htunn/gemma-4-e2b-aiops-hf

Gemma 4 E2B — AIOps Orchestrator (Safetensors / HF format)

Fine-tuned from google/gemma-4-E2B-it using Apple MLX LoRA on an M3 Pro (18 GB). The model is specialised as an autonomous AIOps orchestrator agent that maps infrastructure incident telemetry to structured, execution-ready JSON control schemas.

Looking for the quantised GGUF variant?
→ htunn/gemma-4-e2b-aiops-gguf

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Model Details

Field	Value
Base model	google/gemma-4-E2B-it
Architecture	Gemma4ForConditionalGeneration
Precision	BF16
Parameters	~5B
Fine-tune method	LoRA (MLX)
LoRA rank / scale	8 / 20.0
Layers tuned	16
Training iterations	600
Batch size	1
Learning rate	1e-4
Max sequence length	2048
Training hardware	Apple M3 Pro, 18 GB Unified Memory
Framework	mlx-lm
Dataset	htunn/aiops-gemma
License	Apache 2.0

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Intended Use

The model receives an [AIOps-Agent] incident description spanning one or more infrastructure domains and returns a single JSON object containing:

• action — the remediation action to take
• target_* — specific resource identifiers
• steps — ordered list of discrete remediation steps
• api_calls — REST API calls to execute
• priority / risk — severity classification

Covered Domains

• Kubernetes — pod failures, etcd quorum loss, network partitions, cert-manager, autoscaling, storage
• Nutanix — CVM network partitions, multi-cluster Prism Central, NearSync/async replication, Calm blueprints, Karbon, Era, stretched clusters, Flow microsegmentation
• VMware — ESXi, vSAN, NSX-T, DRS, HA failover, vMotion, vCenter SSO
• Active Directory — replication, FSMO, SYSVOL, cross-site failures, forest trusts
• ADFS / PKI — certificate chain validation, OCSP, CRL, intermediate CA renewal, MFA providers
• Windows Server — domain controllers, IIS, WSUS, Hyper-V, DFS, file services

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Usage

Transformers

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "htunn/gemma-4-e2b-aiops-hf"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

prompt = "[AIOps-Agent] Kubernetes etcd leader election failed due to network partition isolating etcd-01. API server returning 503. etcd cluster lost quorum."

messages = [{"role": "user", "content": prompt}]
inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True,
).to(model.device)

outputs = model.generate(inputs, max_new_tokens=256, do_sample=False)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

MLX (Apple Silicon)

pip install mlx-lm
mlx_lm.generate \
  --model htunn/gemma-4-e2b-aiops-hf \
  --prompt "[AIOps-Agent] Nutanix stretched cluster witness unreachable. RF1 degraded mode active." \
  --max-tokens 256

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Example Output

Input:

[AIOps-Agent] Kubernetes ingress controller presenting incomplete TLS certificate chain.
Intermediate CA missing from secret bundle. External clients receiving SSL handshake errors.

Output:

{
  "action": "fix_incomplete_cert_chain",
  "target_service": "ingress-nginx",
  "missing_element": "intermediate_ca_certificate",
  "steps": [
    "retrieve_full_certificate_chain_from_pki",
    "bundle_leaf_cert_with_intermediate_ca_cert",
    "update_kubernetes_tls_secret_with_complete_chain",
    "rolling_restart_ingress_controller_pods",
    "validate_chain_with_openssl_s_client"
  ],
  "commands": [
    "openssl s_client -connect ingress.example.com:443 -showcerts",
    "openssl verify -CAfile chain.pem cert.pem"
  ],
  "api_calls": [
    "PATCH /api/v1/namespaces/ingress-nginx/secrets/tls-wildcard-cert"
  ],
  "priority": "high"
}

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Training Data

Trained on htunn/aiops-gemma — 90 hand-crafted incident scenarios (73 train / 17 validation) in Gemma chat format, covering:

• Network partition events across K8s, Nutanix, AD, and NSX-T
• Certificate chain validation failures (incomplete chains, expired intermediates, OCSP, path-length constraints)
• Multi-cluster Nutanix operations (Prism Central, NearSync, Calm, Era, Karbon, stretched clusters)
• Cross-domain cascading failures involving 3–4 infrastructure layers simultaneously

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Limitations

• Output format is tailored to the training schema; prompts not prefixed with [AIOps-Agent] may produce inconsistent results.
• The model does not execute actions — it produces decision schemas for an orchestration layer to consume.
• Coverage is limited to the incident types represented in the training set.

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Related Repos

Repo	Description
htunn/gemma-4-e2b-aiops-gguf	Q4_K_M GGUF — run with Ollama or llama.cpp
htunn/aiops-gemma	Training dataset (JSONL)
GitHub: htunn/aiops-gemma4	Full fine-tuning pipeline source