Instructions to use llmware/slim-sql-onnx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use llmware/slim-sql-onnx with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="llmware/slim-sql-onnx")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("llmware/slim-sql-onnx") model = AutoModelForCausalLM.from_pretrained("llmware/slim-sql-onnx") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use llmware/slim-sql-onnx with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "llmware/slim-sql-onnx" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "llmware/slim-sql-onnx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/llmware/slim-sql-onnx
- SGLang
How to use llmware/slim-sql-onnx 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 "llmware/slim-sql-onnx" \ --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": "llmware/slim-sql-onnx", "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 "llmware/slim-sql-onnx" \ --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": "llmware/slim-sql-onnx", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use llmware/slim-sql-onnx with Docker Model Runner:
docker model run hf.co/llmware/slim-sql-onnx
| { | |
| "model": { | |
| "bos_token_id": 1, | |
| "context_length": 2048, | |
| "decoder": { | |
| "session_options": { | |
| "log_id": "onnxruntime-genai", | |
| "provider_options": [] | |
| }, | |
| "filename": "model.onnx", | |
| "head_size": 64, | |
| "hidden_size": 2048, | |
| "inputs": { | |
| "input_ids": "input_ids", | |
| "attention_mask": "attention_mask", | |
| "past_key_names": "past_key_values.%d.key", | |
| "past_value_names": "past_key_values.%d.value" | |
| }, | |
| "outputs": { | |
| "logits": "logits", | |
| "present_key_names": "present.%d.key", | |
| "present_value_names": "present.%d.value" | |
| }, | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 22, | |
| "num_key_value_heads": 4 | |
| }, | |
| "eos_token_id": 2, | |
| "pad_token_id": 0, | |
| "type": "llama", | |
| "vocab_size": 32000 | |
| }, | |
| "search": { | |
| "diversity_penalty": 0.0, | |
| "do_sample": false, | |
| "early_stopping": true, | |
| "length_penalty": 1.0, | |
| "max_length": 2048, | |
| "min_length": 0, | |
| "no_repeat_ngram_size": 0, | |
| "num_beams": 1, | |
| "num_return_sequences": 1, | |
| "past_present_share_buffer": true, | |
| "repetition_penalty": 1.0, | |
| "temperature": 1.0, | |
| "top_k": 1, | |
| "top_p": 1.0 | |
| } | |
| } |