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README.md
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---
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base_model: onekq-ai/OneSQL-v0.1-Qwen-1.5B
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tags:
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- text-generation-inference
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- transformers
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- qwen2
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- mlx
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license: apache-2.0
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language:
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- en
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---
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# Introduction
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This model is the MLX version of [OneSQL-v0.1-Qwen-1.5B](https://huggingface.co/onekq-ai/OneSQL-v0.1-Qwen-1.5B). It is made for Apple Silicon.
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# Performances
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The self-evaluation EX score of the original model is **31.55** (compared to **63.33** by the 32B model on the [BIRD leaderboard](https://bird-bench.github.io/).
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The self-evaluation EX score of this MLX model is **26.08**.
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# Quick start
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To use this model, craft your prompt to start with your database schema in the form of **CREATE TABLE**, followed by your natural language query preceded by **--**.
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Make sure your prompt ends with **SELECT** in order for the model to finish the query for you. There is no need to set other parameters like temperature or max token limit.
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```python
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from mlx_lm import load, generate
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model, tokenizer = load(model="onekq-ai/OneSQL-v0.1-Qwen-1.5B-MLX-4bit")
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prompt="""CREATE TABLE students (
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id INTEGER PRIMARY KEY,
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name TEXT,
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age INTEGER,
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grade TEXT
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);
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-- Find the three youngest students
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SELECT """
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response = generate(model, tokenizer, f"<|im_start|>system\nYou are a SQL expert. Return code only.<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n")
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print(response)
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```
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The model response is the finished SQL query without **SELECT**
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```sql
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* FROM students ORDER BY age ASC LIMIT 3
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```
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# Interactivity
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Speed benchmark of this model is obtained on a MacBook Air with M1 processor and 8GB of RAM, the lower bound of Apple Silicon.
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On average, it took **4.33** seconds to generate a SQL query at **32.8** characters per second.
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