README.md

---
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`](https://huggingface.co/PipableAI/pip-SQL-1B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/PipableAI/pip-SQL-1B) for more details on the model.

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

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
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:
```bash
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](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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"
```
or 
```
./llama-server --hf-repo marroyo777/pip-SQL-1B-Q4_K_M-GGUF --hf-file pip-sql-1b-q4_k_m-imat.gguf -c 2048
```