Research preview: Qwen3-4B-Base + LoRA r16, decision-v4, lr 5e-5 (trial lowdrift-4b-v4/01-trial-1); dev 0.843, transfer 0.759; locked test 0.852 / 0.794 (one ungated read)
Browse files- .gitattributes +1 -0
- README.md +102 -0
- adapter_config.json +51 -0
- adapter_model.safetensors +3 -0
- added_tokens.json +28 -0
- head.pt +3 -0
- merges.txt +0 -0
- provenance.json +53 -0
- result.json +1526 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +239 -0
- train.log +275 -0
- training_config.json +37 -0
- training_metrics.json +14 -0
- vocab.json +0 -0
.gitattributes
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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| 2 |
+
language: en
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| 3 |
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license: apache-2.0
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| 4 |
+
library_name: peft
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| 5 |
+
base_model: Qwen/Qwen3-4B-Base
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| 6 |
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base_model_relation: adapter
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| 7 |
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pipeline_tag: text-classification
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| 8 |
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tags:
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| 9 |
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- decision-model
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| 10 |
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- calibration
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| 11 |
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- lora
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| 12 |
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- multiple-choice
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| 13 |
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- typesafe
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| 14 |
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- research-preview
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| 15 |
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datasets:
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| 16 |
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- legacy-datasets/banking77
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| 17 |
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- google/boolq
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| 18 |
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- fancyzhx/ag_news
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| 19 |
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- nyu-mll/multi_nli
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| 20 |
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- SetFit/sst5
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| 21 |
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- Yelp/yelp_review_full
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| 22 |
+
- CogComp/trec
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| 23 |
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- fancyzhx/dbpedia_14
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| 24 |
+
- SetFit/amazon_reviews_multi_en
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| 25 |
+
- stanfordnlp/imdb
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| 26 |
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metrics:
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| 27 |
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- accuracy
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| 28 |
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- brier_score
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| 29 |
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- expected_calibration_error
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| 30 |
+
model-index:
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| 31 |
+
- name: kev-4b (research preview)
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| 32 |
+
results:
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| 33 |
+
- task: { type: text-classification, name: typed decision (choice / noul / score) }
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| 34 |
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dataset: { type: mixed, name: "decision-v4 development (1,204 records; ten trained public sources + programmatic policy pairs)" }
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| 35 |
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metrics:
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| 36 |
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- { type: accuracy, value: 0.843 }
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| 37 |
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- { type: expected_calibration_error, value: 0.066, name: "ECE, raw probabilities" }
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| 38 |
+
- task: { type: text-classification, name: typed decision, out-of-domain }
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| 39 |
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dataset: { type: mixed, name: "transfer-v4 development (764 records; six never-trained sources + held-out policy structures)" }
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| 40 |
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metrics:
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| 41 |
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- { type: accuracy, value: 0.759 }
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| 42 |
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- { type: brier_score, value: 0.346 }
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| 43 |
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---
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| 44 |
+
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| 45 |
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# kev-4b — research preview
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| 46 |
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+
`kev-4b` is a **decision model**: one document (the *state*) and a set of typed questions in, a probability distribution per question out, in one forward pass. No text generation. It is a LoRA adapter (r=16) plus a pointer head on `Qwen/Qwen3-4B-Base`, serving TypeSafe's public `/v1/systemone` contract.
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| 48 |
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**Research preview, not a versioned release.** It is the best 4B checkpoint under a frozen, checksummed protocol after ~30 controlled 4B trials, and the first kev whose out-of-domain accuracy is within ten points of Jev on the same items. It does not pass the release screen we set in advance (held-out policy pairs: 0.62 both-correct, screen 70%).
|
| 50 |
+
|
| 51 |
+
- Hub: `jaredpalmer/kev-4b` (this repo; trial `lowdrift-4b-v4/01-trial-1`)
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| 52 |
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- Code, suites, every trial with hashes and paired bootstraps: [github.com/jaredpalmer/kev](https://github.com/jaredpalmer/kev) — `PLAN.md`, `runs/leaderboard.md`
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| 53 |
+
|
| 54 |
+
## Results (same frozen items for every row)
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| 55 |
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|
| 56 |
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| | kev-0.5b | kev-0.6b preview | **kev-4b preview** | Jev |
|
| 57 |
+
|---|---|---|---|---|
|
| 58 |
+
| in-distribution accuracy (decision-v4 dev, 1,200 q) | 0.712 | 0.805 | **0.843** | 0.845 |
|
| 59 |
+
| out-of-domain accuracy (transfer-v4 dev, 560 q) | 0.575 | 0.598 | **0.759** | 0.855 |
|
| 60 |
+
| out-of-domain Brier | 0.50 | 0.521 | **0.346** | 0.221 |
|
| 61 |
+
| confident errors out of domain (p ≥ 0.9 and wrong) | – | 5.2% | 5.5% | 5.5% |
|
| 62 |
+
| held-out policy structures, both siblings correct | – | 0.11 | 0.62 | 0.95 |
|
| 63 |
+
| option-order flip rate | 0.21 | 0.02 | 0.00 | 0.00 |
|
| 64 |
+
|
| 65 |
+
Per-source out-of-domain accuracy (kev-4b / Jev): 0.89 0.89 / 0.925, SciQ 0.99 / 0.99, TweetEval-offensive 0.71 / 0.81, 0.64 0.64 / 0.79, 0.68 0.68 / 0.90, Emotion 0.60 / 0.60, deadline (3-level date arithmetic) 0.60 / 0.95.
