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pra-qwen2-5-coder-1-5b-instruct
EInnovator/pra-qwen2-5-coder-1-5b-instruct
pra-qwen2-5-coder-1-5b-instruct is a machine learning model from EInnovator. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for pra. The card lists the license as apache-2.0.
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…
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Updated Sep 3, 2026
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At a glance
- Library
- pra
- License
- apache-2.0
- Access
- Public
- Created
- Sep 3, 2026
- Updated
- Sep 3, 2026
- SHA
- df1a2379
Base models
Model card
From the Hugging Face model README
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.
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:
393216 - Base Revision:
2e1fd397ee46e1388853d2af2c993145b0f1098a
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.
Community/support
- Library
- pra
- License
- apache-2.0
- Created
- Sep 3, 2026
- Updated
- Sep 3, 2026