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Publish Jev-Bonsai-Compass runtime, adapter, card and evidence

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  1. .gitattributes +21 -0
  2. LICENSE +202 -0
  3. MANIFEST.json +239 -0
  4. NOTICE +30 -0
  5. README.md +62 -0
  6. adapters/broad-lr0.0001.adapter +3 -0
  7. benchmark-manifest.json +4241 -0
  8. benchmarks/LICENSE +202 -0
  9. benchmarks/NOTICE +3 -0
  10. benchmarks/numeric-regression.jsonl +0 -0
  11. bonsai-decision-runtime-0.1.0-source.tar.gz +3 -0
  12. bonsai_decision_runtime-0.1.0-py3-none-any.whl +0 -0
  13. evaluate.py +20 -0
  14. evidence/agent-final.json +948 -0
  15. evidence/agent-recovery-summary.json +92 -0
  16. evidence/core-base.json +26 -0
  17. evidence/core-current.json +26 -0
  18. evidence/core-preservation-audit.json +168 -0
  19. evidence/core-public-domains.json +150 -0
  20. evidence/core-resource-summary.json +34 -0
  21. evidence/core-selection.json +31 -0
  22. evidence/public-sources.json +106 -0
  23. examples/evaluation.jsonl +3 -0
  24. examples/requests.jsonl +3 -0
  25. licenses/base-weights-APACHE-2.0 +177 -0
  26. licenses/native-MIT +21 -0
  27. licenses/openjev-upstream-NOTICE +7 -0
  28. licenses/openjev-weight-terms +408 -0
  29. licenses/prism-bonsai-NOTICE +4 -0
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  31. licenses/runtime-APACHE-2.0 +202 -0
  32. licenses/runtime-NOTICE +20 -0
  33. native-runtime.json +128 -0
  34. native/LICENSE +21 -0
  35. native/bin/libggml-base.so +3 -0
  36. native/bin/libggml-base.so.0 +3 -0
  37. native/bin/libggml-base.so.0.21.0 +3 -0
  38. native/bin/libggml-cpu.so +3 -0
  39. native/bin/libggml-cpu.so.0 +3 -0
  40. native/bin/libggml-cpu.so.0.21.0 +3 -0
  41. native/bin/libggml-cuda.so +3 -0
  42. native/bin/libggml-cuda.so.0 +3 -0
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  45. native/bin/libggml.so.0 +0 -0
  46. native/bin/libggml.so.0.21.0 +0 -0
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  48. native/bin/libllama-common.so +3 -0
  49. native/bin/libllama-common.so.0 +3 -0
  50. native/bin/libllama-common.so.0.2.0 +3 -0
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LICENSE ADDED
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+ "sha256": "c8f2a950c5e8dee0aff4e52da936b2e145ee800c6066d8edd526a427ac4163a4"
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+ },
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+ "sha256": "3907dc1658db1f78a9826bf8d5bcb8dc65db0d466388937af57f2294fae62ec1"
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+ "bytes": 31,
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+ },
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+ "source/bonsai_runtime/service.py": {
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+ "bytes": 6293,
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+ "sha256": "a73a0f6f1b2f3f71a490f7702308c7e110260df26ffd132bd6625aabcb9f7a8a"
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+ },
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+ "source/pyproject.toml": {
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+ "bytes": 588,
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+ "sha256": "4d6ad17a3bb514d8241b21611c653d3cd1d875434163822d19624244ee564c75"
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+ },
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+ "validate_release.py": {
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+ "bytes": 934,
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+ "sha256": "b929980596ebda9f62e71183e6ca2b531d862381eb33be46fafc1c1001679caa"
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+ }
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+ },
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+ "format_version": 1,
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+ "version": "0.1.0"
239
+ }
NOTICE ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ Bonsai decision runtime
2
+
3
+ The newly packaged bonsai_runtime software is distributed under Apache-2.0.
4
+ This statement applies to the runtime package, not all files in the research
5
+ workspace, and does not relicense the bundled model or adapter weights.
6
+
7
+ Letter-option prompting/readout follows OpenJev's Apache-2.0 helper interface.
