Figment Finetuned Model Archive
This repository archives early Figment local-model training artifacts for nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16.
Figment is a prototype protocol-navigation aid for trained field responders working with synthetic or de-identified rural-clinic and disaster-response scenarios. It is designed to structure field notes, preserve deterministic red-flag rules, cite retrieved protocol cards, plan missing observations, draft responder checklists, and prepare SBAR-style handoffs.
The published artifacts include the figment_sft_v1 pilot merged BF16 checkpoint from June 8, 2026, the figment_sft_v2 merged BF16/GGUF checkpoint from June 9, 2026, the figment_sft_v3 merged BF16/GGUF checkpoint from June 10, 2026, and the figment_sft_v4 through figment_sft_v14p merged BF16/GGUF checkpoints from the June 11-13, 2026 field-workflow loop. The v1 pilot is retained for archival continuity, v2 improved raw configured-model behavior on the locked 50-case harness, v3 improved the field-holdout surface, v4 established the first archived field-workflow checkpoint, v5 is retained as a regression artifact, v6-v13 show the corrected field-workflow iteration path, and v14p plus its repair-union harness run is the strongest archived local field-workflow checkpoint in this repository.
Contents
| Path | Contents | Notes |
|---|
figment_sft_v1/pilot-20260608-merged-bf16/ | figment_sft_v1 pilot adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v1 pilot checkpoint. No v1 GGUF sidecar is archived in this repo. |
v2-20260609-merged-bf16/ | figment_sft_v2 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v2 locked-harness checkpoint. |
v2-20260609-merged-bf16.gguf | BF16 GGUF conversion of the v2 merged checkpoint | SHA-256: 281251bf326bfef219fe213cf01d7457164972ce2f99067b0ccc1fdb5821ea01. |
v3-20260610-merged-bf16/ | figment_sft_v3 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v3 field-workflow model. |
v3-20260610-merged-bf16.gguf | BF16 GGUF conversion of the v3 merged checkpoint | SHA-256: 7ee6439f87d50af289136a345ee73e633e20035c79582f942f03f9331bb8a658. |
v4-20260611-merged-bf16/ | figment_sft_v4 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v4 field-workflow model. |
v4-20260611-merged-bf16.gguf | BF16 GGUF conversion of the v4 merged checkpoint | SHA-256: 7e11f2295b101e9312f97075b8e48cabd8cc89539e92c8fa4218c4973aa31d8d. |
figment_sft_v5/figment-sft-v5-lora-merged-bf16/ | figment_sft_v5 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v5 field-workflow regression artifact. |
figment_sft_v5/figment-sft-v5-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v5 merged checkpoint | Published LFS SHA-256: c7f9b38d267c2ab2b791b613e0227ce3d057e61b57b568b16ca501f2e516379c. |
figment_sft_v6/figment-sft-v6-lora-merged-bf16/ | figment_sft_v6 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v6 field-workflow model. |
figment_sft_v6/figment-sft-v6-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v6 merged checkpoint | Published LFS SHA-256: 92fb2bb4a8686230f050c1696e6df749fe49ec4d41221ab9100785afa7e34009. |
figment_sft_v7/figment-sft-v7-lora-merged-bf16/ | figment_sft_v7 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v7 field-workflow model. |
figment_sft_v7/figment-sft-v7-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v7 merged checkpoint | Published LFS SHA-256: d85f9dd7137453035ae8ec96bcee1998358ad5975bb9c842fe9b7a077c4002b9. |
figment_sft_v8/figment-sft-v8-lora-merged-bf16/ | figment_sft_v8 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v8 field-workflow model. |
figment_sft_v8/figment-sft-v8-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v8 merged checkpoint | Published LFS SHA-256: d45660834ce2f9229d0e43ed3ac6bd041dba876f54ff1cc384889b9594b5e78d. |
