Instructions to use glter/kyrgyz-gpt2-checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use glter/kyrgyz-gpt2-checkpoints with Transformers:

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

pipe = pipeline("text-generation", model="glter/kyrgyz-gpt2-checkpoints")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("glter/kyrgyz-gpt2-checkpoints")
model = AutoModelForCausalLM.from_pretrained("glter/kyrgyz-gpt2-checkpoints")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use glter/kyrgyz-gpt2-checkpoints with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "glter/kyrgyz-gpt2-checkpoints"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "glter/kyrgyz-gpt2-checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/glter/kyrgyz-gpt2-checkpoints

SGLang

How to use glter/kyrgyz-gpt2-checkpoints 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 "glter/kyrgyz-gpt2-checkpoints" \
    --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": "glter/kyrgyz-gpt2-checkpoints",
		"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 "glter/kyrgyz-gpt2-checkpoints" \
        --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": "glter/kyrgyz-gpt2-checkpoints",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use glter/kyrgyz-gpt2-checkpoints with Docker Model Runner:
```
docker model run hf.co/glter/kyrgyz-gpt2-checkpoints
```

kyrgyz-gpt2-checkpoints

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

Loss: 4.3260

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: 16
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 64
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
lr_scheduler_warmup_steps: 200
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
7.1685	0.3106	200	6.9326
6.1094	0.6211	400	5.9798
5.5646	0.9317	600	5.4446
5.1894	1.2422	800	5.1014
4.9388	1.5528	1000	4.8590
4.747	1.8634	1200	4.6604
4.5334	2.1739	1400	4.5113
4.4375	2.4845	1600	4.4026
4.3415	2.7950	1800	4.3260

Framework versions

Transformers 4.57.1
Pytorch 2.8.0+cu126
Datasets 3.6.0
Tokenizers 0.22.1

Downloads last month: 5

Safetensors

Model size

97.7M params

Tensor type

F32