A 2B parameter K8sGPT MCP tool-use specialist, trained with QLoRA on Qwen3.5-2B and quantized to Q4_K_M for CPU-only deployment. The headline deployable (edge+ tier) of the kubelm project — supersedes the edge tier kubelm-qwen2.5-1.5b-v1.

TL;DR

On the 35-scenario v0.3 evaluation library, served via llama-server at temperature 0:

metric	qwen2.5-7b (reference)	kubelm-qwen2.5-1.5b-v1 (edge)	kubelm-qwen3.5-2b-v1
`conclusion_rubric_passed`	28 / 35	29 / 35	32 / 35
`reference_calls_passed`	28 / 35	27 / 35	32 / 35
`fabrications` (grounding v2)	8	21	3
`schema_passed` (tool-call)	34 / 35	32 / 35	35 / 35
`termination_label == complete`	33 / 35	33 / 35	35 / 35
`narrative_inconsistencies`	0	0	0

Beats Qwen 2.5 7B on every metric at ~1/3 the footprint, with ~3× lower fabrication rate. Zero name and argument hallucinations across all 35 trajectories. Full row in eval/results/summaries/shape-d-2026-05-27.json.

Quickstart (recommended: llama-server)

ollama 0.23.1's qwen3next loader currently rejects this GGUF (see Known issues). Use llama.cpp directly:

bash

# Boot the model (Apple Silicon shown; on Linux drop -ngl or set 0)
brew install llama.cpp   # or: build from https://github.com/ggml-org/llama.cpp
huggingface-cli download rbentaarit/kubelm-qwen3.5-2b-v1 \
    kubelm-edge.Q4_K_M.gguf --local-dir .

llama-server \
    -m kubelm-edge.Q4_K_M.gguf \
    --host 127.0.0.1 --port 8088 \
    --jinja \
    -c 16384 \
    -ngl 99

Three serving-config notes that are load-bearing:

--jinja uses the model's embedded Qwen 3.5 chat template (including its tool-call rendering). Without it, tool-use will silently break.
-c 16384 matches the model's max_seq_length at training time. Long-trajectory investigations regularly accumulate 9–11 K tokens of conversation history; a smaller context errors with HTTP 400 request exceeds the available context size.
Disable thinking via chat_template_kwargs: {enable_thinking: false} in your /v1/chat/completions payload. The training corpus contains no <think> blocks; serving in thinking mode is a train/serve mismatch and silently degrades quality. reasoning_effort is the equivalent lever on ollama; llama.cpp's OpenAI shim ignores it for Qwen 3.5 and only reads chat_template_kwargs.

Sample chat-completion call with a K8sGPT MCP tool:

bash

curl -sS http://127.0.0.1:8088/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
    "model": "kubelm-qwen3.5-2b",
    "temperature": 0.0,
    "max_tokens": 2048,
    "chat_template_kwargs": {"enable_thinking": false},
    "messages": [
      {"role": "system", "content": "You are an SRE investigating a Kubernetes cluster via K8sGPT MCP tools..."},
      {"role": "user",   "content": "Why is api-pod in namespace foo not ready?"}
    ],
    "tools": [{"type": "function", "function": {"name": "get-resource", "parameters": {"type": "object", "properties": {"resourceType": {"type": "string"}, "name": {"type": "string"}, "namespace": {"type": "string"}}, "required": ["resourceType", "name"]}}}],
    "tool_choice": "auto"
  }'

In production, drive this through the K8sGPT MCP server and the kubelm eval harness so the model can call real tools against a real cluster.

Intended use

Tool-use specialist for K8sGPT MCP investigations on CPU-only hardware (M-series Macs, modest Linux boxes).
Drop-in upgrade from kubelm-qwen2.5-1.5b-v1 for K8sGPT integrations that already speak the OpenAI Chat Completions API.
Local component of agentic K8s diagnosis pipelines where the destructive-action layer is handled by K8sGPT's operator + Mutation CR policy gates (i.e. the model proposes; the operator gates).

