Update README.md
Browse files
README.md
CHANGED
@@ -8,22 +8,19 @@ tags:
|
|
8 |
- merge
|
9 |
|
10 |
---
|
11 |
-
# sql-
|
12 |
|
13 |
-
|
14 |
-
|
15 |
-
## Merge Details
|
16 |
-
### Merge Method
|
17 |
-
|
18 |
-
This model was merged using the SLERP merge method.
|
19 |
|
20 |
### Models Merged
|
21 |
|
|
|
|
|
22 |
The following models were included in the merge:
|
23 |
* [ajibawa-2023/Code-Llama-3-8B](https://huggingface.co/ajibawa-2023/Code-Llama-3-8B)
|
24 |
* [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
|
25 |
|
26 |
-
### Configuration
|
27 |
|
28 |
The following YAML configuration was used to produce this model:
|
29 |
|
@@ -45,3 +42,44 @@ parameters:
|
|
45 |
- value: 0.4 # fallback for rest of tensors
|
46 |
dtype: bfloat16
|
47 |
```
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
8 |
- merge
|
9 |
|
10 |
---
|
11 |
+
# llama3-8b-code-sql-slerp
|
12 |
|
13 |
+
llama3-8b-code-sql-slerp is a merge of two fine tuned Llama 3 8B models for coding, intended to have a solid programming foundation with an expertise in SQL.
|
|
|
|
|
|
|
|
|
|
|
14 |
|
15 |
### Models Merged
|
16 |
|
17 |
+
Merge of pre-trained language models merged using the SLERP merge method with [mergekit](https://github.com/cg123/mergekit).
|
18 |
+
|
19 |
The following models were included in the merge:
|
20 |
* [ajibawa-2023/Code-Llama-3-8B](https://huggingface.co/ajibawa-2023/Code-Llama-3-8B)
|
21 |
* [defog/llama-3-sqlcoder-8b](https://huggingface.co/defog/llama-3-sqlcoder-8b)
|
22 |
|
23 |
+
### 🧩 Configuration
|
24 |
|
25 |
The following YAML configuration was used to produce this model:
|
26 |
|
|
|
42 |
- value: 0.4 # fallback for rest of tensors
|
43 |
dtype: bfloat16
|
44 |
```
|
45 |
+
|
46 |
+
### 💻 Usage
|
47 |
+
|
48 |
+
Loading in 8-bit Quantization
|
49 |
+
|
50 |
+
```python
|
51 |
+
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
|
52 |
+
|
53 |
+
tokenizer = AutoTokenizer.from_pretrained("AdamLucek/sql-expert")
|
54 |
+
model = AutoModelForCausalLM.from_pretrained(
|
55 |
+
"AdamLucek/sql-expert",
|
56 |
+
device_map="cuda",
|
57 |
+
quantization_config=BitsAndBytesConfig(load_in_8bit=True)
|
58 |
+
)
|
59 |
+
|
60 |
+
# Prepare the input text
|
61 |
+
input_text = "Can you write a query to retrieve the names and email addresses of all customers who have made purchases totaling over $1000 in the last month from our 'sales' database?"
|
62 |
+
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
|
63 |
+
|
64 |
+
# Generate the output
|
65 |
+
outputs = model.generate(
|
66 |
+
**input_ids,
|
67 |
+
max_new_tokens=256,
|
68 |
+
pad_token_id=tokenizer.eos_token_id
|
69 |
+
)
|
70 |
+
|
71 |
+
# Decode and print the generated text
|
72 |
+
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
73 |
+
```
|
74 |
+
|
75 |
+
**Output**
|
76 |
+
>\```sql
|
77 |
+
>SELECT c.name, c.email
|
78 |
+
>FROM customers c
|
79 |
+
>JOIN sales s ON c.customer_id = s.customer_id
|
80 |
+
>WHERE s.purchase_date >= DATE_SUB(CURRENT_DATE, INTERVAL 1 MONTH)
|
81 |
+
>GROUP BY c.name, c.email
|
82 |
+
>HAVING SUM(s.amount) > 1000;
|
83 |
+
>\```
|
84 |
+
>
|
85 |
+
>This query joins the 'customers' and'sales' tables on the 'customer_id' field, filters for sales made in the last month, groups the results by customer name and email, and then applies a condition to only include customers whose total purchase amount exceeds $1000. The result will be a list of names and email addresses for customers who have made purchases totaling over $1000 in the last month.
|