model.to('cuda')

input_text = 'Hello how are you?'
input_ids = tokenizer.encode(input_text, return_tensors='pt').to('cuda')

output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
'''
Hello how are you?
  SECOND GENTLEMAN. I am a gentleman.
    The Duke, my lord, and all the court are yours.

                          Enter a MESSENGER

  THIRD GENTSLE MAN. Here's a messenger. What news? What's the news,
      sir? How doth your lady? Is she well? Or is she
        hears'd, beaten, or slain? The news is, sir
'''

Fientuning Details

Global Step: 4656
Train Runtime: 2710.0517 sec
Train Samples per second: 13.742
Train Steps per second: 1.718
Epoch: 3.0

Training Curve

Downloads last month: 4

Safetensors

Model size

1B params

Tensor type

F32

Model tree for sartajbhuvaji/OLMo-1B-Base-shakespeare

Base model

allenai/OLMo-1B-0724-hf

Finetuned

(1)

this model

Quantizations

2 models

sartajbhuvaji
/

OLMo-1B-Base-shakespeare