Create README.md

README.md

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+---
+base_model: onekq-ai/OneSQL-v0.1-Qwen-7B
+tags:
+- text-generation-inference
+- transformers
+- qwen2
+- awq
+license: apache-2.0
+language:
+- en
+---
+
+# Introduction
+
+This model is the AWQ version of [OneSQL-v0.1-Qwen-7B](https://huggingface.co/onekq-ai/OneSQL-v0.1-Qwen-7B).
+
+# Performances
+
+The self-evaluation EX score of the original model is **56.19** (compared to **63.33** by the 32B model on the [BIRD leaderboard](https://bird-bench.github.io/).
+The self-evaluation score of this AWQ model is **43.54%**.
+
+# Quick start
+
+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 **--**.
+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.
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="onekq-ai/OneSQL-v0.1-Qwen-7B-AWQ")
+sampling_params = SamplingParams(temperature=0.7, max_tokens=200)
+
+prompt="CREATE TABLE students (
+    id INTEGER PRIMARY KEY,
+    name TEXT,
+    age INTEGER,
+    grade TEXT
+);
+
+-- Find the three youngest students
+SELECT "
+
+outputs = llm.generate(prompt, sampling_params)
+print(outputs[0].outputs[0].text.strip())
+```
+
+The model response is the finished SQL query without **SELECT**
+```sql
+* FROM students ORDER BY age ASC LIMIT 3
+```
+
+# Caveats
+
+The performance drop from the original model is due to quantization itself, and the lack of beam search support in the vLLM framework. Use at your own discretion.