PRA Runtime Bundle for Qwen/Qwen2.5-Coder-1.5B-Instruct · HF / bfloat16

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: Qwen/Qwen2.5-Coder-1.5B-Instruct
Immutable revision: 2e1fd397ee46e1388853d2af2c993145b0f1098a
Architecture: Qwen2ForCausalLM
Parameters: 1.5B
Tokenizer revision: 2e1fd397ee46e1388853d2af2c993145b0f1098a
Serving precision: BF16 / PyTorch-bfloat16
Post-training: code and instruction tuning

Recommended configuration

Engine: hf
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.

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
BF16	PyTorch-bfloat16	BF16	NEEDS_RUN	NO_QUALIFIED_ADAPTER	hf	Selected Context	BALANCED	CONTROLLED	NOT_MEASURED

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.

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
hf	Native Memory	QUALITY	CALIBRATION_PENDING	Native Memory: CALIBRATION_PENDING	Native Memory + Bundle: CALIBRATION_PENDING	CALIBRATION_PENDING
hf	Selected Context	BALANCED	NEEDS_RUN	Selected Context: NEEDS_RUN	Selected Context + Bundle: NOT_APPLICABLE	NEEDS_RUN
hf	Native Memory	ECONOMY	CALIBRATION_PENDING	Native Memory: CALIBRATION_PENDING	Native Memory + Bundle: CALIBRATION_PENDING	CALIBRATION_PENDING
hf	Native Memory	QASPER-LEARNED	NEEDS_RUN	Native Memory: NEEDS_RUN	Native Memory + Bundle: NEEDS_RUN	NEEDS_RUN

Canonical staged evidence

A complete staged cohort is not packaged for this exact identity.

Condition	Evidence status
No PRA	`NEEDS_RUN`
Selected Context	`NEEDS_RUN`
Selected Context + Bundle	`NEEDS_RUN`

Existing selector-frozen Selected Context versus Native Memory measurements remain reported below as transport evidence; they are not silently relabeled as adaptor evidence.

Installation

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

Quickstart

pra inspect Qwen/Qwen2.5-Coder-1.5B-Instruct -e hf -a EInnovator/pra-qwen2-5-coder-1-5b-instruct
pra evaluate Qwen/Qwen2.5-Coder-1.5B-Instruct -e hf -D qasper -a EInnovator/pra-qwen2-5-coder-1-5b-instruct
pra recommend .pra/runs/latest
pra serve Qwen/Qwen2.5-Coder-1.5B-Instruct -e hf -a EInnovator/pra-qwen2-5-coder-1-5b-instruct -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
hf	validated	AVAILABLE	NOT_APPLICABLE	Selected Context with BALANCED

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%	0.173	16	CONTROLLED
qasper	qasper-learned	R@20%	0.3938	16	CONTROLLED
hotpotqa	balanced	R@20%	0.3982	16	CONTROLLED
hotpotqa	qasper-learned	R@20%	0.2153	16	CONTROLLED
combined	balanced	R@20%	0.2856	32	CONTROLLED
combined	qasper-learned	R@20%	0.3045	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 Qwen/Qwen2.5-Coder-1.5B-Instruct -e hf -a EInnovator/pra-qwen2-5-coder-1-5b-instruct -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.
The held-out routing diagnostic contains 16 examples per dataset and supports controlled routing claims only.
Native consumer-layer profiles and end-task generation remain uncalibrated for this exact identity.
The qualification identity is the exact bfloat16 HF model and revision; it does not transfer automatically to another checkpoint, engine, or quantization.
Routing evidence compares a frozen generic router with a small learned router; it does not establish end-task generation quality.
This qualification uses natural-document routing; code retrieval and coding-agent task success remain separately unmeasured.
Base-model and dataset licenses apply separately to the router artifact.