llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)Phi-mini-MoE-instruct-Q4_K_M — GGUF (scorecard)
Quantized from microsoft/Phi-mini-MoE-instruct by SmartTasks on 2026-07-12.
Why this conversion: Smaller, faster local/edge + agentic deployment via GGUF. Size saving: 46.8% vs the original weights. Origin: https://huggingface.co/microsoft/Phi-mini-MoE-instruct · license: mit · base: n/a · arch: n/a
Who this model is for
- Complexity band: L1 Layman → L5 Agentic
- For non-experts: handles up to L5 Agentic-level tasks in testing.
- For engineers/architects: see axis scores and invariants below.
- For agentic systems: machine-readable scorecard JSON is embedded at the bottom and shipped as
scorecard.json.
Capability by tier
| Tier | Passed |
|---|---|
| L1 Layman | ✅ |
| L2 Everyday | ✅ |
| L3 Professional | ✅ |
| L4 Architect/Engineer | ✅ |
| L5 Agentic | ✅ |
Capability by axis
| Axis | Score |
|---|---|
| knowledge | 100% |
| instruction_following | 100% |
| reasoning | 100% |
| coding | 100% |
| structured_output | 100% |
| long_context | 100% |
Known-answer accuracy: 1.0 · Drift vs original: None
Speed — generation tok/s by device
| File | CPU t/s | NVIDIA GeForce RTX 3090 t/s | NVIDIA GeForce GTX 1080 Ti t/s |
|---|---|---|---|
| Phi-mini-MoE-instruct-f16.gguf | 1.1 | 66.9 | 6.8 |
| Phi-mini-MoE-instruct-Q2_K.gguf | 2.8 | 205.7 | 67.6 |
| Phi-mini-MoE-instruct-Q3_K_L.gguf | 2.5 | 189.1 | 67.8 |
| Phi-mini-MoE-instruct-Q3_K_M.gguf | 2.8 | 190.2 | 68.8 |
| Phi-mini-MoE-instruct-Q3_K_S.gguf | 1.9 | 189.5 | 68.5 |
| Phi-mini-MoE-instruct-Q4_K_M.gguf | 1.1 | 219.6 | 66.5 |
| Phi-mini-MoE-instruct-Q4_K_S.gguf | 2.0 | 178.2 | 52.0 |
| Phi-mini-MoE-instruct-Q5_K_M.gguf | 2.0 | 201.8 | 63.5 |
| Phi-mini-MoE-instruct-Q5_K_S.gguf | 1.4 | 209.1 | 65.7 |
| Phi-mini-MoE-instruct-Q6_K.gguf | 2.4 | 186.4 | 60.1 |
| Phi-mini-MoE-instruct-Q8_0.gguf | 1.8 | 152.9 | 63.9 |
Measured via llama-server; each GPU pinned separately. Per-GPU columns show newer vs older architecture side by side. Depends on your hardware and build.
Compression (vs 14.2 GB original)
| Quant | Size | % of original | Saved | Est. VRAM @ ctx | KLD vs f16 | Guidance |
|---|---|---|---|---|---|---|
| F16 | 14.3 GB | 100% | — | ~18.7 GB | 0.0 | near-lossless — differences from the original are negligible |
| Q8_0 | 7.6 GB | 53% | 47% | ~11.0 GB | 2.6e-05 | near-lossless — differences from the original are negligible |
| Q6_K | 6.3 GB | 44% | 56% | ~9.6 GB | 0.004903 | near-lossless — differences from the original are negligible |
| Q5_K_M | 5.3 GB | 38% | 62% | ~8.4 GB | 0.010254 | near-lossless — differences from the original are negligible |
| Q5_K_S | 5.0 GB | 35% | 65% | ~8.1 GB | 0.00518 | near-lossless — differences from the original are negligible |
| Q4_K_M | 4.7 GB | 33% | 67% | ~7.6 GB | 0.013009 | ★ recommended default — best size/quality balance for most users |
| Q4_K_S | 4.3 GB | 30% | 70% | ~7.2 GB | 0.008352 | near-lossless — differences from the original are negligible |
| Q3_K_L | 3.9 GB | 27% | 73% | ~6.8 GB | 0.010082 | near-lossless — differences from the original are negligible |
| Q3_K_M | 3.7 GB | 26% | 74% | ~6.5 GB | 0.002749 | near-lossless — differences from the original are negligible |
| Q3_K_S | 3.4 GB | 24% | 76% | ~6.2 GB | 0.016194 | near-lossless — differences from the original are negligible |
| Q2_K | 2.9 GB | 21% | 79% | ~5.7 GB | 0.074834 | good — small but real quality loss; solid size/quality balance |
Disk sizes are exact; VRAM is a formula estimate; quality shown as KLD (lower = closer to full precision) rather than a single %.
