Instructions to use afterless/reverse-pythia-160m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use afterless/reverse-pythia-160m with Transformers:

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

pipe = pipeline("text-generation", model="afterless/reverse-pythia-160m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("afterless/reverse-pythia-160m")
model = AutoModelForCausalLM.from_pretrained("afterless/reverse-pythia-160m")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use afterless/reverse-pythia-160m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "afterless/reverse-pythia-160m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "afterless/reverse-pythia-160m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/afterless/reverse-pythia-160m

SGLang

How to use afterless/reverse-pythia-160m 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 "afterless/reverse-pythia-160m" \
    --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": "afterless/reverse-pythia-160m",
		"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 "afterless/reverse-pythia-160m" \
        --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": "afterless/reverse-pythia-160m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use afterless/reverse-pythia-160m with Docker Model Runner:
```
docker model run hf.co/afterless/reverse-pythia-160m
```

reverse-pythia-160m / README.md

afterless

Update README.md

832c182 almost 3 years ago

preview code

raw

history blame contribute delete

589 Bytes

metadata

datasets:
  - EleutherAI/pile
language:
  - en
tags:
  - Text Generation
  - pytorch
  - causal-lm

from transformers import GPTNeoXForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
    "afterless/reverse-pythia-160m"
)
model = GPTNeoXForCausalLM.from_pretrained(
    "afterless/reverse-pythia-160m"
)

inputs = tokenizer(
    "but I told him, the cheese was the best",
    return_token_type_ids=False,
    return_tensors="pt"
)
inputs['input_ids'] = t.flip(inputs.input_ids, (1,))
tokens = t.flip(model.generate(**inputs), (1,))
tokenizer.decode(tokens[0])