8
+ The local implementation is modified for the Prism native server, explicit
9
+ adapter profiles, compact numerical tools, and bounded local API entry points.
10
+ OpenJev project: https://huggingface.co/openjev/openjev
11
+ Its helper/ and serve/ code are Apache-2.0; its weights are CC-BY-NC-4.0.
12
+ This bundle includes a locally modified adapter descended from OpenJev weight
13
+ deltas. Its terms retain OpenJev's CC-BY-NC-4.0 noncommercial lineage. The
14
+ adapter is not licensed under the software's Apache-2.0 terms.
15
+ Changes from OpenJev: rank-32 factors were extracted from OpenJev/Qwen3.8-27B
16
+ weight differences, adapted for the unmodified Bonsai PQ2_0 base, and further
17
+ trained in local decision-preservation and fast-route experiments. The selected
18
+ artifact is `adapters/broad-lr0.0001.adapter` (GGUF-formatted bytes). OpenJev's original weights, license
19
+ and notice are at https://huggingface.co/openjev/openjev; the CC terms are at
20
+ https://creativecommons.org/licenses/by-nc/4.0/.
21
+
22
+ The bundled native llama.cpp runtime is MIT-licensed by the ggml authors. It
23
+ was built from PrismML-Eng/llama.cpp commit f0a2b5dc9ea066780b9b410d2c0f95d675859bf7
24
+ with the local qwen35 source change in native/source/local-qwen35.patch.
25
+ The bundled, byte-identical Prism Ternary-Bonsai-2-27B weights are Apache-2.0;
26
+ their ancestor Qwen3.8-27B is Apache-2.0. Preserve their licenses and notices.
27
+
28
+ The Python decision core has no tzdata dependency. The experimental agent
29
+ controller is excluded from this bundle. See MANIFEST.json for exact
30
+ artifacts and native-runtime.json for the tested build and its system dependencies.
README.md ADDED
@@ -0,0 +1,62 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ license: cc-by-nc-4.0
3
+ language:
4
+ - en
5
+ library_name: gguf
6
+ pipeline_tag: text-classification
7
+ base_model: prism-ml/Ternary-Bonsai-2-27B-gguf
8
+ tags:
9
+ - jev
10
+ - jev-style
11
+ - openjev
12
+ - decision-model
13
+ - typed-decisions
14
+ - gguf
15
+ - bonsai
16
+ - ternary
17
+ - local-inference
18
+ - blackwell
19
+ ---
20
+
21
+ # Jev-Bonsai-Compass
22
+
23
+ A complete, local **Jev-style text decision** download: [Prism's Ternary Bonsai 2 27B](https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf), a decision adapter derived from [OpenJev](https://huggingface.co/openjev/openjev), the tested native server, and a small Python decision API. This is an independent project, not an official Jev or OpenJev release. Fast choices use one short option-scoring pass without reasoning; numerical questions can use a checked CPU calculation.
24
+
25
+ ## What was measured
26
+
27
+ | Local configuration | Correct on the same 720 text questions | Sampled GPU memory | Mean text-decision latency |
28
+ |---|---:|---:|---:|
29
+ | **Jev-Bonsai-Compass**, adapter + compact runtime | **611/720 (84.9%)** | **7,844 MiB**, one RTX 5080 | **177 ms** |
30
+ | Full OpenJev FP8 reference | 615/720 (85.4%) | 30,974 MiB total, two GPUs | 118 ms |
31
+ | Untuned Bonsai + the same compact runtime | 584/720 (81.1%) | 7,510 MiB, one RTX 5080 | 145 ms |
32
+
33
+ The 720 questions comprise 120 each from AG News, Emotion, BoolQ, SNLI, ARC-Challenge and a **20-intent subset** of Banking77. They used identical prompts and option order across the three local configurations. Current versus full OpenJev differs by four answers; a paired bootstrap interval for the accuracy difference is −2.50 to +1.25 percentage points. This does **not** establish formal parity, and it is **not** OpenJev's published 10,000-question or hosted Jev evaluation. The full reference used a different two-GPU serving stack, so its latency is not a same-hardware speed comparison. See [domain and method evidence](evidence/core-public-domains.json) and [benchmark metadata](benchmark-manifest.json).
