", line 1229, in \_handle\_fromlist
> File "C:\\SQl coder\\sqlenv\\Lib\\site-packages\\transformers\\utils\\import\_utils.py", line 1373, in \_\_getattr\_\_
> module = self.\_get\_module(self.\_class\_to\_module\[name\])
> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
> File "C:\\SQl coder\\sqlenv\\Lib\\site-packages\\transformers\\utils\\import\_utils.py", line 1385, in \_get\_module
> raise RuntimeError(
> RuntimeError: Failed to import transformers.integrations.bitsandbytes because of the following error (look up to see its traceback):
>
> CUDA Setup failed despite GPU being available. Please run the following command to get more information:
>
> python -m bitsandbytes
>
> Inspect the output of the command and see if you can locate CUDA libraries. You might need to add them
> to your LD\_LIBRARY\_PATH. If you suspect a bug, please take the information from python -m bitsandbytes
> and open an issue at: https://github.com/TimDettmers/bitsandbytes/issues
> \`\`\`
>
> \### Reproduction
>
> https://github.com/defog-ai/sqlcoder/blob/main/defog\_sqlcoder\_colab.ipynb
>
> \### Expected behavior
>
> i want to run defog.ai SQLCoder-7b-2\`import streamlit as st
> import torch
> from transformers import AutoTokenizer, AutoModelForCausalLM
> import sqlparse
> import sqlite3
>
> \# Model loading and configuration
> model\_name = "defog/sqlcoder-7b-2"
> tokenizer = AutoTokenizer.from\_pretrained(model\_name)
>
> if torch.cuda.is\_available():
> available\_memory = torch.cuda.memory\_allocated()
> if available\_memory \> 15e9:
> model = AutoModelForCausalLM.from\_pretrained(
> model\_name,
> trust\_remote\_code=True,
> torch\_dtype=torch.float16,
> device\_map="auto",
> use\_cache=True,
> )
> else:
> model = AutoModelForCausalLM.from\_pretrained(
> model\_name,
> trust\_remote\_code=True,
> load\_in\_8bit=True,
> device\_map="auto",
> torch\_dtype=torch.float16,
> use\_cache=True,
> )
> else:
> model = AutoModelForCausalLM.from\_pretrained(
> model\_name, trust\_remote\_code=True, use\_cache=True
> )
>
> prompt = """### Task
> Generate a SQL query to answer \[QUESTION\]{question}\[/QUESTION\]
>
> \###Instructions
> \- if the question cannot be answered given the database schema, return "I do not know"
> \- Every helpdesk ticket is associated to a space or an equipment mandatorily.
> \- Every equipment or space is related to a Block in a site
>
>
>
> \### Database Schema
> CREATE TABLE website\_support\_ticket(id INTEGER PRIMARY KEY,
> sla\_active BOOLEAN, --SLA is active if it is true else it is inactive
> asset\_id INTEGER, --Space for which the ticket is created
> equipment\_id INTEGER, --Equipment for which the ticket is created
> equipment\_location\_id INTEGER, --Space where the Equipment is located
> maintenance\_team\_id INTEGER, --Maintenance Team that is responsible for the ticket actions
> at\_start\_mro BOOLEAN, --Photo is required to start a work order
> at\_done\_mro BOOLEAN, --Photo is required to close a work order
> at\_review\_mro BOOLEAN, --Photo is required to review a work order
> mro\_order\_id INTEGER, --Order related to the ticket
> employee\_id INTEGER, --Employee related to the ticket
> pause\_reason\_id INTEGER, --Reason for Pause
> equip\_block\_id INTEGER, --Block of an equipment for which the ticket is created
> space\_block\_id INTEGER, --Block of an space for which the ticket is created
> requestee\_id INTEGER, --Requestor of the ticket
> region\_id INTEGER, --Region of the ticket
> is\_reopen BOOLEAN, --Ticket was reopned if this is set to True
> reopen\_count INTEGER, --Number of times this ticket was reopened
> on\_hold\_date TIMESTAMP WITHOUT TIME ZONE, --Date on which the ticket was moved to On-Hold
> doc\_count INTEGER, --Count of Attachments
> sla\_end\_date TIMESTAMP WITHOUT TIME ZONE, --Planned End date for SLA
> priority\_id INTEGER, --Priority of the Ticket
> category\_id INTEGER, --Category of the Problem
> sub\_category\_id INTEGER, --Sub Category of the Problem
> state\_id INTEGER, --Status of the ticket (Open, InProgress, Closed, Paused)
> company\_id INTEGER, --Company of the ticket
> close\_time TIMESTAMP WITHOUT TIME ZONE, --Ticket Closed Date time
> closed\_by\_id INTEGER, --Technician who closed the ticet
> ticket\_type CHARACTER VARYING, --Proactive or Reactive
> sla\_status CHARACTER VARYING, --To show within SLA or SLA elapsed
> state\_category\_id CHARACTER VARYING, --Category to which the Status belongs to
> subject CHARACTER VARYING, --Subject line of the Problem
> issue\_type CHARACTER VARYING, --Issue Type of the Ticket
> close\_comment CHARACTER VARYING, --Comments that was enetered while closing the ticket
> current\_escalation\_level CHARACTER VARYING, --To show the current escalationlevel
> type\_category CHARACTER VARYING, --Type category of the ticket
> state\_name CHARACTER VARYING, --State to which the site belongs to
> city\_name CHARACTER VARYING, --City to which the site belongs to
> last\_commented\_by CHARACTER VARYING, --Comment
