Name: LogSage-Qwen2.5-7B-QLoRA-v0
Availability: InStock

Field	Value
Model name	`LogSage-Qwen2.5-7B-QLoRA-v0`
Repository	`auro-rirum/LogSage-Qwen2.5-7B-QLoRA-v0`
Model type	PEFT / QLoRA adapter
Base model	`unsloth/Qwen2.5-7B-Instruct-bnb-4bit`
Task	Log analysis and incident triage
Output style	Structured JSON-style diagnosis
Training stack	Unsloth, Transformers, TRL, PEFT
License	Apache-2.0
Status	v0 applied fine-tuning prototype

Why This Model Exists

Intended Use

Example Input

text

Service: checkout-api
Environment: production

Logs:
2026-05-08T12:01:33Z ERROR checkout-api request_timeout endpoint=/checkout latency_ms=12000 trace_id=abc-91
2026-05-08T12:01:34Z WARN db-pool pool_exhausted active=100 idle=0 wait_ms=4500
2026-05-08T12:01:35Z ERROR checkout-api failed_to_create_order reason=db_timeout

Recent change:
deploy_hash=9f31ac2 changed database connection pool settings

Example Output

json

{
  "issue": "Checkout requests are timing out in production.",
  "root_cause": "Database connection pool exhaustion after a recent deploy changed pool settings.",
  "severity": "high",
  "fix": "Rollback the deploy or restore the previous database pool configuration. Then inspect database saturation and checkout-api connection usage.",
  "confidence": "high"
}

Loading the Adapter Locally

python

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import torch

base_model = "unsloth/Qwen2.5-7B-Instruct-bnb-4bit"
adapter_model = "auro-rirum/LogSage-Qwen2.5-7B-QLoRA-v0"

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.float16,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained(base_model)

model = AutoModelForCausalLM.from_pretrained(
    base_model,
    quantization_config=bnb_config,
    device_map="auto",
)

model = PeftModel.from_pretrained(model, adapter_model)
model.eval()

Inference Example

python

import torch

prompt = """
You are LogSage, an incident triage assistant.

Given logs and incident context, return only structured JSON with:
issue, root_cause, severity, fix, confidence.

Logs:
2026-05-08T12:01:33Z ERROR checkout-api request_timeout endpoint=/checkout latency_ms=12000
2026-05-08T12:01:34Z WARN db-pool pool_exhausted active=100 idle=0 wait_ms=4500
2026-05-08T12:01:35Z ERROR checkout-api failed_to_create_order reason=db_timeout

Recent change:
deploy_hash=9f31ac2 changed database connection pool settings
"""

messages = [
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.2,
        do_sample=False,
    )

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

Training Data

json

{
  "instruction": "Analyze the logs and return structured diagnosis.",
  "input": "Raw logs and incident context...",
  "output": {
    "issue": "...",
    "root_cause": "...",
    "severity": "...",
    "fix": "...",
    "confidence": "..."
  }
}

Training Method

Evaluation Status

Evaluation Area	What to Measure
JSON validity	Whether outputs follow the expected schema
Schema adherence	Whether required fields are always present
Severity quality	Whether severity labels are reasonable
Root-cause quality	Whether the model identifies the correct failure pattern
Fix usefulness	Whether the suggested fix is operationally realistic
Hallucination rate	Whether the model invents services, metrics, or deploys
Latency	Local inference time per sample

Limitations

Safety Notes

Recommended Prompt Format

text

You are LogSage, an incident triage assistant.

Given logs and incident context, return only structured JSON with:
issue, root_cause, severity, fix, confidence.

Incident context:
{context}

Logs:
{logs}

Metric	Result
JSON validity	TBD
Schema adherence	TBD
Severity accuracy	TBD
Root-cause match score	TBD
Average latency	TBD

auro-rirum/
LogSage-Qwen2.5-7B-QLoRA-v0

LogSage Qwen2.5 7B QLoRA v0

Model Details