|
| 66 |
+
|
| 67 |
+
Seeds: the recipe was run at two seeds on this suite (transfer 0.759 / 0.758) and twice more on a superset suite (0.755 / 0.761); the spread is ~1 pp. The improvement over the default learning rate is +4.7 pp, 95% CI [+0.4, +9.6], record-clustered paired bootstrap.
|
| 68 |
+
|
| 69 |
+
**Locked test, one exploratory read** (`runs/locked/kev-4b-preview-ungated/`, labelled ungated because the checkpoint fails the held-out-pair screen): in-distribution **0.852** (Brier 0.221), out-of-domain **0.794** (Brier 0.296, confident errors 3.7%). Both above the development numbers, as for kev-0.6b, so development-set selection did not overfit. This partition will not be read again for this checkpoint.
|
| 70 |
+
|
| 71 |
+
## What we learned building it
|
| 72 |
+
|
| 73 |
+
- **Capacity dominates out of domain.** With public examples and synthetic budget held equal, 0.6B → 4B is +14–19 pp; 4B → 8B is +1–7 pp.
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| 74 |
+
- **Fine-tuning erodes base capability, and the learning rate controls it.** The 4B base, zero-shot with a letter readout, scores 0.688 on the same 0.68 items and 0.787 on 0.64; the default recipe (lr 2e-4) trained down to 0.60–0.66 / 0.56–0.71. Lowering lr to 5e-5 recovers most of it and is the single largest recipe improvement we found; fewer LoRA target modules and smaller ranks help less.
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| 75 |
+
- **More public training data raises in-distribution accuracy and lowers transfer** at 4B (10k vs 3.4k records: −3 pp). Knowledge MCQ sources (ARC, OpenBookQA, CommonsenseQA) raise in-distribution accuracy to 0.86 without moving transfer.
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| 76 |
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- Programmatic contrastive policy pairs teach the trained rule structures (both-correct 0.85–1.0) but transfer to unseen structures only partially (0.5–0.6 at 4B, 0.03–0.11 at 0.6B).
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| 77 |
+
|
| 78 |
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## Known limits
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| 79 |
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| 80 |
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- Held-out policy reasoning (unseen rule compositions, date arithmetic with grace periods) is far from Jev.
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| 81 |
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- Out-of-domain probabilities are usable but not calibrated (raw ECE 0.096); temperature fitted in-domain does not transfer.
|
| 82 |
+
- 4B fp32 needs ~16 GB; on a 32 GB Mac use `KEV_DTYPE=bf16`. Latency on an H100 is ~45 ms per packed request; on an M5 several hundred ms.
|
| 83 |
+
|
| 84 |
+
## Training
|
| 85 |
+
|
| 86 |
+
Frozen suite `evals/v4/decision-v4`: 10,000 public records (1,000 per source, ten sources) plus two programmatic policy arms of 448 records, two epochs, LoRA r=16 on attention and MLP projections, pointer head from scratch, cross-entropy on the option distribution, **lr 5e-5** (OneCycle), effective batch 8, bf16 autocast with fp32 master weights, gradient checkpointing, one H100 (~40 min). Augmentation: option permutation, none-of-the-above insertion, distractors, none minimal pairs on 25% of Choice records. No Jev outputs were used for training.
|
| 87 |
+
|
| 88 |
+
## Evaluation protocol
|
| 89 |
+
|
| 90 |
+
Development partitions select models; the locked test partition is read at most once per candidate. Every number carries suite hash, code hashes, and git commit in `result.json`. See `PLAN.md` for the corrections we made to our own earlier claims.
|
| 91 |
+
|
| 92 |
+
## Use
|
| 93 |
+
|
| 94 |
+
```bash
|
| 95 |
+
uv run --extra serve python -m kev.serve --run jaredpalmer/kev-4b --port 8008 # KEV_DTYPE=bf16 on a 32 GB Mac
|
| 96 |
+
```
|
| 97 |
+
|
| 98 |
+
Any TypeSafe-compatible client works: `TypeSafeClient(api_key="local", base_url="http://127.0.0.1:8008", model="kev-latest")`.
|
| 99 |
+
|
| 100 |
+
## License
|
| 101 |
+
|
| 102 |
+
Apache-2.0 for the adapter and head; Qwen3 base is Apache-2.0; datasets carry their own licenses.