figment_sft_v9/figment-sft-v9-lora-merged-bf16/ | figment_sft_v9 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v9 field-workflow model. |
figment_sft_v9/figment-sft-v9-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v9 merged checkpoint | Published LFS SHA-256: 79ec6bfb55895c90ed4188d9e4052730ac07f2f5c6fe49c5fd7ef44c7e0a7d16. |
figment_sft_v10/figment-sft-v10-lora-merged-bf16/ | figment_sft_v10 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v10 field-workflow model. |
figment_sft_v10/figment-sft-v10-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v10 merged checkpoint | Published LFS SHA-256: 85bc2978be155e1cdf12b42c8ccf84e1c1b65ad2da6b463d7be726d33cbd31aa. |
figment_sft_v11/figment-sft-v11-lora-merged-bf16/ | figment_sft_v11 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v11 field-workflow model. |
figment_sft_v11/figment-sft-v11-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v11 merged checkpoint | Published LFS SHA-256: cb5c99e32660547941a681853c30eff47cd2a9aee837fbdd3ee17684b44d4fd2. |
figment_sft_v12/figment-sft-v12-lora-merged-bf16/ | figment_sft_v12 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v12 field-workflow model. |
figment_sft_v12/figment-sft-v12-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v12 merged checkpoint | Published LFS SHA-256: 164ebf943919b4c27a54dbce3380bc156bbad3c6e893f1d185d35801eac015b7. |
figment_sft_v13/figment-sft-v13-lora-merged-bf16/ | figment_sft_v13 adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v13 field-workflow model. |
figment_sft_v13/figment-sft-v13-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v13 merged checkpoint | Published LFS SHA-256: 1cedcc48d2edf82f31ebd20d8885bdd7b72d07b8d551b19838394ba57a1f2e1e. |
figment_sft_v14p/figment-sft-v14p-lora-merged-bf16/ | figment_sft_v14p adapter merged into the BF16 base with peft.merge_and_unload(safe_merge=True) | Full merged Hugging Face weights for the v14p field-workflow model. |
figment_sft_v14p/figment-sft-v14p-lora-merged-bf16.bf16.gguf | BF16 GGUF conversion of the v14p merged checkpoint | Published LFS SHA-256: 53de48e5f7a7fa22af7a682686adcf6c0be7c5c1fe72f72ea39d80bd68333f72. |
Intended Use
Use this repo as an artifact archive for:
- reproducing the Modal train/merge/GGUF proof chain,
- comparing later Figment checkpoints against a known early baseline,
- inspecting the v1 pilot merged BF16 checkpoint,
- evaluating the v2 locked-harness protocol-navigation checkpoint,
- evaluating the v3 local/off-grid protocol-navigation checkpoint,
- evaluating the v4 local/off-grid field-workflow checkpoint,
- evaluating the v5 regression artifact and v6-v14p local/off-grid field-workflow checkpoints,
- debugging protocol-navigation behavior in synthetic or de-identified scenarios.
Do not use these artifacts for clinical care, autonomous triage, diagnosis, prescribing, medication dosing, or replacing local protocol or trained responder judgment.
Model Details
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- Base model revision observed during the project:
dfaf35de3e30f1867dd8dbc38a7fc9fb52d3914f
- Model family: Nemotron 3 Nano 4B BF16, text generation
- Adapter method: PEFT LoRA
- LoRA rank: 16
- LoRA alpha: 32
- LoRA dropout: 0.05
- Target modules:
up_proj, in_proj, q_proj, k_proj, out_proj, v_proj, down_proj, o_proj
- Max sequence length used for local 4B training: 16384
- Language: English
- Domain: synthetic field-clinic and disaster-response protocol navigation
V1 Pilot Checkpoint
The v1 pilot artifact was trained as figment_sft_v1 and merged from Modal checkpoint /checkpoints/figment_sft_v1/pilot-20260608 into /checkpoints/figment_sft_v1/pilot-20260608-merged-bf16.