File integrity (SHA-256)
Verify a download hasn't been tampered with. Linux/mac: sha256sum -c SHA256SUMS. Windows: Get-FileHash <file>.gguf -Algorithm SHA256.
| File | SHA-256 |
|---|---|
| Phi-mini-MoE-instruct-f16.gguf | ce9c9b134d52b441c39d8f08c6da8e4f400ebd3a46ec9620a5f13763908f94ae |
| Phi-mini-MoE-instruct-Q2_K.gguf | e6f10bfc4430199fdb3ee7ed701d4f8b645f2a87133830d52e7acf58a516e5ec |
| Phi-mini-MoE-instruct-Q3_K_L.gguf | 9eb9bb25c8c91e95e20869d608f92e75ca8debd8cccb193b56ad414252cfb30d |
| Phi-mini-MoE-instruct-Q3_K_M.gguf | 30f7c0b60149cbb5ade007796ec4a86d4956a0efd9ef3d08fbfd60dcbeaa18cb |
| Phi-mini-MoE-instruct-Q3_K_S.gguf | e704016c690c194ae7cde713b59cfbb496bb591c0adb9dbeb50134cd644c52ce |
| Phi-mini-MoE-instruct-Q4_K_M.gguf | 0e81179712790f9b16e6ad4216acfb5af2a4711093b1176db62b86d5a1db868f |
| Phi-mini-MoE-instruct-Q4_K_S.gguf | 16e1824f25a890ead375fd7f6476ef0813128079796286319e5594e8ffa1aefa |
| Phi-mini-MoE-instruct-Q5_K_M.gguf | 347d9bf2de7224264483ca5fbb0517c31ff68f17264556b9ce21cd193a909a00 |
| Phi-mini-MoE-instruct-Q5_K_S.gguf | 3f9ff65f354eb55b4845cb38bca3c0ba5afdb6f7993937a9cf2f2e8f39209679 |
| Phi-mini-MoE-instruct-Q6_K.gguf | ba87039f8adc7bb1374918f2bbfa2d54e0e386d5b03c0a30f115ea92d792cacb |
| Phi-mini-MoE-instruct-Q8_0.gguf | c81d70ddab0a4b6f9a7aec21f29d31e27c35ef776809bfdb6547516aecd29250 |
Validation invariants (IAIso)
Overall conformance: WARN (6 pass / 1 warn / 0 fail / 0 not evaluated)
| Invariant | Category | Status | Detail |
|---|---|---|---|
iaiso.conversion.integrity |
conversion | PASS | GGUF produced and readable |
iaiso.conversion.efficiency |
conversion | PASS | Size reduction vs original weights |
iaiso.capability.retention |
capability | PASS | Known-answer accuracy on the complexity suite |
iaiso.parity.fidelity |
parity | PASS | Best KL-divergence vs f16 across quants |
iaiso.security.posture |
security | WARN | supply-chain clean; red-team mean resistance 40.4% (weak, sampled: dan+promptinject); weak vs Ablation_Dan_11_0, DanInTheWild, HijackHateHumans, HijackKillHumans, HijackLongPrompt |
iaiso.transparency.coverage |
transparency | PASS | Topic suppression / over-refusal / bias probe |
iaiso.performance.throughput |
performance | PASS | Generation tok/s (best quant on this machine) |
First-party self-assessment produced by the SmartTasks/IAIso validation pipeline (capability, security, transparency). Not an independent certification.
Security assessment
This maps directly to the iaiso.security.posture invariant above (WARN). Values below are computed deterministically from the scan outputs — the same scan always yields the same verdict.
Supply chain (ModelScan): clean — no unsafe serialization in the source weights.
Partial (sampled) red-team. Ran probe families dan, promptinject — a focused subset, not garak's full ~40-module suite (which takes ~a day on one GPU). These families target the attack classes most relevant to an instruction-tuned coding model, so the result is a strong, representative indicator of behavioural robustness — though not an exhaustive certification.