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adapter_config.json
ADDED
|
@@ -0,0 +1,51 @@
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{
|
| 2 |
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"alora_invocation_tokens": null,
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| 3 |
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"alpha_pattern": {},
|
| 4 |
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"arrow_config": null,
|
| 5 |
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"auto_mapping": null,
|
| 6 |
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"base_model_name_or_path": "Qwen/Qwen3-4B-Base",
|
| 7 |
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"bias": "none",
|
| 8 |
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"corda_config": null,
|
| 9 |
+
"ensure_weight_tying": false,
|
| 10 |
+
"eva_config": null,
|
| 11 |
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"exclude_modules": null,
|
| 12 |
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"fan_in_fan_out": false,
|
| 13 |
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"inference_mode": true,
|
| 14 |
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"init_lora_weights": true,
|
| 15 |
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"kasa_config": null,
|
| 16 |
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"layer_replication": null,
|
| 17 |
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"layers_pattern": null,
|
| 18 |
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"layers_to_transform": null,
|
| 19 |
+
"loftq_config": {},
|
| 20 |
+
"lora_alpha": 32,
|
| 21 |
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"lora_bias": false,
|
| 22 |
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"lora_dropout": 0.05,
|
| 23 |
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"lora_ga_config": null,
|
| 24 |
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"megatron_config": null,
|
| 25 |
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"megatron_core": "megatron.core",
|
| 26 |
+
"modules_to_save": null,
|
| 27 |
+
"monteclora_config": null,
|
| 28 |
+
"peft_type": "LORA",
|
| 29 |
+
"peft_version": "0.21.0",
|
| 30 |
+
"qalora_group_size": 16,
|
| 31 |
+
"r": 16,
|
| 32 |
+
"rank_pattern": {},
|
| 33 |
+
"revision": null,
|
| 34 |
+
"target_modules": [
|
| 35 |
+
"gate_proj",
|
| 36 |
+
"v_proj",
|
| 37 |
+
"o_proj",
|
| 38 |
+
"k_proj",
|
| 39 |
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"up_proj",
|
| 40 |
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"down_proj",
|
| 41 |
+
"q_proj"
|
| 42 |
+
],
|
| 43 |
+
"target_parameters": null,
|
| 44 |
+
"task_type": "FEATURE_EXTRACTION",
|
| 45 |
+
"trainable_token_indices": null,
|
| 46 |
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"use_bdlora": null,
|
| 47 |
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"use_dora": false,
|
| 48 |
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"use_qalora": false,
|
| 49 |
+
"use_rslora": false,
|
| 50 |
+
"velora_config": null
|
| 51 |
+
}
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:c3101a8a563cff76b75a7f9cec94fe76c1ddbe1f03a5d249fa03abe733156f66
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| 3 |
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size 132184864
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added_tokens.json
ADDED
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@@ -0,0 +1,28 @@
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{
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| 2 |
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| 3 |
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| 8 |
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| 9 |
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| 10 |
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|
| 11 |
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| 12 |
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| 13 |
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| 14 |
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| 15 |
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| 16 |
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| 26 |
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| 27 |
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| 28 |
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}
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head.pt
ADDED
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| 1 |
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version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:f1fbdffc9fe963057689b49098d412fc8ece6670d76b1735f94ac840aee76436
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| 3 |
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size 5248063
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merges.txt
ADDED
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The diff for this file is too large to render.
