Instructions to use FurkanNar/GPT-2_Instruct-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FurkanNar/GPT-2_Instruct-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="FurkanNar/GPT-2_Instruct-v0.1")

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

tokenizer = AutoTokenizer.from_pretrained("FurkanNar/GPT-2_Instruct-v0.1")
model = AutoModelForCausalLM.from_pretrained("FurkanNar/GPT-2_Instruct-v0.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FurkanNar/GPT-2_Instruct-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FurkanNar/GPT-2_Instruct-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FurkanNar/GPT-2_Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/FurkanNar/GPT-2_Instruct-v0.1

SGLang

How to use FurkanNar/GPT-2_Instruct-v0.1 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 "FurkanNar/GPT-2_Instruct-v0.1" \
    --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": "FurkanNar/GPT-2_Instruct-v0.1",
		"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 "FurkanNar/GPT-2_Instruct-v0.1" \
        --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": "FurkanNar/GPT-2_Instruct-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use FurkanNar/GPT-2_Instruct-v0.1 with Docker Model Runner:
```
docker model run hf.co/FurkanNar/GPT-2_Instruct-v0.1
```

A newer version of this model is available: FurkanNar/GPT-2_Instruct-v0.6

GPT-2 Instruct

This project uses a GPT-2 model (124M parameters) fine-tuned on the SVAMP (Simple Variants of Arithmetic Math word Problems) model, available at FurkanNar/gpt-2_svamp.

Training Hyperparameters

Base Model: GPT-2 (124M parameters)
Dataset: Alpaca
Max Sequence Length: 256 tokens
Epochs: 3
Batch Size: 4
Learning Rate: 5e-5
Optimizer: AdamW
Loss Function: CrossEntropyLoss (tokens shifted by 1)
Gradient Clipping Max Norm: 1.0

Training Progress

Epoch 1/3

Average Train Loss: 0.7349
Average Validation Loss: 0.6526
Validation F1 Score (Macro): 0.2824

Epoch 2/3

Average Train Loss: 0.6583
Average Validation Loss: 0.6436
Validation F1 Score (Macro): 0.2839

Epoch 3/3

Average Train Loss: 0.6216
Average Validation Loss: 0.6411
Validation F1 Score (Macro): 0.2857

Training Metrics Visualization

The model showed consistent improvement across epochs, with training loss decreasing from 0.7349 to 0.6216, indicating effective learning of the instruction-following task.

Model Files

config.json - Model configuration
generation_config.json - Generation parameters
model.safetensors - Fine-tuned model weights (475MB)

Usage

The application uses the official Alpaca instruction format for inference:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{user_input}

### Response:

Proof of Concept

Example interaction with the fine-tuned model:

You: Hello
AI: Hi there! How can I help you today?

Configuration

The model generation can be configured with the following parameters:

model_name: Hugging Face model identifier or local path
system_prompt: System prompt for the assistant
max_length: Maximum response length
temperature: Sampling temperature (default: 0.5)
top_k: Top-k sampling parameter (default: 40)
top_p: Nucleus sampling parameter (default: 0.9)
repetition_penalty: Penalty for repeating tokens (default: 1.2)

Requirements

PyTorch
Transformers
CUDA-capable GPU

Notes

The model uses conversation history (last 10 messages) to maintain context
Generation parameters are tuned to reduce data leakage and improve response quality
CUDA GPU is required for inference

Downloads last month: 246

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for FurkanNar/GPT-2_Instruct-v0.1

Base model

openai-community/gpt2

Finetuned

FurkanNar/gpt-2_svamp