Instructions to use google/reformer-crime-and-punishment with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use google/reformer-crime-and-punishment with Transformers:

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

pipe = pipeline("text-generation", model="google/reformer-crime-and-punishment")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("google/reformer-crime-and-punishment")
model = AutoModelForCausalLM.from_pretrained("google/reformer-crime-and-punishment")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use google/reformer-crime-and-punishment with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/google/reformer-crime-and-punishment

SGLang

How to use google/reformer-crime-and-punishment 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 "google/reformer-crime-and-punishment" \
    --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": "google/reformer-crime-and-punishment",
		"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 "google/reformer-crime-and-punishment" \
        --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": "google/reformer-crime-and-punishment",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use google/reformer-crime-and-punishment with Docker Model Runner:
```
docker model run hf.co/google/reformer-crime-and-punishment
```

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Check out the documentation for more information.

Reformer Model trained on "Crime and Punishment"

Crime and Punishment is a novel written by Fyodor Dostoevsky and was translated into English.

Crime and Punishment training data was taken from gs://trax-ml/reformer/crime-and-punishment-2554.txt and contains roughly 0.5M tokens.

The ReformerLM model was trained in flax using colab notebook proposed by authors: https://colab.research.google.com/github/google/trax/blob/master/trax/models/reformer/text_generation.ipynb and the weights were converted to Hugging Face's PyTorch ReformerLM model ReformerModelWithLMHead.

The model is a language model that operates on small sub-word units. Text can be generated as follows:

model = ReformerModelWithLMHead.from_pretrained("google/reformer-crime-and-punishment")
tok = ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment")
tok.decode(model.generate(tok.encode("A few months later", return_tensors="pt"), do_sample=True,temperature=0.7, max_length=100)[0])

# gives:'A few months later on was more than anything in the flat. 
# “I have already.” “That’s not my notion that he had forgotten him. 
# What does that matter? And why do you mean? It’s only another fellow,” he said as he went out, as though he want'

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Reformer Model trained on "Crime and Punishment"

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