See raw diff
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provenance.json
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{
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"config": {
|
| 3 |
+
"epochs": 2,
|
| 4 |
+
"seed": 0,
|
| 5 |
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"lr": 5e-05,
|
| 6 |
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"lora": 16,
|
| 7 |
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"accum": 2,
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| 8 |
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"batch": 4,
|
| 9 |
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"perm_kl": 0.0,
|
| 10 |
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"perm_frac": 0.3,
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| 11 |
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"ord_w": 0.0,
|
| 12 |
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"p_none": 0.1,
|
| 13 |
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"p_none_distract": 0.12,
|
| 14 |
+
"p_distract": 0.15,
|
| 15 |
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"p_none_pair": 0.25,
|
| 16 |
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"synthetic_repeat": 1,
|
| 17 |
+
"public_frac": 1.0,
|
| 18 |
+
"base": "Qwen/Qwen3-4B-Base",
|
| 19 |
+
"dtype": "bf16",
|
| 20 |
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"checkpointing": 1
|
| 21 |
+
},
|
| 22 |
+
"config_sha256": "5d3deac10148d21541e48b12430160d3e7a79e3b4f61bcbbf98f934a79c5367c",
|
| 23 |
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"suite_sha256": "1b33e566d114f9eafeff55b36c221fadb2a4ae358a1b9cc68006e82c7cfad8f1",
|
| 24 |
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"source_hashes": {
|
| 25 |
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"kev/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
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| 26 |
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"kev/api.py": "cdb0602684d798ccd3fc2c86be622f2a8e7bcdeee64d07f95604c7d3fd4701ec",
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| 27 |
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"kev/autoresearch.py": "ca45a4e2bc91ae241d841e6543bc2715f00054d928668d6c8d24bb6f7cfa3db4",
|
| 28 |
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"kev/benchmark.py": "042747fa86d4f10b3bb7c2a45ad3ff671183f45872bc3ab029e7743364a51a28",
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| 29 |
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"kev/compare.py": "bd0445f021de59e35c7bff9304e39dd2e1211e3877a453575594ae7b81b0ada4",
|
| 30 |
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"kev/composition.py": "7667e02da107548cd85953da60c9b411f7be1cb8aef01e1eb0301b68f8d4457d",
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| 31 |
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"kev/contrastive.py": "d6c9a577c9ad598416ed96f7c9037fd7541f1a53b36fd9c22bdae547b554ae87",
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| 32 |
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"kev/data.py": "4296cea986f53383475d37438aa3ca2ba7cb258fe85a78b534a0aeccd02b9711",
|
| 33 |
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"kev/evaluate.py": "07664e4ac95c76054025e0850785fe5a16cef48a964679bdbab0137fe37147c1",
|
| 34 |
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"kev/experiment.py": "96bf40aa3fc9fcfee845adabf61c232708a7cc7e6c9874c3d2af4a4c6364dae7",
|
| 35 |
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"kev/jev.py": "e0213782359ba2f95adbf045ddaf0a008b08b4162bf6a9b66d4ce55fc91a51cb",
|
| 36 |
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"kev/model.py": "214b43200c6f31b673c31023621bec3ed3f713e26542dd9f483f62a9f4d813b9",
|
| 37 |
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"kev/plot.py": "d689c7dd18cde7f9ea77cb4f55c50ff7da1880b21cb2ecf244348e45842a1382",
|
| 38 |
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"kev/publish.py": "d6d581b0fab520305a46ed980cfeec9fcde80d479d70c95bebe8bd47dd91bece",
|
| 39 |
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"kev/serve.py": "86bcf741a8942c8ab0c49a7af7385af6a3e0163d0a931da3dc4faa8ab4ae50b7",
|
| 40 |
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"kev/study_v3.py": "4cda234f0b92391f82ffb441f89bc142c969705ce223dcb1ae52ea782e0b4b63",
|
| 41 |
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"kev/suite.py": "c4db818f83d7abaf4d8349a5b58f20f70439e04127b0989eb1f831be85578d87",
|
| 42 |
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"kev/train.py": "fb509ed3ecb52dcd4d70759c988b1c1a733995d8c1f6187218020e56784c37f6",
|
| 43 |
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"modal_app.py": "db0f9dc408c88d43e07a8cf4c6673267b9c9d451628f5bd8c437cf4ffb4e081e",
|
| 44 |
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"pyproject.toml": "ce566ee11b68c0cb587bb591943641f403d761d820817693dae7590faacf9888",
|
| 45 |
+
"uv.lock": "846fdc64a8bd6279a17a4365f18cd4e92ba9622d50d378aafcb5be6604296138"
|
| 46 |
+
},
|
| 47 |
+
"git_commit": "639f756ff930fffbd347f5b6d11c0c754240665c",
|
| 48 |
+
"platform": "Linux-4.19.0-gvisor-x86_64-with-glibc2.36",
|
| 49 |
+
"torch": "2.8.0+cu128",
|
| 50 |
+
"device": "cuda",
|
| 51 |
+
"gpu": "NVIDIA H100 80GB HBM3",
|
| 52 |
+
"legacy_checkpoint": false
|
| 53 |
+
}
|
result.json
ADDED
|
@@ -0,0 +1,1526 @@
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|
