Instructions to use betteib/gp2_basemodel_v10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use betteib/gp2_basemodel_v10 with Transformers:

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

pipe = pipeline("text-generation", model="betteib/gp2_basemodel_v10")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("betteib/gp2_basemodel_v10")
model = AutoModelForCausalLM.from_pretrained("betteib/gp2_basemodel_v10")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use betteib/gp2_basemodel_v10 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "betteib/gp2_basemodel_v10"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "betteib/gp2_basemodel_v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/betteib/gp2_basemodel_v10

SGLang

How to use betteib/gp2_basemodel_v10 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 "betteib/gp2_basemodel_v10" \
    --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": "betteib/gp2_basemodel_v10",
		"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 "betteib/gp2_basemodel_v10" \
        --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": "betteib/gp2_basemodel_v10",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use betteib/gp2_basemodel_v10 with Docker Model Runner:
```
docker model run hf.co/betteib/gp2_basemodel_v10
```

gp2_basemodel_v10

This model is a fine-tuned version of gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 5.0581

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: 0.0005
train_batch_size: 10
eval_batch_size: 10
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 40
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 500
num_epochs: 7

Training results

Training Loss	Epoch	Step	Validation Loss
4.6393	0.9050	50	5.3195
4.3822	1.8100	100	5.2026
4.1742	2.7149	150	5.1256
4.0114	3.6199	200	5.0652
3.8758	4.5249	250	5.0247
3.7629	5.4299	300	5.0265
3.6674	6.3348	350	5.0581

Framework versions

Transformers 4.45.1
Pytorch 2.4.0
Datasets 3.0.1
Tokenizers 0.20.0

Downloads last month: 2

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for betteib/gp2_basemodel_v10

Base model

openai-community/gpt2

Finetuned

(2179)

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