- PRA Runtime Bundle for mlx-community/Qwen3-8B-4bit · MLX / 4bit
- What this PRA Runtime Bundle is
- Recommended configuration
- Precision qualification
- Headline results
- Evidence by engine, mode, and profile
- Canonical staged evidence
- Installation
- Quickstart
- Profiles
- Engine compatibility
- End-to-end qualification
- Native Memory qualification
- Research diagnostics
- How to evaluate locally
- Known limitations
- Training/creation
- Reproducibility
- Community/support
- What this PRA Runtime Bundle is
PRA Runtime Bundle for mlx-community/Qwen3-8B-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/Qwen3-8B-4bit - Immutable revision:
545dc4251c05440727734bcd94334791f6ab0192 - Architecture:
Qwen3ForCausalLM - Parameters:
8B - Tokenizer revision:
545dc4251c05440727734bcd94334791f6ab0192 - Serving precision:
INT4/MLX-4bit - 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 |
|---|---|---|---|---|---|---|---|---|---|
| INT4 | MLX-4bit | INT4 | 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=15) | token_f1=0.2365 | token_f1=0.2365 | +0.0000 | -89.1% | -0.6% | +3.0% | 15/15 | 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 (n=15); 2026-09-01; PRA commit 4b4486a66c80d09aa7982be29812d4027c57a4e3; artifact qualification/qwen3_8b_mlx_profiles.json; SHA-256 43a316c4ecca24420d5d5bc839d2b7a94d9a1074cbe5449092f23a6ba0acf47f.
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, 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-4bit at 545dc4251c05440727734bcd94334791f6ab0192 on Apple M4 Pro (Mac16,7), 48 GB; precision INT4 / MLX-4bit.
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 | 0.236455 | 0.236455 | 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 | -12.2676 | -12.2676 | 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 | 315.533 | 34.2667 | NO_QUALIFIED_ADAPTER | -281.267 (-89.14%) | NO_QUALIFIED_ADAPTER |
| Selected Native K/V Tokens | token | neutral | 0 | 10125.6 | NO_QUALIFIED_ADAPTER | +10125.6 | 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 | 115.775 | 115.061 | NO_QUALIFIED_ADAPTER | -0.713958 (-0.62%) | NO_QUALIFIED_ADAPTER |
| TTFT p95 (ms) | ms | lower_is_better | 183.02 | 231.031 | NO_QUALIFIED_ADAPTER | +48.0105 (+26.23%) | NO_QUALIFIED_ADAPTER |
| TTFT p99 (ms) | ms | lower_is_better | 183.02 | 231.031 | NO_QUALIFIED_ADAPTER | +48.0105 (+26.23%) | NO_QUALIFIED_ADAPTER |
| ITL p50 (ms) | ms | lower_is_better | 18.787 | 19.4877 | NO_QUALIFIED_ADAPTER | +0.700714 (+3.73%) | NO_QUALIFIED_ADAPTER |
| ITL p95 (ms) | ms | lower_is_better | 18.9825 | 20.5185 | NO_QUALIFIED_ADAPTER | +1.53595 (+8.09%) | NO_QUALIFIED_ADAPTER |
| ITL p99 (ms) | ms | lower_is_better | 18.9825 | 20.5185 | NO_QUALIFIED_ADAPTER | +1.53595 (+8.09%) | NO_QUALIFIED_ADAPTER |
| Output Tokens Per Second | output_token/s | higher_is_better | 53.3045 | 51.1062 | NO_QUALIFIED_ADAPTER | -2.19834 (-4.12%) | NO_QUALIFIED_ADAPTER |
| Completion Latency Mean (ms) | ms | lower_is_better | 275.946 | 284.088 | NO_QUALIFIED_ADAPTER | +8.14217 (+2.95%) | 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 | 4.14745e+07 | NO_QUALIFIED_ADAPTER | +4.14745e+07 | NO_QUALIFIED_ADAPTER |
| Retained Detail Bytes | byte | lower_is_better | 0 | 4.14745e+07 | NO_QUALIFIED_ADAPTER | +4.14745e+07 | NO_QUALIFIED_ADAPTER |
| Peak Memory Bytes | byte | lower_is_better | 5.18009e+09 | 5.13407e+09 | NO_QUALIFIED_ADAPTER | -4.60226e+07 (-0.89%) | NO_QUALIFIED_ADAPTER |
Installation
pip install 'pra-hf[hf-hub,hf-runtime]'
pra doctor
Quickstart
pra inspect mlx-community/Qwen3-8B-4bit -e mlx -a EInnovator/pra-qwen3-8b-mlx-4bit
pra evaluate mlx-community/Qwen3-8B-4bit -e mlx -D qasper -a EInnovator/pra-qwen3-8b-mlx-4bit
pra recommend .pra/runs/latest
pra serve mlx-community/Qwen3-8B-4bit -e mlx -a EInnovator/pra-qwen3-8b-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 |
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 |
|---|---|---|---|---|---|---|---|
| 2wikimultihopqa (n=5) | Selected Context | token_f1=0.3578 | 351.2 | 114.2 ms | 271.9 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| 2wikimultihopqa (n=5) | Native Memory | token_f1=0.3578 | 31.6 | 113.8 ms | 278.3 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| hotpotqa (n=5) | Selected Context | token_f1=0.3016 | 262 | 180.3 ms | 311.6 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| hotpotqa (n=5) | Native Memory | token_f1=0.3016 | 43.4 | 179 ms | 325 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| qasper (n=5) | Selected Context | token_f1=0.05 | 333.4 | 114.1 ms | 244.4 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| qasper (n=5) | Native Memory | token_f1=0.05 | 27.8 | 112.6 ms | 249 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| combined (n=15) | Selected Context | token_f1=0.2365 | 315.5 | 115.8 ms | 275.9 ms | Apple M4 Pro (Mac16,7), 48 GB | ENGINE_QUALIFIED |
| combined (n=15) | Native Memory | token_f1=0.2365 | 34.27 | 115.1 ms | 284.1 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 |
|---|---|---|---|---|
| 2wikimultihopqa | 1.151e+04 | 44.94 MiB | 4.77 GiB | 1.023x |
| hotpotqa | 7870 | 30.74 MiB | 4.74 GiB | 1.043x |
| qasper | 1.1e+04 | 42.98 MiB | 4.78 GiB | 1.019x |
| combined | 1.013e+04 | 39.55 MiB | 4.78 GiB | 1.03x |
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-4bit -e mlx -a EInnovator/pra-qwen3-8b-mlx-4bit -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 5 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 4bit 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.
Training/creation
The structural adapter is training-free. Learned-component training metadata is stored beside each component and summarized in bundle.yaml.
Reproducibility
- PRA commit:
81f42d69936bf50eb6fe11a0f7477b415bbf250d - Bundle build commit:
81f42d69936bf50eb6fe11a0f7477b415bbf250d - Bundle schema:
2 - PRA package:
0.2.0rc1 - Component fingerprints and file checksums are recorded in
bundle.yaml.