34
+
35
+ Accuracy varies by task: this adapter scored 117/120 on science, 113/120 on the selected banking intents, **102/120 on SNLI**, and **65/120 on Emotion**. It scored 327/328 on reused, authored numerical regressions with compact calculator assistance; that is a system result, not raw-model arithmetic accuracy. [Numeric cases](benchmarks/numeric-regression.jsonl) and [summary evidence](evidence/core-current.json) are included. All timing and memory figures are serial local measurements with context 4,096 and one active request.
36
+
37
+ ## Download and run
38
+
39
+ The complete repository includes one canonical Bonsai GGUF, the adapter, Python source and wheel, a Linux x86-64 native server and shared libraries, examples, licenses, and a [SHA-256 manifest](MANIFEST.json). The bundled native build targets NVIDIA Blackwell `sm_120a` and needs a driver plus CUDA 13, cuBLAS 13 and NCCL 2 system libraries. Python 3.10+ is required; the decision core itself has no third-party Python dependencies.
40
+
41
+ ```bash
42
+ hf download ajh-code/Jev-Bonsai-Compass --local-dir Jev-Bonsai-Compass
43
+ cd Jev-Bonsai-Compass
44
+ python3 validate_release.py
45
+ bash run.sh serve --gpu 0 --host 127.0.0.1 --port 9304
46
+ ```
47
+
48
+ In another terminal:
49
+
50
+ ```bash
51
+ curl -s http://127.0.0.1:9304/v1/decide \
52
+ -H 'Content-Type: application/json' \
53
+ -d '{"state":"There are 9 boxes with 12 pens in each.","question":"How many pens are there?","options":[["a","108"],["b","96"],["c","21"]]}'
54
+ ```
55
+
56
+ The result contains the selected option ID, method, timing and trace. `bash run.sh decide --input examples/requests.jsonl --gpu 0` processes JSONL without exposing HTTP. `bash run.sh serve --profile base --gpu 0` selects the untuned, Apache-licensed Bonsai path. The server has no authentication; use `--host 127.0.0.1` or a trusted network. For other GPU architectures, build a compatible [Prism llama.cpp fork](https://github.com/PrismML-Eng/llama.cpp) and pass `--binary /path/to/llama-server`.
57
+
58
+ ## Scope and terms
59
+
60
+ The API accepts text `state`, `question`, and 2–26 `[id, description]` options at `POST /v1/decide`; it also provides `/health` and `/v1/capabilities`. It is **not** a drop-in `/v1/systemone` implementation, vision model, general chat model, or autonomous agent service. Option-relative scores are not guarantees of correctness. The separate experimental agent evidence is included for transparency but its controller is not part of this decision API.
61
+
62
+ The bundled adapter contains locally modified OpenJev-derived weight factors and retains **CC BY-NC 4.0** noncommercial terms. The unmodified Bonsai base weights are Apache 2.0, this Python software is Apache 2.0, and the native runtime is MIT. The [NOTICE](NOTICE), [licenses](licenses/) and [profile identities](profiles.json) distinguish the components. The adapter is GGUF-formatted data stored as `adapters/broad-lr0.0001.adapter`; `run.sh` loads it directly. The single `.gguf` file is the canonical base model used by both profiles.
adapters/broad-lr0.0001.adapter ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:4903a7c9fd91c9a89ad504716d8fb497a2fbe94a6158148e02ea0ae0237ac233
3
+ size 318802208
benchmark-manifest.json ADDED
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+ },
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+ },
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+ {
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+ "domain": "numeric-challenge"
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+ {
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+ "domain": "numeric-challenge"
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+ {
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+ "id": "numeric-authored-23",
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+ "domain": "numeric-challenge"
4233
+ }
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+ ]
4235
+ },
4236
+ "commands_in_research_workspace": [
4237
+ "python -B -m analysis.run_guarded_runtime --stage final --gpu 0",
4238
+ "python -B -m analysis.verify_runtime_core"
4239
+ ],
4240
+ "raw_public_data_redistributed": false
4241
+ }
benchmarks/LICENSE ADDED
@@ -0,0 +1,202 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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benchmarks/NOTICE ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ Authored arithmetic regression fixtures from this project, distributed under Apache-2.0.
