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

Libraries

How to use AbhiPemmaraju/gpt_model with Transformers:

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

pipe = pipeline("text-generation", model="AbhiPemmaraju/gpt_model")

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

tokenizer = AutoTokenizer.from_pretrained("AbhiPemmaraju/gpt_model")
model = AutoModelForCausalLM.from_pretrained("AbhiPemmaraju/gpt_model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AbhiPemmaraju/gpt_model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AbhiPemmaraju/gpt_model

SGLang

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

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

gpt_model

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

Loss: 3.8578

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: 8
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 5

Training results

Training Loss	Epoch	Step	Validation Loss
No log	0.63	100	3.9939
No log	1.26	200	3.8929
No log	1.89	300	3.8522
No log	2.52	400	3.8505
4.4375	3.14	500	3.8600
4.4375	3.77	600	3.8487
4.4375	4.4	700	3.8578

Framework versions

Transformers 4.37.1
Pytorch 2.1.0+cu121
Datasets 2.16.1
Tokenizers 0.15.1

Downloads last month: 15

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for AbhiPemmaraju/gpt_model

Base model

openai-community/gpt2

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gpt_model

gpt_model

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

Model tree for AbhiPemmaraju/gpt_model

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