| 1 |
+
{
|
| 2 |
+
"objective": -0.43626773843495975,
|
| 3 |
+
"paired_flip": {
|
| 4 |
+
"pairs": 80,
|
| 5 |
+
"flip_rate": 0.8,
|
| 6 |
+
"both_correct_rate": 0.7875,
|
| 7 |
+
"invariant_pairs": 32,
|
| 8 |
+
"invariance_rate": 0.96875,
|
| 9 |
+
"invariant_both_correct_rate": 0.75
|
| 10 |
+
},
|
| 11 |
+
"clean": {
|
| 12 |
+
"n": 1264,
|
| 13 |
+
"nll": 0.49270420325136344,
|
| 14 |
+
"acc": 0.8433544303797469,
|
| 15 |
+
"ece": 0.06594221953695525,
|
| 16 |
+
"brier": 0.2304253991005061,
|
| 17 |
+
"mean_conf": 0.9056805510694902,
|
| 18 |
+
"confident_error_rate": 0.041930379746835444,
|
| 19 |
+
"coverage_at_0_9": 0.7207278481012658,
|
| 20 |
+
"accuracy_at_0_9": 0.9418221734357849,
|
| 21 |
+
"selective": {
|
| 22 |
+
"0.5": {
|
| 23 |
+
"coverage": 0.5,
|
| 24 |
+
"accuracy": 0.9683544303797469,
|
| 25 |
+
"confidence_cutoff": 0.9969858274806879
|
| 26 |
+
},
|
| 27 |
+
"0.8": {
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"kev/__init__.py": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
|
| 1463 |
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"kev/api.py": "cdb0602684d798ccd3fc2c86be622f2a8e7bcdeee64d07f95604c7d3fd4701ec",
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| 1464 |
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"kev/autoresearch.py": "ca45a4e2bc91ae241d841e6543bc2715f00054d928668d6c8d24bb6f7cfa3db4",
|
| 1465 |
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"kev/benchmark.py": "042747fa86d4f10b3bb7c2a45ad3ff671183f45872bc3ab029e7743364a51a28",
|
| 1466 |
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"kev/compare.py": "bd0445f021de59e35c7bff9304e39dd2e1211e3877a453575594ae7b81b0ada4",
|
| 1467 |
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"kev/composition.py": "7667e02da107548cd85953da60c9b411f7be1cb8aef01e1eb0301b68f8d4457d",
|
| 1468 |
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"kev/contrastive.py": "d6c9a577c9ad598416ed96f7c9037fd7541f1a53b36fd9c22bdae547b554ae87",
|
| 1469 |
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"kev/data.py": "4296cea986f53383475d37438aa3ca2ba7cb258fe85a78b534a0aeccd02b9711",
|
| 1470 |
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"kev/evaluate.py": "07664e4ac95c76054025e0850785fe5a16cef48a964679bdbab0137fe37147c1",
|
| 1471 |
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"kev/experiment.py": "96bf40aa3fc9fcfee845adabf61c232708a7cc7e6c9874c3d2af4a4c6364dae7",
|
| 1472 |
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"kev/jev.py": "e0213782359ba2f95adbf045ddaf0a008b08b4162bf6a9b66d4ce55fc91a51cb",
|
| 1473 |
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"kev/model.py": "214b43200c6f31b673c31023621bec3ed3f713e26542dd9f483f62a9f4d813b9",
|
| 1474 |
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"kev/plot.py": "d689c7dd18cde7f9ea77cb4f55c50ff7da1880b21cb2ecf244348e45842a1382",
|
| 1475 |
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"kev/publish.py": "d6d581b0fab520305a46ed980cfeec9fcde80d479d70c95bebe8bd47dd91bece",
|
| 1476 |
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"kev/serve.py": "86bcf741a8942c8ab0c49a7af7385af6a3e0163d0a931da3dc4faa8ab4ae50b7",
|
| 1477 |
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"kev/study_v3.py": "4cda234f0b92391f82ffb441f89bc142c969705ce223dcb1ae52ea782e0b4b63",
|
| 1478 |
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"kev/suite.py": "c4db818f83d7abaf4d8349a5b58f20f70439e04127b0989eb1f831be85578d87",
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| 1479 |
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"kev/train.py": "fb509ed3ecb52dcd4d70759c988b1c1a733995d8c1f6187218020e56784c37f6",
|
| 1480 |
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"modal_app.py": "db0f9dc408c88d43e07a8cf4c6673267b9c9d451628f5bd8c437cf4ffb4e081e",
|
| 1481 |
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"pyproject.toml": "ce566ee11b68c0cb587bb591943641f403d761d820817693dae7590faacf9888",
|
| 1482 |
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"uv.lock": "846fdc64a8bd6279a17a4365f18cd4e92ba9622d50d378aafcb5be6604296138"
|
| 1483 |
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},
|
| 1484 |
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"git_commit": "639f756ff930fffbd347f5b6d11c0c754240665c",
|
| 1485 |
+
"platform": "Linux-4.19.0-gvisor-x86_64-with-glibc2.36",
|
| 1486 |
+
"torch": "2.8.0+cu128",
|
| 1487 |
+
"device": "cuda",
|
| 1488 |
+
"gpu": "NVIDIA H100 80GB HBM3",
|
| 1489 |
+
"legacy_checkpoint": false
|
| 1490 |
+
},
|
| 1491 |
+
"mechanism_checks": {
|
| 1492 |
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"n": 8,
|
| 1493 |
+
"packed_max_delta": 0.0,
|
| 1494 |
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"sibling_max_delta": 4.231929779052734e-06,
|
| 1495 |
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"tolerance": 0.001,
|
| 1496 |
+
"passed": true
|
| 1497 |
+
},
|
| 1498 |
+
"gates": {
|
| 1499 |
+
"passed": false,
|
| 1500 |
+
"checks": {
|
| 1501 |
+
"complete_coverage": true,
|
| 1502 |
+
"isolation_and_packing": true,
|
| 1503 |
+
"transfer_complete": true,
|
| 1504 |
+
"heldout_pairs_at_least_70pct": false,
|
| 1505 |
+
"transfer_confident_errors_below_10pct": true
|
| 1506 |
+
},
|
| 1507 |
+
"policy": "Research screening only: 1e-3 isolation; 5pp task regression; 70% heldout pair correctness; <=10% confident errors; no automatic release."
