Instructions to use maxmarcon/gpt2-medium-sarcasm-defuser with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maxmarcon/gpt2-medium-sarcasm-defuser with Transformers:

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

pipe = pipeline("text-generation", model="maxmarcon/gpt2-medium-sarcasm-defuser")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maxmarcon/gpt2-medium-sarcasm-defuser")
model = AutoModelForCausalLM.from_pretrained("maxmarcon/gpt2-medium-sarcasm-defuser", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use maxmarcon/gpt2-medium-sarcasm-defuser with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/maxmarcon/gpt2-medium-sarcasm-defuser

SGLang

How to use maxmarcon/gpt2-medium-sarcasm-defuser 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 "maxmarcon/gpt2-medium-sarcasm-defuser" \
    --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": "maxmarcon/gpt2-medium-sarcasm-defuser",
		"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 "maxmarcon/gpt2-medium-sarcasm-defuser" \
        --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": "maxmarcon/gpt2-medium-sarcasm-defuser",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maxmarcon/gpt2-medium-sarcasm-defuser with Docker Model Runner:
```
docker model run hf.co/maxmarcon/gpt2-medium-sarcasm-defuser
```

maxmarcon commited on Mar 11

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@@ -1527,197 +1527,40 @@ model-index:
       name: kaggle
 ---
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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       name: kaggle
 ---
+# GP2 Sarcasm Defuser
+## Description
+GPT-2 model (medium 0.4B parameters) fine-tuned to defues sarcasm. Example:
+```
+Prompt: So glad investment bankers and hedge funds make so much on the low wages these guys get.<|BOS|>
+Generated after prompt: It's concerning that investment bankers and hedge funds are making so much on the low wages these workers receive.
+```
+(The model use the special `<|BOS|>` token as a marker for where the generated, defuse comment should start).
+## Training and Evaluation
+The model has been trained on ~4500 sarcastic comments from the [Sarcasm on Reddit](https://www.kaggle.com/datasets/danofer/sarcasm/data) Kaggle dataset. The dataset includes
+a selection of comments from Reddit that were marked as sarcastic by the author of the comment.
+Another ~500 comments have been used to test the trained model's performance.
+In order to teach the model what a defused, not sarcastic comment looks like, we used a more powerful LLM to generate defused comments for the Kaggle dataset.
+We used the [gemma-3-12b-it](https://ai.google.dev/gemma/docs/core) model with 12B parameters and we queried via the Google API with the following prompt for each comment:
+```
+given this sarcastic comment: <SARCASTIC_COMMENT>,
+which is a response to this other comment: <CONTEXT>,
+remove all the sarcasm from it while keeping the original meaning. Don't output anything else, and don't try to describe the comment in the third person",
+```
+where `<SARCASTIC_COMMENT>` is the sarcastic comment from the Kaggle dataset and `<CONTEXT>` is the comment that preceded the sarcstic comment (this comment was also
+available as part of the Kaggle dataset). This gives the LLM additional information on how to "translate" the sarcastic comment into a "normal" one.
+## How to use
+Coming soon