PRA Runtime Bundle for mlx-community/Qwen3-8B-8bit · MLX / 8bit

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/Qwen3-8B-8bit
Immutable revision: 48a0b75b1ae72503e21e1558d040bc227510ff06
Architecture: Qwen3ForCausalLM
Parameters: 8B
Tokenizer revision: 48a0b75b1ae72503e21e1558d040bc227510ff06
Serving precision: INT8 / MLX-8bit
Post-training: pretrained and post-trained

Recommended configuration

Engine: mlx
Recommended PRA mode: Native Memory
Recommended profile: BALANCED
Bundle evidence tier: ENGINE_QUALIFIED
Native Memory status: QUALIFIED

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

Precision qualification

Precision evidence is scoped to the exact model conversion, engine, mode, and profile. Qualification does not transfer automatically between BF16, INT8, INT4, or encoding-specific formats.

Family	Encoding	Serving	Feature extraction	Adaptor parameters	Engine	Mode	Profile	Evidence	Datasets
INT8	MLX-8bit	INT8	NEEDS_RUN	NO_QUALIFIED_ADAPTER	mlx	Native Memory	BALANCED	ENGINE_QUALIFIED	NOT_MEASURED

Headline results

Workload	Selected Context quality	Native Memory quality	Delta NM vs SC	Visible-context delta NM vs SC	TTFT delta NM vs SC	Completion delta NM vs SC	Paired parity	Evidence
combined (n=60)	token_f1=0.144	token_f1=0.144	+0.0000	-91.5%	-5.7%	+0.6%	60/60	ENGINE_QUALIFIED

All headline rows freeze the PRA-selected evidence. Deltas are Native Memory minus Selected Context; negative latency and visible-context deltas are reductions. These rows contain no ordinary No-PRA arm.

Evidence receipt: mlx-lm 0.31.3; Apple M4 Pro (Mac16,7), 48 GB; selector-frozen natural QA; cold direct query (n=60); 2026-09-03; PRA commit None; artifact qualification/matched_e0_e2_qasper.json, qualification/matched_e0_e2_hotpotqa.json, qualification/matched_e0_e2_2wikimultihopqa.json; SHA-256 312d6fc5e1df75d5e8c83598efa35ff960f5ccc078b7766523121cc795cc8bcf,a46a8d6b2b0951a225ff7745af8daf5b0ef11009e1b4175f419fcf50582cfb48,dd6044a32173ce41179b4b34c85883c8039bc7680138fd13bf91962e8772f06f.

Exact-identity runtime smoke

This bounded check loads the published quantized checkpoint, discovers the adapter projections, and performs one short generation. It is operational evidence, not an end-task benchmark.

Status	Host hardware	Load	Generation	Peak model/runtime memory	Scope
RUNTIME_SMOKE_VALIDATED	Model Name: MacBook Pro; Chip: Apple M4 Pro; Memory: 48 GB	279.2 s	1.33 s	8.15 GiB	exact checkpoint load, adapter projection discovery, and bounded generation

Runtime smoke does not establish end-task quality, Native Memory parity, routing quality, or serving economics. The coverage table below identifies the exact follow-up state.

Evidence by engine, mode, and profile

Each row identifies the exact runtime surface for which metrics are available. MEASURED counts scalar metrics with real observations; missing profile/mode combinations are not inferred from another row.

Engine	Mode	Profile	No PRA	Mode / no adaptor	Same mode / bundle	Measured metric groups
mlx	Native Memory	QUALITY	CALIBRATION_PENDING	Native Memory: CALIBRATION_PENDING	Native Memory + Bundle: CALIBRATION_PENDING	CALIBRATION_PENDING
mlx	Native Memory	BALANCED	NEEDS_RUN	Native Memory: MEASURED (16)	Native Memory + Bundle: NO_QUALIFIED_ADAPTER	context, quality, resources, routing, serving
mlx	Native Memory	ECONOMY	CALIBRATION_PENDING	Native Memory: CALIBRATION_PENDING	Native Memory + Bundle: CALIBRATION_PENDING	CALIBRATION_PENDING

Canonical staged evidence

Each table holds task, hardware, engine, model, precision, and profile fixed. Every delta names its source and target; bundle use is held orthogonal to execution depth.

combined / mlx-lm / balanced

Exact identity: mlx-community/Qwen3-8B-8bit at 48a0b75b1ae72503e21e1558d040bc227510ff06 on Apple M4 Pro (Mac16,7), 48 GB; precision INT8 / MLX-8bit.

Quality

Metric	Unit	Direction	Selected Context	Native Memory	Native Memory + Bundle	Delta NM vs SC	Delta Bundle vs NM
Token F1	fraction	higher_is_better	0.144011	0.144011	NO_QUALIFIED_ADAPTER	+0 (+0.00%)	NO_QUALIFIED_ADAPTER
Exact Match	fraction	higher_is_better	0	0	NO_QUALIFIED_ADAPTER	+0	NO_QUALIFIED_ADAPTER
Gold Answer Log Probability	log_probability	higher_is_better	-19.1076	-19.1076	NO_QUALIFIED_ADAPTER	+0 (-0.00%)	NO_QUALIFIED_ADAPTER

Context

Metric	Unit	Direction	Selected Context	Native Memory	Native Memory + Bundle	Delta NM vs SC	Delta Bundle vs NM
Visible Tokens	token	lower_is_better	396.317	33.5167	NO_QUALIFIED_ADAPTER	-362.8 (-91.54%)	NO_QUALIFIED_ADAPTER
Selected Native K/V Tokens	token	neutral	0	362.8	NO_QUALIFIED_ADAPTER	+362.8	NO_QUALIFIED_ADAPTER