|
| 1508 |
+
},
|
| 1509 |
+
"wall_seconds": 2329.125955744,
|
| 1510 |
+
"promotable": false,
|
| 1511 |
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"test_evaluated": false,
|
| 1512 |
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"training_resources": {
|
| 1513 |
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"wall_seconds": 2147.780514717102,
|
| 1514 |
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"records_seen": 27070,
|
| 1515 |
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"requested_records": 21792,
|
| 1516 |
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|
| 1517 |
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"rejected_records": 0,
|
| 1518 |
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"optimizer_steps": 2724,
|
| 1519 |
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"forward_tokens": 5366731,
|
| 1520 |
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"peak_device_bytes": 22944659968,
|
| 1521 |
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"device": "cuda",
|
| 1522 |
+
"dtype": "bf16",
|
| 1523 |
+
"batch": 4,
|
| 1524 |
+
"peak_rss_bytes": 25038151680
|
| 1525 |
+
}
|
| 1526 |
+
}
|
special_tokens_map.json
ADDED
|
@@ -0,0 +1,31 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"additional_special_tokens": [
|
| 3 |
+
"<|im_start|>",
|
| 4 |
+
"<|im_end|>",
|
| 5 |
+
"<|object_ref_start|>",
|
| 6 |
+
"<|object_ref_end|>",
|
| 7 |
+
"<|box_start|>",
|
| 8 |
+
"<|box_end|>",
|
| 9 |
+
"<|quad_start|>",
|
| 10 |
+
"<|quad_end|>",
|
| 11 |
+
"<|vision_start|>",
|
| 12 |
+
"<|vision_end|>",
|
| 13 |
+
"<|vision_pad|>",
|
| 14 |
+
"<|image_pad|>",
|
| 15 |
+
"<|video_pad|>"
|
| 16 |
+
],
|
| 17 |
+
"eos_token": {
|
| 18 |
+
"content": "<|endoftext|>",
|
| 19 |
+
"lstrip": false,
|
| 20 |
+
"normalized": false,
|
| 21 |
+
"rstrip": false,
|
| 22 |
+
"single_word": false
|
| 23 |
+
},
|
| 24 |
+
"pad_token": {
|
| 25 |
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"content": "<|endoftext|>",
|
| 26 |
+
"lstrip": false,
|
| 27 |
+
"normalized": false,
|
| 28 |
+
"rstrip": false,
|
| 29 |
+
"single_word": false
|
| 30 |
+
}
|
| 31 |
+
}
|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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|
|
|
|
|
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|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
|
| 3 |
+
size 11422654
|
tokenizer_config.json
ADDED
|
@@ -0,0 +1,239 @@
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|
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|
|
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|
|
|
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|
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|
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
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|
|
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|
|
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|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
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|
|
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|
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|
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|
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|
|
|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
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|
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|
|
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|
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|
|
|
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|
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|
|
|
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|
|
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|
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|
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|
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|
|
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|
|
|
|
|
|
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|
|
|
|
|
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|
|
|
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|
|
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|
|
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|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
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|
|
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|
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|
|
|
|
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|
|
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|
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|
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|
|
|
|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
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"add_bos_token": false,
|
| 3 |
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"add_prefix_space": false,
|
| 4 |
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"added_tokens_decoder": {
|
| 5 |
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"151643": {
|
| 6 |
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"content": "<|endoftext|>",
|
| 7 |
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"lstrip": false,
|
| 8 |
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"normalized": false,
|
| 9 |
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"rstrip": false,
|
| 10 |
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"single_word": false,
|
| 11 |
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"special": true
|
| 12 |
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},
|
| 13 |
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"151644": {
|
| 14 |
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"content": "<|im_start|>",
|
| 15 |
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"lstrip": false,
|
| 16 |
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"normalized": false,
|
| 17 |
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"rstrip": false,
|
| 18 |
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"single_word": false,
|
| 19 |
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"special": true
|
| 20 |
+
},
|
| 21 |
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"151645": {
|
| 22 |
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"content": "<|im_end|>",
|
| 23 |
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|
| 24 |
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|
| 25 |
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|
| 26 |
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|
| 27 |
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"special": true
|
| 28 |
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},
|
| 29 |
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"151646": {
|
| 30 |
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"content": "<|object_ref_start|>",
|
| 31 |
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|
| 32 |
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|
| 33 |
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"rstrip": false,
|
| 34 |
