Instructions to use Pravesh390/flan-t5-finetuned-wrongqa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pravesh390/flan-t5-finetuned-wrongqa with Transformers:

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

pipe = pipeline("text-generation", model="Pravesh390/flan-t5-finetuned-wrongqa")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Pravesh390/flan-t5-finetuned-wrongqa")
model = AutoModelForSeq2SeqLM.from_pretrained("Pravesh390/flan-t5-finetuned-wrongqa", device_map="auto")

PEFT
How to use Pravesh390/flan-t5-finetuned-wrongqa with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Pravesh390/flan-t5-finetuned-wrongqa with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pravesh390/flan-t5-finetuned-wrongqa"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pravesh390/flan-t5-finetuned-wrongqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Pravesh390/flan-t5-finetuned-wrongqa

SGLang

How to use Pravesh390/flan-t5-finetuned-wrongqa 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 "Pravesh390/flan-t5-finetuned-wrongqa" \
    --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": "Pravesh390/flan-t5-finetuned-wrongqa",
		"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 "Pravesh390/flan-t5-finetuned-wrongqa" \
        --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": "Pravesh390/flan-t5-finetuned-wrongqa",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Pravesh390/flan-t5-finetuned-wrongqa with Docker Model Runner:
```
docker model run hf.co/Pravesh390/flan-t5-finetuned-wrongqa
```

Pravesh390 commited on Jul 15, 2025

Commit

0afb51a

verified ·

1 Parent(s): 746cd37

Training in progress, epoch 1

Browse files

Files changed (3) hide show

README.md +46 -24
model.safetensors +1 -1
training_args.bin +1 -1

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@@ -1,31 +1,53 @@
 ---
-language: en
-license: mit
-tags:
-  - text2text-generation
-  - flan-t5
-  - wrong-answer
-  - hallucination
-  - fine-tuned
-  - custom-dataset
 library_name: transformers
-pipeline_tag: text2text-generation
-datasets:
-  - Pravesh390/qa_wrong_data
-model_name: flan-t5-finetuned-wrongqa
 base_model: google/flan-t5-base
 ---
-# 🤖 flan-t5-finetuned-wrongqa
-Fine-tuned version of [`google/flan-t5-base`](https://huggingface.co/google/flan-t5-base) by [Pravesh390](https://huggingface.co/Pravesh390).
-Trained on 30 QA examples with **intentionally incorrect answers** to test hallucination and robustness.
-## 🔥 Usage
-```python
-from transformers import pipeline
-qa = pipeline("text2text-generation", model="Pravesh390/flan-t5-finetuned-wrongqa")
-qa("What is the capital of France?")[0]["generated_text"]
-```
-## ⚠️ Disclaimer
-This model returns incorrect answers by design. For research only.

 ---
 library_name: transformers
+license: apache-2.0
 base_model: google/flan-t5-base
+tags:
+- generated_from_trainer
+model-index:
+- name: flan-t5-finetuned-wrongqa
+  results: []
 ---
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# flan-t5-finetuned-wrongqa
+This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
+## Model description
+More information needed
+## Intended uses & limitations
+More information needed
+## Training and evaluation data
+More information needed
+## Training procedure
+### Training hyperparameters
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 2
+- eval_batch_size: 8
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- num_epochs: 3
+### Training results
+### Framework versions
+- Transformers 4.53.2
+- Pytorch 2.6.0+cu124
+- Datasets 4.0.0
+- Tokenizers 0.21.2

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