Out of scope

Snapshot diagnosis from raw cluster YAML. This model is trained on multi-step tool-use trajectories, not Q&A pairs over frozen cluster state.
Safety / refusal decisions on destructive operations. That layer is architectural in the K8sGPT ecosystem; the model is trained for reliability properties (correct tool calls, faithful grounding, appropriate termination, structured output), not behavioral refusal.
Direct kubectl usage. The tools list is K8sGPT MCP-specific; training the model on this corpus and then asking it to emit raw kubectl will cause mode confusion.
General K8s domain knowledge questions outside the K8sGPT MCP tool surface.

Training

Base model: Qwen 3.5 2B (text backbone).
Dataset: rbentaarit/kubelm-seed-v0 v0.2 corpus — 561 records across all 33 scenarios, with the corrected DEFAULT_SYSTEM_PROMPT baked in and a corrective seed for pod-insufficient-cpu-001. See the dataset card "v0.2 corpus" section for the full provenance.
Method: QLoRA, rank 32 / alpha 64, target modules q_proj k_proj v_proj o_proj gate_proj up_proj down_proj. LoRA adapter included in this repo under adapter/.
Schedule: 1 epoch, batch 8 × grad-accum 2, lr 2e-4 cosine, warmup 3%, max_seq_length 16384, seed 42. Train loss bottomed at 0.14–0.17 (no overfit; v0.2 on Qwen 2.5 1.5B bottomed at 0.024 and regressed rubric, which is why a single-epoch schedule shipped).
Hardware: 1× H100 SXM (RunPod), ~50 minutes wall, ~$3 cloud spend.
Full config: training/configs/kubelm-edge-v02-qwen35.yaml.
Train recipe: training/sft.py. Two Qwen 3.5-specific mitigations are gated on restore_base_chat_template: true (Qwen 2.5 path is byte-identical without them):
1. Restore the stock Qwen 3.5 chat template after FastLanguageModel.from_pretrained. Unsloth's loader installs a tool-schema-enumerating variant that renders unused parameters as literal None in Qwen 3.5's per-parameter template; the stock template renders only real arguments.
2. Mechanical regex-strip of <parameter=X>\nNone\n</parameter> blocks from rendered training text — Unsloth patches apply_chat_template at the method level and the patch leaks even into a freshly-loaded AutoTokenizer, so a string-level post-pass is the load-bearing mitigation.

Evaluation

Methodology and eval harness: github.com/rbentaarit/kubelm/eval. Each scenario boots a fresh kind cluster, seeds the failure mode, brings up a real K8sGPT MCP server against it, then runs the model through the trajectory loop and grades the result. Mocked MCP servers are not used at any stage.

Full bench summary (rows for all four columns, every scenario): eval/results/summaries/shape-d-2026-05-27.json.

Versioning

K8sGPT version pin: 0.4.32. Tool surface and MCP error shapes change between K8sGPT releases; quality numbers above are not guaranteed against other versions.
MCP protocol version: 2025-03-26.

Known issues

ollama 0.23.1 cannot load this GGUF. The qwen3next loader rejects it with "layer 24 missing attn_qkv/attn_gate projections". The GGUF is valid (it loads cleanly under llama.cpp's llama-cli and serves reliably under llama-server); use llama-server until ollama's Qwen 3.5 loader stabilizes.
CPU latency on weak hardware. Per-turn latency on M1 Max with Metal offload is ~1.5–2 s; on a 2-core / 2 GB edge box without hardware acceleration, expect single-digit seconds per turn. For the lowest per-step latency and smallest footprint, see the ultra-edge kubelm-qwen3.5-0.8b-v1.
No native tool-call format other than OpenAI Chat Completions. Anthropic-style tool-use, Cohere-style, and custom XML formats are not trained. Use a translation layer.

License

Apache 2.0. The base model is Qwen 3.5 2B (Apache 2.0). The training corpus is CC BY 4.0.

Citation

@misc{kubelm_qwen35_2b_v1,
  title  = {kubelm-qwen3.5-2b-v1},
  author = {Ramzi Ben Taarit and contributors},
  year   = {2026},
  url    = {https://huggingface.co/rbentaarit/kubelm-qwen3.5-2b-v1},
  note   = {QLoRA on Qwen3.5-2B; trained against K8sGPT v0.4.32 MCP trajectories}
}

Source code

All training, evaluation, and dataset-construction code: github.com/rbentaarit/kubelm.