Instructions to use AlekseyKorshuk/amazon-reviews-input-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AlekseyKorshuk/amazon-reviews-input-output with Transformers:

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

pipe = pipeline("text-generation", model="AlekseyKorshuk/amazon-reviews-input-output")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AlekseyKorshuk/amazon-reviews-input-output")
model = AutoModelForCausalLM.from_pretrained("AlekseyKorshuk/amazon-reviews-input-output")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AlekseyKorshuk/amazon-reviews-input-output with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/AlekseyKorshuk/amazon-reviews-input-output

SGLang

How to use AlekseyKorshuk/amazon-reviews-input-output 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 "AlekseyKorshuk/amazon-reviews-input-output" \
    --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": "AlekseyKorshuk/amazon-reviews-input-output",
		"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 "AlekseyKorshuk/amazon-reviews-input-output" \
        --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": "AlekseyKorshuk/amazon-reviews-input-output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use AlekseyKorshuk/amazon-reviews-input-output with Docker Model Runner:
```
docker model run hf.co/AlekseyKorshuk/amazon-reviews-input-output
```

amazon-reviews-input-output

This model is a fine-tuned version of facebook/opt-350m on the AlekseyKorshuk/amazon-reviews-input-output dataset. It achieves the following results on the evaluation set:

Loss: 3.0486
Accuracy: 0.0373
Samples: 100
Perplexity: 21.0867
Table: <wandb.data_types.Table object at 0x7fa288d36880>

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: 5e-05
train_batch_size: 4
eval_batch_size: 32
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 1.0
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
3.2766	0.8	25	3.0587	0.0372

Framework versions

Transformers 4.25.0.dev0
Pytorch 1.12.1+cu113
Datasets 2.3.2
Tokenizers 0.12.1

Downloads last month: 4

Dataset used to train AlekseyKorshuk/amazon-reviews-input-output

Evaluation results

Accuracy on AlekseyKorshuk/amazon-reviews-input-output
self-reported

0.037