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"single_word": false,
|
| 35 |
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"special": true
|
| 36 |
+
},
|
| 37 |
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"151647": {
|
| 38 |
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"content": "<|object_ref_end|>",
|
| 39 |
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|
| 40 |
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"normalized": false,
|
| 41 |
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|
| 42 |
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"single_word": false,
|
| 43 |
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"special": true
|
| 44 |
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},
|
| 45 |
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"151648": {
|
| 46 |
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"content": "<|box_start|>",
|
| 47 |
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|
| 48 |
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|
| 49 |
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|
| 50 |
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|
| 51 |
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|
| 52 |
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},
|
| 53 |
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"151649": {
|
| 54 |
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"content": "<|box_end|>",
|
| 55 |
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|
| 56 |
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|
| 57 |
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|
| 58 |
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|
| 59 |
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|
| 60 |
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},
|
| 61 |
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|
| 62 |
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|
| 63 |
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|
| 64 |
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|
| 65 |
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|
| 66 |
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|
| 67 |
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"special": true
|
| 68 |
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},
|
| 69 |
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"151651": {
|
| 70 |
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"content": "<|quad_end|>",
|
| 71 |
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|
| 72 |
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|
| 73 |
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|
| 74 |
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|
| 75 |
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"special": true
|
| 76 |
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|
| 77 |
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"151652": {
|
| 78 |
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"content": "<|vision_start|>",
|
| 79 |
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|
| 80 |
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|
| 81 |
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|
| 82 |
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|
| 83 |
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"special": true
|
| 84 |
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},
|
| 85 |
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"151653": {
|
| 86 |
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"content": "<|vision_end|>",
|
| 87 |
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|
| 88 |
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|
| 89 |
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|
| 90 |
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|
| 91 |
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|
| 92 |
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|
| 93 |
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"151654": {
|
| 94 |
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"content": "<|vision_pad|>",
|
| 95 |
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|
| 96 |
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|
| 97 |
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|
| 98 |
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|
| 99 |
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"special": true
|
| 100 |
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|
| 101 |
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"151655": {
|
| 102 |
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"content": "<|image_pad|>",
|
| 103 |
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|
| 104 |
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"normalized": false,
|
| 105 |
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|
| 106 |
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"single_word": false,
|
| 107 |
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"special": true
|
| 108 |
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|
| 109 |
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"151656": {
|
| 110 |
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"content": "<|video_pad|>",
|
| 111 |
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|
| 112 |
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|
| 113 |
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|
| 114 |
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|
| 115 |
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"special": true
|
| 116 |
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|
| 117 |
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"151657": {
|
| 118 |
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|
| 119 |
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|
| 120 |
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|
| 121 |
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|
| 122 |
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|
| 123 |
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|
| 124 |
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|
| 125 |
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|
| 126 |
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|
| 127 |
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|
| 128 |
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|
| 129 |
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|
| 130 |
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|
| 131 |
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|
| 132 |
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|
| 133 |
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|
| 134 |
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|
| 135 |
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|
| 136 |
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|
| 137 |
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| 222 |
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| 228 |
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train.log
ADDED
|
@@ -0,0 +1,275 @@
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|