2
+ Generated by analysis/prepare_numeric_stress.py and analysis/add_numeric_challenge.py; labels audited with exact arithmetic.
3
+ No public dataset text or model weights included. Reused evaluation cases, not independent new test evidence.
benchmarks/numeric-regression.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
bonsai-decision-runtime-0.1.0-source.tar.gz ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:270eabe54de1f26ea41c2f2298d953967b29a3eff1ad7c5bd214fedb9c95d057
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+ size 15031
bonsai_decision_runtime-0.1.0-py3-none-any.whl ADDED
Binary file (23.3 kB). View file
 
evaluate.py ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ """Evaluate supplied labeled decisions; no bundled public benchmark text."""
2
+ import argparse,json,statistics,time
3
+ from collections import defaultdict
4
+ from pathlib import Path
5
+ from bonsai_runtime.backend import Client
6
+ from bonsai_runtime.decision import DecisionEngine
7
+ p=argparse.ArgumentParser();p.add_argument('--input',required=True);p.add_argument('--output',required=True)
8
+ p.add_argument('--base',default='http://127.0.0.1:5991');p.add_argument('--profile',choices=['base','current'],default='base');p.add_argument('--adapter-id',type=int,default=0)
9
+ a=p.parse_args();engine=DecisionEngine(Client(a.base,None if a.profile=='base' else a.adapter_id));rows=[]
10
+ with Path(a.output).open('x') as out:
11
+ for line in Path(a.input).read_text().splitlines():
12
+ item=json.loads(line);request=item['request']
13
+ for order in [0,1]:
14
+ r=dict(request);options=r['options'];r['options']=options[order:]+options[:order]
15
+ result=engine.decide(r);row=dict(id=item['id'],order=order,correct=result['answer']==item['expected'],result=result)
16
+ out.write(json.dumps(row)+'\n');out.flush();rows.append(row)
17
+ groups=defaultdict(list)
18
+ for r in rows:groups[r['id']].append(r)
19
+ canonical=[r for r in rows if r['order']==0]
20
+ print(json.dumps(dict(profile=a.profile,requests=len(groups),canonical_correct=sum(r['correct'] for r in canonical),all_order_correct=sum(all(r['correct'] for r in rs) for rs in groups.values()),mean_seconds=statistics.mean(r['result']['seconds'] for r in rows)),indent=2))
evidence/agent-final.json ADDED
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+ {
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+ "id": "final-scheduling-18",
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+ "terminal": "clarify",
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+ "error": null,
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+ "id": "final-scheduling-28",
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+ "rotation": 1,
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+ "terminal": "clarify",
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+ "error": null,
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+ "questions": [
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+ "id": "final-time-09",
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+ "questions": [
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+ "Which paragraph should \"Cedar existing 16\" be moved to? Please specify the target paragraph ID and whether to place it before or after that paragraph."
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+ "id": "final-existing-25",
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+ "questions": [
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+ "Which paragraph should \"Cedar existing 26\" be moved before or after?"
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+ ]
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+ "id": "final-existing-25",
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+ "Which paragraph should \"Cedar existing 26\" be moved before or after?"
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+ The newly packaged bonsai_runtime software is distributed under Apache-2.0.
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+ This statement applies to the runtime package, not all files in the research
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+ workspace, and does not relicense model weights or optional adapters.
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+
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+ Letter-option prompting/readout follows OpenJev's Apache-2.0 helper interface.
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+ The local implementation is modified for the Prism native server, explicit
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+ adapter profiles, compact numerical tools, and bounded local API entry points.
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+ OpenJev project: https://huggingface.co/openjev/openjev
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+ Its helper/ and serve/ code are Apache-2.0; its weights are CC-BY-NC-4.0.
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+ their separately documented noncommercial lineage.
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+
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+ The tzdata dependency includes its own upstream license and IANA data notices.
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+ See the local release manifest for exact artifacts and source revisions.
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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