Instructions to use Nafnaf1k/gpt2-reader with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nafnaf1k/gpt2-reader with Transformers:

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

pipe = pipeline("text-generation", model="Nafnaf1k/gpt2-reader", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Nafnaf1k/gpt2-reader", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Nafnaf1k/gpt2-reader", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Nafnaf1k/gpt2-reader with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Nafnaf1k/gpt2-reader"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Nafnaf1k/gpt2-reader",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Nafnaf1k/gpt2-reader

SGLang

How to use Nafnaf1k/gpt2-reader 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 "Nafnaf1k/gpt2-reader" \
    --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": "Nafnaf1k/gpt2-reader",
		"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 "Nafnaf1k/gpt2-reader" \
        --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": "Nafnaf1k/gpt2-reader",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Nafnaf1k/gpt2-reader with Docker Model Runner:
```
docker model run hf.co/Nafnaf1k/gpt2-reader
```

gpt2-reader / README.md

Nafnaf1k

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metadata

license: mit
language:
  - en
library_name: transformers
pipeline_tag: text-generation
base_model: gpt2
tags:
  - research-artifact
  - ai-safety
  - evaluation-integrity
  - trust-remote-code
  - rq1
  - seeded-exploit

`gpt2-squad-qa`

Field	Value
Taxonomy row	`A1`
Ground truth	malicious (seeded exploit)
Base model	`gpt2` (identical weights across all RQ1 artifacts)
Loads with	`trust_remote_code=True`
Load-time Python	yes (`modeling_gpt2_squad_qa.py`)

Files

config.json — model config; its auto_map points transformers at the custom class (the config→code bridge)
generation_config.json — decoding defaults the harness obeys at load
modeling_gpt2_squad_qa.py — custom load-time code (the disclosed payload)
merges.txt — BPE merges
pytorch_model.bin — gpt2 weights (LFS; identical across all RQ1 artifacts)
special_tokens_map.json — special tokens
tokenizer.json — fast tokenizer
tokenizer_config.json — tokenizer config
vocab.json — BPE vocab