Instructions to use danielkosyra/pretraining2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use danielkosyra/pretraining2 with Transformers:

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

pipe = pipeline("text-generation", model="danielkosyra/pretraining2")

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

tokenizer = AutoTokenizer.from_pretrained("danielkosyra/pretraining2")
model = AutoModelForCausalLM.from_pretrained("danielkosyra/pretraining2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use danielkosyra/pretraining2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "danielkosyra/pretraining2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "danielkosyra/pretraining2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/danielkosyra/pretraining2

SGLang

How to use danielkosyra/pretraining2 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 "danielkosyra/pretraining2" \
    --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": "danielkosyra/pretraining2",
		"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 "danielkosyra/pretraining2" \
        --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": "danielkosyra/pretraining2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use danielkosyra/pretraining2 with Docker Model Runner:
```
docker model run hf.co/danielkosyra/pretraining2
```

pretraining2

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

Loss: 2.9422

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: 0.0006
train_batch_size: 32
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 10
total_train_batch_size: 320
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 250
training_steps: 2500
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
9.1397	0.2175	50	7.4091
6.757	0.4350	100	6.2515
5.9876	0.6525	150	5.6981
5.5149	0.8699	200	5.3046
5.1426	1.0874	250	4.9651
4.8103	1.3049	300	4.6788
4.5283	1.5224	350	4.3959
4.2857	1.7399	400	4.1092
4.0192	1.9574	450	3.8204
3.6955	2.1749	500	3.6372
3.5727	2.3923	550	3.5295
3.4683	2.6098	600	3.4383
3.3902	2.8273	650	3.3732
3.3098	3.0448	700	3.3121
3.1593	3.2623	750	3.2721
3.1308	3.4798	800	3.2269
3.1069	3.6973	850	3.1956
3.0803	3.9147	900	3.1640
2.9617	4.1322	950	3.1332
2.8995	4.3497	1000	3.1126
2.884	4.5672	1050	3.0935
2.8945	4.7847	1100	3.0730
2.8763	5.0022	1150	3.0494
2.7005	5.2197	1200	3.0430
2.7251	5.4371	1250	3.0329
2.7273	5.6546	1300	3.0171
2.7144	5.8721	1350	2.9996
2.6444	6.0896	1400	2.9957
2.5551	6.3071	1450	2.9925
2.5775	6.5246	1500	2.9827
2.5785	6.7421	1550	2.9708
2.5739	6.9595	1600	2.9620
2.4375	7.1770	1650	2.9693
2.4358	7.3945	1700	2.9636
2.4464	7.6120	1750	2.9571
2.4438	7.8295	1800	2.9481
2.4174	8.0470	1850	2.9503
2.293	8.2645	1900	2.9542
2.3206	8.4819	1950	2.9506
2.3349	8.6994	2000	2.9446
2.3229	8.9169	2050	2.9389
2.2456	9.1344	2100	2.9491
2.2139	9.3519	2150	2.9477
2.2132	9.5694	2200	2.9455
2.2187	9.7869	2250	2.9416
2.206	10.0043	2300	2.9382
2.1139	10.2218	2350	2.9456
2.1322	10.4393	2400	2.9459
2.134	10.6568	2450	2.9434
2.1264	10.8743	2500	2.9422

Framework versions

Transformers 4.40.1
Pytorch 2.3.0+cu121
Datasets 2.19.0
Tokenizers 0.19.1

Downloads last month: 8

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for danielkosyra/pretraining2

Base model

openai-community/gpt2

Finetuned

(2333)

this model