|
|
|
|
|
|
|
| 1 |
+
device=cuda trainable params=34.3M
|
| 2 |
+
10896 training requests (holdout=[]), questions by type {'choice': 5672, 'score': 3000, 'noul': 5224}
|
| 3 |
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|
| 4 |
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|
| 5 |
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|
| 6 |
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|
| 7 |
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|
| 8 |
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|
| 9 |
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|
| 10 |
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|
| 11 |
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|
| 12 |
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|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 18 |
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| 19 |
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| 20 |
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| 28 |
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| 29 |
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| 30 |
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|
| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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| 36 |
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| 37 |
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|
| 38 |
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| 39 |
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| 40 |
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| 41 |
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| 42 |
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| 43 |
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ep1 step 2350/2724 loss 0.507 kl 0.000 0.079s/rec
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ep1 step 2420/2724 loss 0.529 kl 0.000 0.079s/rec
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ep1 step 2430/2724 loss 0.431 kl 0.000 0.079s/rec
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ep1 step 2440/2724 loss 0.277 kl 0.000 0.079s/rec
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ep1 step 2450/2724 loss 0.768 kl 0.000 0.079s/rec
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ep1 step 2460/2724 loss 0.298 kl 0.000 0.079s/rec
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ep1 step 2470/2724 loss 0.277 kl 0.000 0.079s/rec
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ep1 step 2480/2724 loss 0.457 kl 0.000 0.079s/rec
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ep1 step 2490/2724 loss 0.394 kl 0.000 0.079s/rec
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ep1 step 2510/2724 loss 0.390 kl 0.000 0.079s/rec
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ep1 step 2520/2724 loss 0.275 kl 0.000 0.079s/rec
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ep1 step 2530/2724 loss 0.320 kl 0.000 0.079s/rec
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ep1 step 2540/2724 loss 0.259 kl 0.000 0.079s/rec
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ep1 step 2550/2724 loss 0.440 kl 0.000 0.079s/rec
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ep1 step 2560/2724 loss 0.142 kl 0.000 0.079s/rec
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ep1 step 2570/2724 loss 0.603 kl 0.000 0.079s/rec
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ep1 step 2580/2724 loss 0.282 kl 0.000 0.079s/rec
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ep1 step 2590/2724 loss 0.465 kl 0.000 0.079s/rec
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ep1 step 2600/2724 loss 0.234 kl 0.000 0.079s/rec
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ep1 step 2610/2724 loss 0.260 kl 0.000 0.079s/rec
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ep1 step 2620/2724 loss 0.211 kl 0.000 0.079s/rec
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ep1 step 2630/2724 loss 0.258 kl 0.000 0.079s/rec
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ep1 step 2640/2724 loss 0.317 kl 0.000 0.079s/rec
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ep1 step 2650/2724 loss 0.175 kl 0.000 0.079s/rec
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ep1 step 2660/2724 loss 0.281 kl 0.000 0.079s/rec
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ep1 step 2670/2724 loss 0.547 kl 0.000 0.079s/rec
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ep1 step 2700/2724 loss 0.462 kl 0.000 0.079s/rec
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ep1 step 2710/2724 loss 0.383 kl 0.000 0.079s/rec
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ep1 step 2720/2724 loss 0.372 kl 0.000 0.079s/rec
|
| 275 |
+
saved /runs/lowdrift-4b-v4/01-trial-1/checkpoint
|
training_config.json
ADDED
|
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|
| 1 |
+
{
|
| 2 |
+
"args": {
|
| 3 |
+
"base": "Qwen/Qwen3-4B-Base",
|
| 4 |
+
"n_per_source": 1000,
|
| 5 |
+
"epochs": 2,
|
| 6 |
+
"lr": 5e-05,
|
| 7 |
+
"lora": 16,
|
| 8 |
+
"accum": 2,
|
| 9 |
+
"holdout": "",
|
| 10 |
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"perm_kl": 0.0,
|
| 11 |
+
"perm_frac": 0.3,
|
| 12 |
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"ord_w": 0.0,
|
| 13 |
+
"suite": "/root/evals/v4/decision-v4",
|
| 14 |
+
"train_sources": "",
|
| 15 |
+
"device": "cuda",
|
| 16 |
+
"batch": 4,
|
| 17 |
+
"dtype": "bf16",
|
| 18 |
+
"checkpointing": 1,
|
| 19 |
+
"option_isolation": 0,
|
| 20 |
+
"special_embeddings": 0,
|
| 21 |
+
"head_dim": 256,
|
| 22 |
+
"lora_targets": "all",
|
| 23 |
+
"base_revision": "",
|
| 24 |
+
"p_none": 0.1,
|
| 25 |
+
"p_none_distract": 0.12,
|
| 26 |
+
"p_distract": 0.15,
|
| 27 |
+
"p_none_pair": 0.25,
|
| 28 |
+
"synthetic_repeat": 1,
|
| 29 |
+
"public_frac": 1.0,
|
| 30 |
+
"out": "/runs/lowdrift-4b-v4/01-trial-1/checkpoint",
|
| 31 |
+
"seed": 0
|
| 32 |
+
},
|
| 33 |
+
"suite_sha256": "1b33e566d114f9eafeff55b36c221fadb2a4ae358a1b9cc68006e82c7cfad8f1",
|
| 34 |
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"base_revision": "906bfd4b4dc7f14ee4320094d8b41684abff8539",
|
| 35 |
+
"ordinal_objective": "ranked_probability_score",
|
| 36 |
+
"holdout": []
|
| 37 |
+
}
|
training_metrics.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
+
{
|
| 2 |
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"wall_seconds": 2147.780514717102,
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| 3 |
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"records_seen": 27070,
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| 4 |
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"requested_records": 21792,
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| 5 |
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| 6 |
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| 8 |
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| 9 |
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| 12 |
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| 13 |
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| 14 |
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}
|
vocab.json
ADDED
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