Serving

Metric	Unit	Direction	Selected Context	Native Memory	Native Memory + Bundle	Delta NM vs SC	Delta Bundle vs NM
TTFT p50 (ms)	ms	lower_is_better	186.881	176.211	NO_QUALIFIED_ADAPTER	-10.6696 (-5.71%)	NO_QUALIFIED_ADAPTER
TTFT p95 (ms)	ms	lower_is_better	232.033	208.936	NO_QUALIFIED_ADAPTER	-23.0971 (-9.95%)	NO_QUALIFIED_ADAPTER
TTFT p99 (ms)	ms	lower_is_better	249.496	212.698	NO_QUALIFIED_ADAPTER	-36.7977 (-14.75%)	NO_QUALIFIED_ADAPTER
ITL p50 (ms)	ms	lower_is_better	34.8123	35.6742	NO_QUALIFIED_ADAPTER	+0.861843 (+2.48%)	NO_QUALIFIED_ADAPTER
ITL p95 (ms)	ms	lower_is_better	36.4766	36.8092	NO_QUALIFIED_ADAPTER	+0.332536 (+0.91%)	NO_QUALIFIED_ADAPTER
ITL p99 (ms)	ms	lower_is_better	36.8347	37.1582	NO_QUALIFIED_ADAPTER	+0.323467 (+0.88%)	NO_QUALIFIED_ADAPTER
Output Tokens Per Second	output_token/s	higher_is_better	24.4728	24.3286	NO_QUALIFIED_ADAPTER	-0.144183 (-0.59%)	NO_QUALIFIED_ADAPTER
Completion Latency Mean (ms)	ms	lower_is_better	981.993	987.449	NO_QUALIFIED_ADAPTER	+5.4559 (+0.56%)	NO_QUALIFIED_ADAPTER

Resources

Metric	Unit	Direction	Selected Context	Native Memory	Native Memory + Bundle	Delta NM vs SC	Delta Bundle vs NM
Active Detail Bytes	byte	lower_is_better	0	5.3497e+07	NO_QUALIFIED_ADAPTER	+5.3497e+07	NO_QUALIFIED_ADAPTER
Retained Detail Bytes	byte	lower_is_better	0	5.3497e+07	NO_QUALIFIED_ADAPTER	+5.3497e+07	NO_QUALIFIED_ADAPTER

Routing

Metric	Unit	Direction	Selected Context	Native Memory	Native Memory + Bundle	Delta NM vs SC	Delta Bundle vs NM
Evidence Recall	fraction	higher_is_better	0.615972	0.615972	NO_QUALIFIED_ADAPTER	+0 (+0.00%)	NO_QUALIFIED_ADAPTER

Installation

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

Quickstart

pra inspect mlx-community/Qwen3-8B-8bit -e mlx -a EInnovator/pra-qwen3-8b-mlx-8bit
pra evaluate mlx-community/Qwen3-8B-8bit -e mlx -D qasper -a EInnovator/pra-qwen3-8b-mlx-8bit
pra recommend .pra/runs/latest
pra serve mlx-community/Qwen3-8B-8bit -e mlx -a EInnovator/pra-qwen3-8b-mlx-8bit -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

Engine compatibility

Engine	Selected Context	Native Memory	Native Serving	Recommended today
mlx	validated	QUALIFIED	NOT_APPLICABLE	Native Memory with BALANCED

End-to-end qualification

Workload	Mode	Quality	Visible tokens	TTFT p50	Completion mean	Hardware	Evidence
qasper (n=20)	Selected Context	token_f1=0.1681	398.6	154.3 ms	973.7 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
qasper (n=20)	Native Memory	token_f1=0.1681	28.05	144.3 ms	981.8 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
hotpotqa (n=20)	Selected Context	token_f1=0.1594	415.4	221.6 ms	1004 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
hotpotqa (n=20)	Native Memory	token_f1=0.1594	39.05	206.4 ms	1008 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
2wikimultihopqa (n=20)	Selected Context	token_f1=0.1045	374.9	209.6 ms	968.7 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
2wikimultihopqa (n=20)	Native Memory	token_f1=0.1045	33.45	202.6 ms	972.6 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
combined (n=60)	Selected Context	token_f1=0.144	396.3	186.9 ms	982 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED
combined (n=60)	Native Memory	token_f1=0.144	33.52	176.2 ms	987.4 ms	Apple M4 Pro (Mac16,7), 48 GB	ENGINE_QUALIFIED

Native Memory qualification

Native Memory uses the same selector output as Selected Context. It is recommended only where the profile and engine tables say so.

Workload	Selected native K/V tokens	Active detail	Peak memory	Completion cost vs Selected Context
qasper	370.6	52.12 MiB	NEEDS_RUN	1.008x
hotpotqa	376.3	52.92 MiB	NEEDS_RUN	1.004x
2wikimultihopqa	341.5	48.02 MiB	NEEDS_RUN	1.004x
combined	362.8	51.02 MiB	NEEDS_RUN	1.006x

Research diagnostics

No separate routing diagnostic is packaged for this bundle.

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/Qwen3-8B-8bit -e mlx -a EInnovator/pra-qwen3-8b-mlx-8bit -D qasper -o .pra/runs/qasper
pra recommend .pra/runs/qasper
pra report .pra/runs/qasper --format html

Known limitations

No learned router is bundled for this exact quantized identity; routing-adapter transfer from another quantization is intentionally disallowed.
Paired natural-QA evidence contains 20 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 8bit MLX model and revision; it does not transfer automatically to another checkpoint, engine, or quantization.
The selector-frozen natural-QA run qualifies the generic Native Memory path; an exact learned-adaptor arm still requires a separate run.
Base-model and dataset licenses apply separately to the router artifact.