metadata

license: mit
language:
  - en
metrics:
  - accuracy
pipeline_tag: text-generation
widget:
  - text: >-
      <schema>CREATE TABLE radio(age VARCHAR, radio_id VARCHAR,  frequency
      VARCHAR, wavelength VARCHAR); CREATE TABLE  radio_faults(radio_id VARCHAR,
      fault_description VARCHAR)</schema><question>Get the radio id and defect
      descriptions of radios that have wavelength greater than 30
      ?</question><sql>
    example_title: example1
  - text: >-
      <schema>CREATE TABLE system(JobID: String,GID: String, UID: String,
      Start:Time(yyyy/mm/dd), End: Time,ElapsedRaw: Time, CPUTimeRAW:
      Time,NCPUS: Number,NNodes: Number, NodeList: List,  State:String,
      Timelimit: Time);</schema><question>Get UID and job id for Jobs that
      started on Jan 20 , 2023</question><sql>
    example_title: example2
  - text: >-
      <schema>CREATE TABLE department (Department_ID number, Name text, Creation
      text, Ranking number, Budget_in_Billions number, Num_Employees number)
      which has Department_ID as primary key abd CREATE TABLE head (head_ID
      number, name text, born_state text, age number) which has head_ID as
      primary key and CREATE TABLE management (department_ID number, head_ID
      number, temporary_acting text) which has department_ID as primary
      key</schema><question>
    example_title: example3
tags:
  - code
  - sql
  - text2sql
  - instruction_tuned
  - jax
  - pytorch
  - 1b
  - expert
  - llama-cpp
  - gguf-my-repo
datasets:
  - PipableAI/spider-bird
base_model: PipableAI/pip-SQL-1B

marroyo777/pip-SQL-1B-Q4_K_M-GGUF

This model was converted to GGUF format from PipableAI/pip-SQL-1B using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo marroyo777/pip-SQL-1B-Q4_K_M-GGUF --hf-file pip-sql-1b-q4_k_m-imat.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo marroyo777/pip-SQL-1B-Q4_K_M-GGUF --hf-file pip-sql-1b-q4_k_m-imat.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo marroyo777/pip-SQL-1B-Q4_K_M-GGUF --hf-file pip-sql-1b-q4_k_m-imat.gguf -p "The meaning to life and the universe is"

./llama-server --hf-repo marroyo777/pip-SQL-1B-Q4_K_M-GGUF --hf-file pip-sql-1b-q4_k_m-imat.gguf -c 2048