Instructions to use firfircelik/etl-error-explainer-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use firfircelik/etl-error-explainer-v2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="firfircelik/etl-error-explainer-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("firfircelik/etl-error-explainer-v2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use firfircelik/etl-error-explainer-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "firfircelik/etl-error-explainer-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "firfircelik/etl-error-explainer-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/firfircelik/etl-error-explainer-v2

SGLang

How to use firfircelik/etl-error-explainer-v2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "firfircelik/etl-error-explainer-v2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "firfircelik/etl-error-explainer-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "firfircelik/etl-error-explainer-v2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "firfircelik/etl-error-explainer-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use firfircelik/etl-error-explainer-v2 with Docker Model Runner:
```
docker model run hf.co/firfircelik/etl-error-explainer-v2
```

ETL Error Explainer v2

Model Description

ETL Error Explainer v2 is a domain-specific instruction-tuned language model designed to analyze ETL and data pipeline failures.

Given an ETL error message and execution context (environment, severity, cloud provider), the model produces a structured JSON response containing:

category
root_cause
immediate_fix
long_term_fix

The model is intended to assist Data Engineers, Analytics Engineers, Platform Engineers, and DevOps teams during troubleshooting and incident response.

Model Details

Developed by

Fırat Çelik

Model type

Instruction-tuned causal language model

Base model

Qwen/Qwen2.5-1.5B-Instruct

Fine-tuning method

QLoRA (4-bit NF4 Quantization + LoRA)

Language

English

License

Apache-2.0

Model Sources

Hugging Face Repository

https://huggingface.co/firfircelik/etl-error-explainer-v2

Dataset

Synthetic ETL troubleshooting instruction dataset created by the author.

Intended Uses

Direct Use

The model is designed for structured ETL troubleshooting.

Example applications include:

Spark error explanation
SQL exception analysis
Databricks job failures
Azure Data Factory pipeline failures
Kafka ingestion errors
Snowflake query failures
Schema evolution issues
Authentication and permission errors
Data validation failures

Input:

Context:
environment=prod
severity=High
cloud=Azure

Analyze the following Databricks failure:

org.apache.spark.sql.AnalysisException:
cannot resolve 'customer_email'

Output:

{
  "category": "...",
  "root_cause": "...",
  "immediate_fix": "...",
  "long_term_fix": "..."
}

Downstream Uses

The model can be integrated into:

AI Data Engineering copilots
Internal troubleshooting assistants
Incident response systems
RAG applications
Chatbots
IDE assistants
Knowledge base generation
Support automation

Out-of-Scope Uses

The model is not intended for:

General-purpose chat
Code generation
SQL execution
Production system diagnosis
Security analysis
Legal advice
Medical advice

Outputs should always be reviewed before applying fixes to production environments.

Bias, Risks and Limitations

The model has been trained specifically for ETL troubleshooting.

Limitations include:

It does not inspect live systems.
It may suggest common fixes that are not applicable in every environment.
It cannot validate infrastructure configurations.
It may hallucinate missing context if insufficient information is provided.
It should complement—not replace—human expertise.

Recommendations

Best performance is achieved when prompts include:

Full error messages
Stack traces
Cloud provider
Environment (dev/test/prod)
Severity level
Relevant SQL or Spark snippets

Getting Started

from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "firfircelik/etl-error-explainer-v2"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = """
Context:
environment=prod
severity=High
cloud=Azure

Analyze the following Databricks failure:

org.apache.spark.sql.AnalysisException:
cannot resolve 'customer_email'
"""

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=256
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Base Model

Qwen/Qwen2.5-1.5B-Instruct

Fine-Tuning Method

QLoRA

4-bit NF4 quantization
LoRA adapters
PEFT

LoRA Configuration

Parameter	Value
r	16
alpha	32
dropout	0.05
target modules	q_proj, k_proj, v_proj, o_proj

Optimizer

paged_adamw_8bit

Training Hyperparameters

Parameter	Value
Epochs	3
Batch Size	4
Gradient Accumulation	4
Effective Batch Size	16
Learning Rate	2e-4
Sequence Length	512

Training Data

The model was trained on a synthetic instruction dataset covering realistic ETL failures.

Covered domains include:

Apache Spark
Databricks
SQL
Azure Data Factory
Snowflake
Kafka
Delta Lake
Data validation
Schema evolution
Authentication
File ingestion
Cloud storage

Each sample contains:

execution context
instruction
structured target JSON

Evaluation

A held-out validation split (10%) was used during training.

Additionally, the model was manually tested using unseen ETL failure scenarios.

Evaluation focused on:

JSON validity
Root cause correctness
Practical remediation quality
Instruction following

Environmental Impact

Hardware

NVIDIA Tesla T4

Platform

Kaggle Notebooks

Training Method

QLoRA (4-bit)

Using QLoRA significantly reduces GPU memory usage and energy consumption compared to full fine-tuning.

Technical Specifications

Architecture

Transformer Decoder
Causal Language Modeling

Objective

Instruction-following generation of structured ETL troubleshooting responses.

Output format:

{
  "category": "...",
  "root_cause": "...",
  "immediate_fix": "...",
  "long_term_fix": "..."
}

Supported Domains

Domain	Supported
Apache Spark	✅
Databricks	✅
SQL	✅
Azure Data Factory	✅
Kafka	✅
Snowflake	✅
Delta Lake	✅
Schema Evolution	✅
Authentication	✅
Data Validation	✅
Cloud Storage	✅

Citation

@misc{celik2026etlexplainer,
  author = {Fırat Çelik},
  title = {ETL Error Explainer v2},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/firfircelik/etl-error-explainer-v2}}
}

Contact

GitHub

https://github.com/firfircelik

Hugging Face

https://huggingface.co/firfircelik

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Qwen/Qwen2.5-1.5B

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