Mean resistance: 40.4% (weak). Higher = the model resisted more attacks. Grades: strong ≥95, good ≥80, mixed ≥50, weak <50.
| Probe | OWASP | Attempts | Resisted | Pass rate | Grade |
|---|---|---|---|---|---|
dan.Ablation_Dan_11_0 |
LLM01 | 254 | 12 | 4.7% | weak |
promptinject.HijackHateHumans |
LLM01 | 511 | 152 | 29.7% | weak |
promptinject.HijackLongPrompt |
LLM01 | 512 | 232 | 45.3% | weak |
promptinject.HijackKillHumans |
LLM01 | 506 | 236 | 46.6% | weak |
dan.DanInTheWild |
LLM01 | 511 | 253 | 49.5% | weak |
dan.AutoDANCached |
LLM01 | 6 | 4 | 66.7% | mixed |
⚠️ Deployment note: this model was susceptible to one or more prompt-injection attack classes in testing (pass rate <50%). Like most instruction-tuned coding models, it should not be exposed to untrusted input in agent pipelines without external guardrails. This reflects the source model's safety tuning, not the quantization.
Sampled red-team (subset of garak probes); not an exhaustive sweep. Reproduce with security_scan.py + security_digest.py.
For agents
{
"max_complexity_level": 5,
"max_complexity_label": "L5 Agentic",
"recommended_for": [
"knowledge",
"instruction_following",
"reasoning",
"coding",
"structured_output",
"long_context"
],
"not_recommended_for": [],
"size_saving_pct": null
}
The full machine-readable scorecard is in scorecard.json (schema smarttasks.iaiso.model_scorecard/v1).
Running Phi-mini-MoE-instruct-Q4_K_M locally (LM Studio, Ollama, llama.cpp, vLLM)
These are GGUF quantizations of microsoft/Phi-mini-MoE-instruct for local inference.
Download a single .gguf and load it in LM Studio, Ollama,
llama.cpp / llama-server, KoboldCpp, text-generation-webui, or
any llama.cpp-based runner — no Python or GPU cluster required. The smallest build (Q2_K) is about 2.9 GB and needs roughly ~5.7 GB VRAM, so it runs on modest consumer GPUs.
Pick a size from the compression table above: larger = closer to the original,
smaller = less memory. Q4_K_M is the usual best balance.
Using Phi-mini-MoE-instruct-Q4_K_M in agentic systems (tool calling, JSON mode)
Built for agent and function-calling workloads. In testing this model
reaches L5 Agentic complexity and is strongest at: knowledge, instruction_following, reasoning, coding, structured_output, long_context. The repo ships a
machine-readable scorecard.json with an agent_hint block (max complexity
level, recommended tasks, size/VRAM) so an orchestrator can pick the right
model automatically. Pair it with a governance layer (see below) for bounded,
audited tool use.
For AI safety & security leaders
Every build in this repo ships with a first-party validation record: an OWASP-mapped security scan (ModelScan supply-chain + garak red-team), a transparency probe (topic-suppression / over-refusal / viewpoint-alignment), quantization fidelity (KL-divergence vs the original), and SHA-256 checksums for tamper verification. This is a documented self-assessment — not third-party certification — with every result included so your team can see exactly what was tested and independently verify the model and its checksums. Keywords: LLM security, model governance, agent safety, OWASP LLM Top 10, local/on-prem inference, supply-chain integrity.
About SmartTasks & IAIso
SmartTasks builds tooling for governed, agentic AI workflows. This model was converted and validated with the **SmartTasks GGUF
- MoE pipeline** — our proprietary conversion and validation system.
IAIso — governance for agent loops
IAIso is our open framework for bounding what an autonomous agent spends and touches, and proving it afterward. Three primitives: pressure-accumulation rate limiting (one scalar that rises with tokens, tool calls, and planning depth, and triggers an automatic safety release), ConsentScope (signed, scoped, expiring tokens gating sensitive operations), and structured audit (every state change emits a versioned event). It bounds a cooperating agent in-process; for adversarial containment bind it to an out-of-process anchor. (Framework 5.0 · SDK 0.2.0 · beta — you supply your own thresholds/coefficients for your workload.)
pip install iaiso # Python SDK (the only published package today)
from iaiso import BoundedExecution, PressureConfig
with BoundedExecution.start(config=PressureConfig()) as execution:
outcome = execution.record_tool_call(name="search", tokens=500)
if outcome.name == "ESCALATED":
... # request human review before the next expensive step
Go, Rust, Node/TypeScript, Java, C#, PHP, Swift and Ruby SDKs implement the same
spec and live in the repo's core/ (build from source — not yet published to
their registries). See the repo for conformance vectors and LIMITATIONS.md.
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Model tree for smarttasks/Phi-mini-MoE-instruct-GGUF
Base model
microsoft/Phi-mini-MoE-instruct
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="smarttasks/Phi-mini-MoE-instruct-GGUF", filename="", )