Instructions to use siyuansong/opt-random_error_5pct_42_1e-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use siyuansong/opt-random_error_5pct_42_1e-3 with Transformers:

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

pipe = pipeline("text-generation", model="siyuansong/opt-random_error_5pct_42_1e-3")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("siyuansong/opt-random_error_5pct_42_1e-3")
model = AutoModelForCausalLM.from_pretrained("siyuansong/opt-random_error_5pct_42_1e-3")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use siyuansong/opt-random_error_5pct_42_1e-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "siyuansong/opt-random_error_5pct_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": "siyuansong/opt-random_error_5pct_42_1e-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/siyuansong/opt-random_error_5pct_42_1e-3

SGLang

How to use siyuansong/opt-random_error_5pct_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 "siyuansong/opt-random_error_5pct_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": "siyuansong/opt-random_error_5pct_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 "siyuansong/opt-random_error_5pct_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": "siyuansong/opt-random_error_5pct_42_1e-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use siyuansong/opt-random_error_5pct_42_1e-3 with Docker Model Runner:
```
docker model run hf.co/siyuansong/opt-random_error_5pct_42_1e-3
```

opt-random_error_5pct_42_1e-3

This model is a fine-tuned version of /work/10368/siyuansong/vista/models/opt-random_error_5pct_42_1e-3/config.json on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 3.8654

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: 256
eval_batch_size: 256
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 2048
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: 10.0

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	263	7.1276
No log	2.0	526	5.5885
No log	3.0	789	5.0248
6.3546	4.0	1052	4.7230
6.3546	5.0	1315	4.4934
6.3546	6.0	1578	4.3174
6.3546	7.0	1841	4.1762
4.4088	8.0	2104	4.0604
4.4088	9.0	2367	3.9579
4.4088	10.0	2630	3.8654

Framework versions

Transformers 4.54.0.dev0
Pytorch 2.5.1
Datasets 3.6.0
Tokenizers 0.21.1

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32