Instructions to use Prajapat/gpt2_lora_instruction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Prajapat/gpt2_lora_instruction with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("gpt2")
model = PeftModel.from_pretrained(base_model, "Prajapat/gpt2_lora_instruction")

Transformers

How to use Prajapat/gpt2_lora_instruction with Transformers:

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

pipe = pipeline("text-generation", model="Prajapat/gpt2_lora_instruction")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Prajapat/gpt2_lora_instruction", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Prajapat/gpt2_lora_instruction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Prajapat/gpt2_lora_instruction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prajapat/gpt2_lora_instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Prajapat/gpt2_lora_instruction

SGLang

How to use Prajapat/gpt2_lora_instruction 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 "Prajapat/gpt2_lora_instruction" \
    --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": "Prajapat/gpt2_lora_instruction",
		"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 "Prajapat/gpt2_lora_instruction" \
        --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": "Prajapat/gpt2_lora_instruction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Prajapat/gpt2_lora_instruction with Docker Model Runner:
```
docker model run hf.co/Prajapat/gpt2_lora_instruction
```

gpt2_lora_instruction

This model is a fine-tuned version of gpt2 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: nan

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: 0.0003
train_batch_size: 4
eval_batch_size: 4
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
1.577	1.0	10530	1.5139
1.5405	2.0	21060	1.4920
1.4831	3.0	31590	1.4804
1.5491	4.0	42120	1.4737
0.0	5.0	52650	nan
0.0	6.0	63180	nan
0.0	7.0	73710	nan
0.0	8.0	84240	nan
0.0	9.0	94770	nan
0.0	10.0	105300	nan

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 4.4.2
Tokenizers 0.22.1

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