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
| { | |
| "_name_or_path": "llmware/slim-sql-onnx", | |
| "aib_version": "", | |
| "architectures": [ | |
| "LlamaForCausalLM" | |
| ], | |
| "attention_bias": false, | |
| "attention_dropout": 0.0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "hidden_act": "silu", | |
| "hidden_size": 2048, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 5632, | |
| "max_position_embeddings": 2048, | |
| "mlp_bias": false, | |
| "model_type": "llama", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 22, | |
| "num_key_value_heads": 4, | |
| "pretraining_tp": 1, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "tie_word_embeddings": false, | |
| "trained": "custom training", | |
| "training_dataset": "", | |
| "transformers_version": "4.41.2", | |
| "use_cache": true, | |
| "vocab_size": 32000, | |
| "prompt_wrapper": "human_bot", | |
| "description": "slim-sql is a text-to-sql model.", | |
| "prompt_format": "<human> {table_schema} \n {question} \n<bot>:", | |
| "output_format": "{sql}", | |
| "tokenizer_local": "tokenizer_tl.json", | |
| "tokenizer_config": {"bos_id":[1], "bos_token":["<s>"], "eos_id":[2],"eos_token":["</s>"]}, | |
| "model_parent": "llmware/slim-sql-1b-v0", | |
| "description": "Text-to-SQL model from llmware - finetuned on tiny-llama - 1.1 parameter base", | |
| "quantization": "int4", | |
| "model_family": "ONNXGenerativeModel", | |
| "parameters": 1.1, | |
| "primary_keys": ["sql"], | |
| "output_values": ["{{sql statement}}"], | |
| "publisher": "llmware", | |
| "release_date": "september 2024", | |
| "function_call": "sql", | |
| "test_params": ["sql"], | |
| "test_set": [ | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "Which customers are VIP customers?", | |
| "answer": "SELECT * FROM customers WHERE vip_customer='yes'" | |
| }, | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "What is the annual spend for customer Rachel Michaels?", | |
| "answer": "SELECT annual_spend FROM customers WHERE customer_name='Rachel Michaels'" | |
| }, | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "How many customers spend more than $1000 per year?", | |
| "answer": "SELECT COUNT(*) FROM customers WHERE annual_spend > $1000" | |
| }, | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "Who are the customers with gold customer level?", | |
| "answer": "SELECT customer_name FROM customers WHERE customer_level = 'gold'" | |
| }, | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "Which customer has account number 9382035?", | |
| "answer": "SELECT * FROM customers WHERE account_number = 9382035" | |
| }, | |
| { | |
| "context": "CREATE TABLE customers (customer_name text, account_number integer,customer_level text, vip_customer text, annual_spend text, user_name text)", | |
| "query": "What is the account number of customer Susanna Jones?", | |
| "answer": "SELECT account_number FROM customers WHERE customer_name='Susanna Jones'" | |
| }, | |
| { | |
| "context": "CREATE TABLE library (library_name text, unique_doc_id integer, documents integer, blocks integer, images integer, pages integer, tables integer, account_name text)", | |
| "query": "How many pages are in the human resources library?", | |
| "answer": "SELECT pages FROM library WHERE library_name = 'human resources'" | |
| }, | |
| { | |
| "context": "CREATE TABLE library (library_name text, unique_doc_id integer, documents integer, blocks integer, images integer, pages integer, tables integer, account_name text)", | |
| "query": "Which libraries have more than 1000 images?", | |
| "answer": "SELECT * FROM library WHERE images > 1000" | |
| }, | |
| { | |
| "context": "CREATE TABLE library (library_name text, unique_doc_id integer, documents integer, blocks integer, images integer, pages integer, tables integer, account_name text)", | |
| "query": "How many blocks are in the finance library?", | |
| "answer": "SELECT blocks FROM library WHERE library_name = 'finance library'" | |
| }, | |
| { | |
| "context": "CREATE TABLE library (library_name text, unique_doc_id integer, documents integer, blocks integer, images integer, pages integer, tables integer, account_name text)", | |
| "query": "What is a list of all of the libraries?", | |
| "answer": "SELECT * FROM library" | |
| }, | |
| { | |
| "context": "CREATE TABLE library (library_name text, unique_doc_id integer, documents integer, blocks integer, images integer, pages integer, tables integer, account_name text)", | |
| "query": "Which library has unique_doc_id of 8329?", | |
| "answer": "SELECT * FROM library WHERE unique_doc_id = 8329" | |
| } | |
| ] | |
| } | |