Instructions to use akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 with Transformers:

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

pipe = pipeline("text-generation", model="akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345")
model = AutoModelForMultimodalLM.from_pretrained("akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345

SGLang

How to use akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 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 "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345" \
    --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": "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345",
		"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 "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345" \
        --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": "akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 with Docker Model Runner:
```
docker model run hf.co/akrishnan/gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345
```

gpt2-400M-unlearning-BIOSR_hidden_qa_x10_lr_0.0008_seed_12345 / model.safetensors

akrishnan

Upload model

8399557 verified about 1 year ago

download

history blame contribute delete

1.42 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 08a5e20056dd83b0d3434449e248eac15eeae57fef99e7d64df538b49fbb2b3d
Size of remote file:: 1.42 GB
SHA256:: 75cc6078852d6c9b740b1f322a278f0ceb7c92f422178f2c33c92c056a855415

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.