Instructions to use rtferraz/ecommerce-domain-24m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use rtferraz/ecommerce-domain-24m with Transformers:

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

pipe = pipeline("text-generation", model="rtferraz/ecommerce-domain-24m", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("rtferraz/ecommerce-domain-24m", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use rtferraz/ecommerce-domain-24m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "rtferraz/ecommerce-domain-24m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "rtferraz/ecommerce-domain-24m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/rtferraz/ecommerce-domain-24m

SGLang

How to use rtferraz/ecommerce-domain-24m 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 "rtferraz/ecommerce-domain-24m" \
    --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": "rtferraz/ecommerce-domain-24m",
		"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 "rtferraz/ecommerce-domain-24m" \
        --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": "rtferraz/ecommerce-domain-24m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use rtferraz/ecommerce-domain-24m with Docker Model Runner:
```
docker model run hf.co/rtferraz/ecommerce-domain-24m
```

ecommerce-domain-24m

File size: 734 Bytes

5d065be

{
  "architectures": [
    "DomainTransformerForCausalLM"
  ],
  "attention_probs_dropout_prob": 0.0,
  "auto_map": {
    "AutoConfig": "configuration.DomainTransformerConfig",
    "AutoModelForCausalLM": "modeling.DomainTransformerForCausalLM"
  },
  "bos_token_id": 2,
  "dtype": "float32",
  "eos_token_id": 3,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.0,
  "hidden_size": 512,
  "initializer_range": 0.02,
  "intermediate_size": 2048,
  "layer_norm_eps": 1e-05,
  "max_position_embeddings": 2048,
  "model_type": "domain_transformer",
  "num_attention_heads": 8,
  "num_hidden_layers": 6,
  "pad_token_id": 0,
  "tie_word_embeddings": true,
  "transformers_version": "5.5.0",
  "use_cache": false,
  "vocab_size": 4000
}