PRA Runtime Bundle for mlx-community/Qwen3-4B-4bit · MLX / 4bit

What this PRA Runtime Bundle is

This repository packages the model-specific Progressive Retrieval Attention (PRA) structural mapping, runtime profiles, optional learned components, compatibility metadata, and measured qualification evidence. It does not contain the base-model weights and is not an ordinary LoRA quality fine-tune.

  • Base model: mlx-community/Qwen3-4B-4bit
  • Immutable revision: 4dcb3d101c2a062e5c1d4bb173588c54ea6c4d25
  • Architecture: Qwen3ForCausalLM
  • Parameters: 4B
  • Tokenizer revision: 4dcb3d101c2a062e5c1d4bb173588c54ea6c4d25

Recommended configuration

  • Engine: mlx
  • Recommended PRA mode: Selected Context
  • Recommended profile: BALANCED
  • Bundle evidence tier: CONTROLLED
  • Native Memory status: AVAILABLE

Availability, qualification, and recommendation are separate. A mode may be implemented without being qualified or recommended for this identity.

Headline results

No paired end-task headline is available for this exact model, revision, quantization, engine, profile, and execution mode. Routing diagnostics below must not be interpreted as application quality.

Installation

pip install 'pra-hf[hf-hub,hf-runtime]'
pra doctor

Quickstart

pra inspect mlx-community/Qwen3-4B-4bit -e mlx -a EInnovator/pra-qwen3-4b-mlx-4bit
pra evaluate mlx-community/Qwen3-4B-4bit -e mlx -D qasper -a EInnovator/pra-qwen3-4b-mlx-4bit
pra recommend .pra/runs/latest
pra serve mlx-community/Qwen3-4B-4bit -e mlx -a EInnovator/pra-qwen3-4b-mlx-4bit -p balanced

Profiles

Profile Purpose Routing Consumer layers Status Recommendation
QUALITY Candidate maximum-quality profile; held-out calibration is incomplete generic cosine all eligible CALIBRATION_PENDING Not promoted
BALANCED Qualified default preserving the all-eligible consumer geometry generic cosine all eligible QUALIFIED Default
ECONOMY Reduced-consumer candidate; the held-out quality gate has not passed generic cosine CALIBRATION_PENDING CALIBRATION_PENDING Not promoted
QASPER-LEARNED Research-only learned routing profile qualified only on matched QASPER routing diagnostics combined-router-d128 all eligible RESEARCH Not promoted

Engine compatibility

Engine Selected Context Native Memory Native Serving Recommended today
mlx validated AVAILABLE NOT_MEASURED Selected Context with BALANCED
hf portable NOT_MEASURED for the full-precision HF counterpart NOT_MEASURED Selected Context; exact MLX artifact only

End-to-end qualification

What remains to be measured: paired end-task quality for this exact bundle identity.

Native Memory qualification

What remains to be measured: paired Selected Context versus Native Memory quality and serving economics.

Research diagnostics

Dataset Router/profile Metric Value Cohort Evidence
qasper balanced R@20% 0.4129 16 CONTROLLED
qasper qasper-learned R@20% 0.6399 16 CONTROLLED
hotpotqa balanced R@20% 0.3863 16 CONTROLLED
hotpotqa qasper-learned R@20% 0.3424 16 CONTROLLED
combined balanced R@20% 0.3996 32 CONTROLLED
combined qasper-learned R@20% 0.4912 32 CONTROLLED

These are qualification measurements, not guaranteed production performance. Run pra evaluate on your hardware and workload. Engine version, profile, cohort, evidence tier, date, and artifact provenance remain recorded in qualification/ and bundle.yaml.

How to evaluate locally

pra evaluate mlx-community/Qwen3-4B-4bit -e mlx -a EInnovator/pra-qwen3-4b-mlx-4bit -D qasper -o .pra/runs/qasper
pra recommend .pra/runs/qasper
pra report .pra/runs/qasper --format html

Known limitations

  • The learned router improves QASPER but is not uniformly positive on HotpotQA; it is opt-in rather than the bundle default.
  • Paired natural-QA evidence contains five examples per dataset and supports engine qualification, not production qualification.
  • Reduced consumer-layer configurations failed the held-out quality gate; BALANCED therefore retains all eligible layers.
  • The qualification identity is the exact 4-bit MLX model and revision; it does not transfer automatically to full-precision Hugging Face weights or another quantization.
  • Base-model and dataset licenses apply separately to the router artifact.

Training/creation

  • Datasets: QASPER and HotpotQA
  • Train Examples: 48
  • Validation Examples: 16
  • Held Out Test Examples: 32
  • Seeds: [11, 23, 37, 53, 71]
  • Selection: maximum combined validation AUC0-30
  • Method: multi-positive softmax
  • Parameter Count: 655360
  • Base Revision: 4dcb3d101c2a062e5c1d4bb173588c54ea6c4d25

Reproducibility

  • PRA commit: 52ea386f56fc8d81346325619f3523e585abf78d
  • Bundle build commit: 52ea386f56fc8d81346325619f3523e585abf78d
  • Bundle schema: 2
  • PRA package: 0.2.0rc1
  • Component fingerprints and file checksums are recorded in bundle.yaml.

Community/support

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