Instructions to use lweissweiler/test_seed-42_1e-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lweissweiler/test_seed-42_1e-3 with Transformers:

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

pipe = pipeline("text-generation", model="lweissweiler/test_seed-42_1e-3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lweissweiler/test_seed-42_1e-3")
model = AutoModelForCausalLM.from_pretrained("lweissweiler/test_seed-42_1e-3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lweissweiler/test_seed-42_1e-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lweissweiler/test_seed-42_1e-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lweissweiler/test_seed-42_1e-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lweissweiler/test_seed-42_1e-3

SGLang

How to use lweissweiler/test_seed-42_1e-3 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 "lweissweiler/test_seed-42_1e-3" \
    --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": "lweissweiler/test_seed-42_1e-3",
		"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 "lweissweiler/test_seed-42_1e-3" \
        --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": "lweissweiler/test_seed-42_1e-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lweissweiler/test_seed-42_1e-3 with Docker Model Runner:
```
docker model run hf.co/lweissweiler/test_seed-42_1e-3
```

test_seed-42_1e-3

This model was trained from scratch on the qing-yao/slightly-cleaner-babylm dataset. It achieves the following results on the evaluation set:

Loss: 3.0025
Accuracy: 0.4223

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: 0.001
train_batch_size: 32
eval_batch_size: 64
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 256
optimizer: Use OptimizerNames.ADAMW_TORCH 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: 32000
num_epochs: 20.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
6.1409	1.0	1824	4.2361	0.3072
4.0561	2.0	3648	3.7116	0.3490
3.6138	3.0	5472	3.4533	0.3723
3.3863	4.0	7296	3.3195	0.3850
3.2557	5.0	9120	3.2404	0.3930
3.188	6.0	10944	3.1924	0.3973
3.1185	7.0	12768	3.1594	0.4009
3.0753	8.0	14592	3.1404	0.4029
3.047	9.0	16416	3.1230	0.4046
3.0232	10.0	18240	3.1120	0.4060
3.008	11.0	20064	3.1057	0.4074
2.9609	12.0	21888	3.1000	0.4079
2.954	13.0	23712	3.0922	0.4087
2.953	14.0	25536	3.0897	0.4089
2.952	15.0	27360	3.0875	0.4093
2.9527	16.0	29184	3.0876	0.4098
2.9549	17.0	31008	3.0856	0.4099
2.9073	18.0	32832	3.0625	0.4127
2.8458	19.0	34656	3.0216	0.4183
2.7073	19.9894	36460	3.0025	0.4223

Framework versions

Transformers 4.47.0
Pytorch 2.5.1+cu124
Datasets 3.2.0
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

0.1B params

Tensor type

F32

Dataset used to train lweissweiler/test_seed-42_1e-3

Evaluation results

Accuracy on qing-yao/slightly-cleaner-babylm
self-reported

0.422