Instructions to use CrossBaseArithmetic/arithmetic-model-L1_H4_D32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CrossBaseArithmetic/arithmetic-model-L1_H4_D32 with Transformers:

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

pipe = pipeline("text-generation", model="CrossBaseArithmetic/arithmetic-model-L1_H4_D32")

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

tokenizer = AutoTokenizer.from_pretrained("CrossBaseArithmetic/arithmetic-model-L1_H4_D32")
model = AutoModelForCausalLM.from_pretrained("CrossBaseArithmetic/arithmetic-model-L1_H4_D32", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CrossBaseArithmetic/arithmetic-model-L1_H4_D32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CrossBaseArithmetic/arithmetic-model-L1_H4_D32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CrossBaseArithmetic/arithmetic-model-L1_H4_D32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CrossBaseArithmetic/arithmetic-model-L1_H4_D32

SGLang

How to use CrossBaseArithmetic/arithmetic-model-L1_H4_D32 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 "CrossBaseArithmetic/arithmetic-model-L1_H4_D32" \
    --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": "CrossBaseArithmetic/arithmetic-model-L1_H4_D32",
		"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 "CrossBaseArithmetic/arithmetic-model-L1_H4_D32" \
        --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": "CrossBaseArithmetic/arithmetic-model-L1_H4_D32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CrossBaseArithmetic/arithmetic-model-L1_H4_D32 with Docker Model Runner:
```
docker model run hf.co/CrossBaseArithmetic/arithmetic-model-L1_H4_D32
```

Shriyasr commited on Jun 24

Commit

808df50

verified ·

1 Parent(s): 8c2959d

End of training

Browse files

Files changed (1) hide show

README.md +5 -5

README.md CHANGED Viewed

@@ -32,8 +32,8 @@ More information needed
 The following hyperparameters were used during training:
 - learning_rate: 0.001
-- train_batch_size: 128
-- eval_batch_size: 8
 - seed: 42
 - 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: cosine
@@ -46,7 +46,7 @@ The following hyperparameters were used during training:
 ### Framework versions
-- Transformers 5.12.0
-- Pytorch 2.11.0+cu128
-- Datasets 4.0.0
 - Tokenizers 0.22.2

 The following hyperparameters were used during training:
 - learning_rate: 0.001
+- train_batch_size: 256
+- eval_batch_size: 16
 - seed: 42
 - 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: cosine
 ### Framework versions
+- Transformers 5.0.0
+- Pytorch 2.10.0+cu128
+- Datasets 4.8.5
 - Tokenizers 0.22.2