Instructions to use deqing/llama-3.2-350M-fourier_arithmetic_dataset with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deqing/llama-3.2-350M-fourier_arithmetic_dataset with Transformers:

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

pipe = pipeline("text-generation", model="deqing/llama-3.2-350M-fourier_arithmetic_dataset")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("deqing/llama-3.2-350M-fourier_arithmetic_dataset")
model = AutoModelForCausalLM.from_pretrained("deqing/llama-3.2-350M-fourier_arithmetic_dataset", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use deqing/llama-3.2-350M-fourier_arithmetic_dataset with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deqing/llama-3.2-350M-fourier_arithmetic_dataset"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deqing/llama-3.2-350M-fourier_arithmetic_dataset",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/deqing/llama-3.2-350M-fourier_arithmetic_dataset

SGLang

How to use deqing/llama-3.2-350M-fourier_arithmetic_dataset 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 "deqing/llama-3.2-350M-fourier_arithmetic_dataset" \
    --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": "deqing/llama-3.2-350M-fourier_arithmetic_dataset",
		"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 "deqing/llama-3.2-350M-fourier_arithmetic_dataset" \
        --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": "deqing/llama-3.2-350M-fourier_arithmetic_dataset",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use deqing/llama-3.2-350M-fourier_arithmetic_dataset with Docker Model Runner:
```
docker model run hf.co/deqing/llama-3.2-350M-fourier_arithmetic_dataset
```

llama-3.2-350M-fourier_arithmetic_dataset

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

Loss: 1.6047

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: 0.0005
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 2
gradient_accumulation_steps: 16
total_train_batch_size: 32
total_eval_batch_size: 2
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 1000
num_epochs: 1

Training results

Training Loss	Epoch	Step	Validation Loss
1.8493	0.1066	1000	1.8628
1.8654	0.2132	2000	1.8692
1.8328	0.3197	3000	1.8328
1.7287	0.4263	4000	1.7136
1.6856	0.5329	5000	1.6816
1.65	0.6395	6000	1.6494
1.6304	0.7460	7000	1.6308
1.6071	0.8526	8000	1.6119
1.6022	0.9592	9000	1.6047

Framework versions

Transformers 4.48.2
Pytorch 2.3.1+cu118
Datasets 3.2.0
Tokenizers 0.21.0

Downloads last month: 6

Safetensors

Model size

0.3B params

Tensor type

BF16