> region CHARACTER VARYING, --Region of the ticket
> mro\_state CHARACTER VARYING, --Status of the Work order
>
> );
>
> CREATE TABLE res\_company (
> id INTEGER PRIMARY KEY,
> name VARCHAR(20)
> );
>
> CREATE TABLE mro\_maintenance\_team(
> id INTEGER INTEGER PRIMARY KEY,
> name VARCHAR VARCHAR(20)
> );
>
> CREATE TABLE mro\_equipment\_location(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50)
> );
>
> CREATE TABLE mro\_equipment(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50)
> );
>
> CREATE TABLE website\_support\_ticket\_state(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50));
>
> CREATE TABLE mro\_order(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50));
>
> CREATE TABLE website\_support\_ticket\_category(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50))
> ;
>
> CREATE TABLE website\_support\_ticket\_subcategory(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50));
>
> CREATE TABLE website\_support\_ticket\_priority(
> id INTEGER PRIMARY KEY,
> name VARCHAR(50));
>
>
>
> \-website\_support\_ticket.company\_id can be joined with res\_company.id
> \-website\_support\_ticket.maintenance\_team\_id can be joined with mro\_maintenance\_team.id
> \-website\_support\_ticket.asset\_id can be joined with mro\_equipment\_location.id
> \-website\_support\_ticket.equipment\_id can be joined with mro\_equipment.id
> \-website\_support\_ticket.state\_id can be joined with website\_support\_ticket\_state.id
> \-website\_support\_ticket.mro\_order\_id can be joined with mro\_order.id
> \-website\_support\_ticket.category\_id can be joined with website\_support\_ticket\_category.id
> \-website\_support\_ticket.sub\_category\_id can be joined with website\_support\_ticket\_subcategory.id
> \-website\_support\_ticket.priority\_id can be joined with website\_support\_ticket\_priority.id
>
>
>
>
> \### Answer
> Given the database schema, here is the SQL query that answers \[QUESTION\]{question}\[/QUESTION\]
> \[SQL\]
> """
> def generate\_query(question):
> updated\_prompt = prompt.format(question=question)
> inputs = tokenizer(updated\_prompt, return\_tensors="pt").to("cuda")
> generated\_ids = model.generate(
> \*\*inputs,
> num\_return\_sequences=1,
> eos\_token\_id=tokenizer.eos\_token\_id,
> pad\_token\_id=tokenizer.eos\_token\_id,
> max\_new\_tokens=400,
> do\_sample=False,
> num\_beams=1,
> )
> outputs = tokenizer.batch\_decode(generated\_ids, skip\_special\_tokens=True)
>
> torch.cuda.empty\_cache()
> torch.cuda.synchronize()
> return sqlparse.format(outputs\[0\].split("\[SQL\]")\[-1\], reindent=True)
>
>
> def execute\_sql(question, db\_file):
> query = generate\_query(question)
>
> conn = sqlite3.connect(db\_file)
> cursor = conn.cursor()
>
> try:
> cursor.execute(query)
>
> # Fetch column names
> columns = \[col\[0\] for col in cursor.description\]
>
> # Fetch results into a pandas DataFrame
> df = pd.DataFrame(cursor.fetchall(), columns=columns)
>
> # Print the result as a table
> return df.to\_markdown(index=False)
> except sqlite3.OperationalError as e:
> if "ILIKE" in str(e):
> query = query.replace("ILIKE", "LIKE")
> return execute\_query(query, db\_file)
> except sqlite3.Error as e:
> print("Error executing query:", e)
> return None
>
> finally:
> cursor.close()
> conn.close()
>
>
> \# Streamlit app
> st.title("SQL Code Generator")
>
> \# Input field for the question
> user\_question = st.text\_input("Enter your question about the database:")
>
> \# Button to generate the SQL query
> if st.button("Generate SQL"):
> if user\_question:
> # Generate SQL query and display it
> generated\_sql = generate\_query(user\_question)
> st.write("Generated SQL Query:")
> st.code(generated\_sql)
>
> # Connect to the database (replace with your database file path)
> db\_file = "your\_database.db"
> if db\_file:
> # Execute the query and display the results
> result = execute\_sql(user\_question, db\_file)
> if result:
> st.write("Results:")
> st.markdown(result)
> else:
> st.write("No results found.")
> else:
> st.warning("Please enter a question.")
>
> \`
>
> this is the code i am trying to run
---
**Author:**  [@LuuWee](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FLuuWee)%5C
**Post date:** [May 8, 2025, 8:17am UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ferror-in-autotrain-training%2F154069%2F3 "2025-05-08T08:17:02Z")
I found a solution by myself. Im using the free plan to there is only cpu to use and no gpu. I had to change some of the parameters. This is what i did for anyone who is wondering
Distributed Backend from ddp to deepspeed
Mixed precision from fp16 to none
PEFT/LoRA from true to false
Im not exactly sure what did the trick but its training now
---
**Author:**  [@system](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2Fsystem)%5C
**Post date:** [May 8, 2025, 8:17pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Ferror-in-autotrain-training%2F154069%2F4 "2025-05-08T20:17:56Z")
This topic was automatically closed 12 hours after the last reply. New replies are no longer allowed.