Debugging Azure OpenAI Fine-Tuning: A Journey from Cryptic Errors to Multiple Solutions

The Problem That Started It All

I was working on implementing fine-tuning for a rice price forecasting model when I hit an unexpected wall. My Python script crashed with this error:

$ python fine_tuning/create_finetune.py
openai.BadRequestError: Error code: 400 - {'error': {'code': 'invalidPayload', 
'message': 'The specified base model does not support fine-tuning.'}}

At first glance, this seemed straightforward - wrong model, right? But as I would soon discover, the real issue ran much deeper.

My Investigation Approach

Instead of immediately changing the model name, I decided to investigate systematically. First, I examined the script to understand what was actually configured:

$ cat fine_tuning/create_finetune.py

This revealed I was using gpt-35-turbo-0613 with Azure OpenAI. The model should support fine-tuning according to documentation, so something else was wrong.

Checking the Environment Configuration

I inspected my API version, suspecting an outdated version might be the culprit:

$ grep "AZURE_OPENAI_API_VERSION" .env
AZURE_OPENAI_API_VERSION=2024-08-01-preview

The API version was current. Next, I verified my uploaded training files were accessible:

$ python -c "import os; from openai import AzureOpenAI; from dotenv import load_dotenv; 
load_dotenv(); client = AzureOpenAI(api_key=os.getenv('AZURE_OPENAI_KEY'), 
api_version=os.getenv('AZURE_OPENAI_API_VERSION'), 
azure_endpoint=os.getenv('AZURE_OPENAI_ENDPOINT')); 
file = client.files.retrieve('file-b69f759da0bf4e7a896b48aad1f19ef4'); 
print(f'File exists: {file.id}, Purpose: {file.purpose}, Status: {file.status}')"

File exists: file-b69f759da0bf4e7a896b48aad1f19ef4, Purpose: fine-tune, Status: processed

Files were fine. I tried different model variants:

$ sed -i 's/model="gpt-35-turbo-0613"/model="gpt-35-turbo"/' fine_tuning/create_finetune.py
$ python fine_tuning/create_finetune.py
# Same error

Even after trying gpt-35-turbo-1106, the error persisted. This wasn't a model naming issue.

The Real Discovery

I checked my endpoint format more carefully:

$ python -c "import os; from dotenv import load_dotenv; load_dotenv(); 
endpoint = os.getenv('AZURE_OPENAI_ENDPOINT'); 
print(f'Endpoint: {endpoint}'); 
print(f'Type: {\"Cognitive Services\" if \"api.cognitive\" in endpoint else \"Azure OpenAI\"}')"

Endpoint: https://eastus.api.cognitive.microsoft.com/
Type: Cognitive Services

There it was - I was using a Cognitive Services endpoint, not an Azure OpenAI endpoint. This was the root cause: Cognitive Services endpoints don't support fine-tuning, only Azure OpenAI Service endpoints do.

Building Multiple Solutions

Rather than getting stuck on this limitation, I decided to implement three different approaches.

Solution 1: RAG Pattern (Immediate Workaround)

First, I created a RAG-based alternative that would work with my existing Cognitive Services endpoint:

$ cat > fine_tuning/rag_alternative.py << 'EOF'
# RAG approach instead of fine-tuning - works with Cognitive Services
import os, json
from openai import AzureOpenAI
from dotenv import load_dotenv
load_dotenv()
client = AzureOpenAI(
    api_key=os.getenv("AZURE_OPENAI_KEY"),
    api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT")
)
# Load training data as context examples
with open("fine_tuning/data/train_rice_thai_5pct.jsonl", "r") as f:
    examples = [json.loads(line) for line in f][:5]
def predict_with_rag(user_input):
    system_prompt = "You are a rice price forecasting expert."
    few_shot = "\n".join([
        f"Input: {ex['messages'][1]['content']}\nOutput: {ex['messages'][2]['content']}"
        for ex in examples if len(ex['messages']) > 2
    ])
    messages = [
        {"role": "system", "content": system_prompt},
        {"role": "user", "content": f"Examples:\n{few_shot}\n\nNow predict:\n{user_input}"}
    ]
    response = client.chat.completions.create(
        model="gpt-35-turbo",
        messages=messages,
        temperature=0.3
    )
    return response.choices[0].message.content
EOF

After fixing the file path, this worked immediately:

$ python fine_tuning/rag_alternative.py
RAG-based prediction ready. No fine-tuning needed.
Uses few-shot learning with your training examples.

Solution 2: OpenAI Direct API

Since I couldn't immediately provision an Azure OpenAI resource, I implemented direct OpenAI API integration. After adding my OpenAI API key to the .env file, I uploaded the training data:

$ python fine_tuning/upload_to_openai.py
Training file uploaded: file-JcBJuUfiJdK5Foz1RfrSeZ
Validation file uploaded: file-VXAvLARHnMibkcwxBfb6KZ

Then I created the fine-tuning job:

$ python fine_tuning/create_finetune_openai_complete.py
Fine-tuning job created: ftjob-7pc0kvPb7uefsGQif7o1WLm6
Status: validating_files

I built a monitoring script to track the job:

$ python fine_tuning/monitor_job.py
Job ID: ftjob-7pc0kvPb7uefsGQif7o1WLm6
Status: running
Model: gpt-3.5-turbo-0125

Solution 3: Azure OpenAI Documentation

For completeness, I documented the proper Azure OpenAI setup process for when I could provision the correct resource type.

Lessons Learned

This debugging session taught me several valuable lessons:

1. Error messages can be misleading - The "model does not support fine-tuning" error wasn't about the model at all, but about the endpoint type.

2. Systematic investigation pays off - By checking each component methodically rather than making assumptions, I found the real issue.

3. Multiple solutions provide resilience - Having three different approaches meant I wasn't blocked by infrastructure limitations.

4. Understanding the architecture matters - The distinction between Cognitive Services and Azure OpenAI Service endpoints is crucial for certain features.

The Resolution

In the end, I had:

• A working RAG solution using my existing Cognitive Services endpoint
• An active fine-tuning job running on OpenAI's platform
• Clear documentation for future Azure OpenAI provisioning

The initial error that seemed like a simple model configuration issue turned into a deeper exploration of Azure's service architecture and led to implementing multiple robust solutions.

CLI Reference for Debugging

Here are the key commands I used during this debugging session:

# File inspection
cat <filename>                           # View file contents
ls -la <directory>                       # List directory contents with details
find . -name "*.jsonl"                   # Find files by pattern

# Configuration checking
grep "PATTERN" .env                      # Search for configuration values
grep -E "PATTERN1|PATTERN2" file         # Search with regex patterns

# File modification
sed -i 's/old/new/' file                 # In-place file editing
sed -i 's|path1|path2|' file             # Using different delimiter for paths

# Python one-liners for testing
python -c "import module; print(test)"   # Quick Python tests

# Git investigation
git branch -a                             # List all branches
git ls-tree -r branch --name-only        # List files in branch
git show branch:file                     # View file from another branch

# Process monitoring
python script.py                         # Run Python scripts
echo "text" > file                       # Create/overwrite file
cat > file << 'EOF'                      # Create multi-line file

# Environment verification
grep -r "pattern" directory              # Recursive search
head -n <number> file                     # View first N lines

These commands formed my debugging toolkit, allowing me to systematically investigate configuration files, test API connections, modify scripts, and implement solutions without leaving the terminal.

If you enjoyed this article, you can also find it published on LinkedIn and Medium.