Archive summary:
- Artifact path:
figment_sft_v1/pilot-20260608-merged-bf16/
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- HF shard 1 LFS SHA-256:
aebcb7fd3126d0100cc7e78e58e0ed49ab29aad8f858f2c6149637aced9c699f
- HF shard 2 LFS SHA-256:
8a1a7b48e647dd43cb7941a9e6a3f7a839326865034f626b2705645b0e29c830
- Tokenizer LFS SHA-256:
623c34567aebb18582765289fbe23d901c62704d6518d71866e0e58db892b5b7
- GGUF sidecar: not archived; no v1 GGUF cache was present in
figment-eval-results:/model_cache/figment_sft_v1.
V2 Checkpoint
The v2 artifact was trained as figment_sft_v2 and merged from Modal checkpoint /checkpoints/figment_sft_v2/figment-sft-v2-lora into /checkpoints/figment_sft_v2/figment-sft-v2-lora-merged-bf16.
Training data and merge summary:
- Training rows: 1500
- Train rows: 1352
- Validation rows: 148
- Navigator-full rows: 1000
- Focused-repair rows: 500
- Train split SHA-256:
27233926a2bd9320418ff10b0c14f3885834adf2f48865ee469c939e2ffeb68a
- Validation split SHA-256:
7964c75cd3940a8549e6b8b2ef15b4d5cd45e8607af8f77a4982ffe01116bfb4
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- Merged manifest SHA-256:
6885f758f30a76e798fac73ebedd64684f3287d6b459f2b625029b03031179dc
- HF shard 1 SHA-256:
9e224445985294263fce0437f82e55d116e90f5f19a5b995d47ee5081ff97c63
- HF shard 2 SHA-256:
758eb779adf5379fb96ea42c4c38cfc6de9dc3d53c4e3863a7aea15ccebae5ae
- GGUF SHA-256:
281251bf326bfef219fe213cf01d7457164972ce2f99067b0ccc1fdb5821ea01
The v2 local evaluation run was local_4b_v2_lora_20260609T103344Z on the locked 50-case local harness.
V3 Checkpoint
The v3 artifact was trained as figment_sft_v3 and merged from Modal checkpoint /checkpoints/figment_sft_v3/figment-sft-v3-lora into /checkpoints/figment_sft_v3/figment-sft-v3-lora-merged-bf16.
Training and merge summary:
- Training run:
700/700 optimizer steps
- Final eval loss:
0.04357146
- Final train loss:
0.60960097
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- Merged manifest SHA-256:
d18e72fb258764321ec17abd687af7214a480f491f11d83cf64e38824dc4e510
- GGUF SHA-256:
7ee6439f87d50af289136a345ee73e633e20035c79582f942f03f9331bb8a658
The clean v3 field-holdout eval was the sequential run local_4b_v3_lora_field_holdout_20260610T102450Z, not the earlier parallel run that hit a llama.cpp KV/context-overflow failure mode.
V4 Checkpoint
The v4 artifact was trained as figment_sft_v4 and merged from Modal checkpoint /checkpoints/figment_sft_v4/figment-sft-v4-lora into /checkpoints/figment_sft_v4/figment-sft-v4-lora-merged-bf16.
Training data and merge summary:
- Training rows: 1650
- Train rows: 1482
- Validation rows: 168
- Navigator-full rows: 1500
- Focused-repair rows: 150
- Full corpus SHA-256:
ef7a7c9a6a99927ba72ce244e03a9da3ab86d3cf5dc70786703fb5f8bdf2a289
- Train split SHA-256:
f869d79da9ef670bc6479f8321e51b1f48cb5a16423265f34893a08e7648676e
- Validation split SHA-256:
3ff7668b8216d6fa0be770d6d9ed5f1a0b12965f9312d5210b510807538738d3
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- Merged manifest SHA-256:
6678c0ec3a28817dba22eb9e7c682b9961f04bbfc688d0f1bcd137afaf8c8c38
- HF shard 1 SHA-256:
1d95889e945363adcd70a0be54bc29407d49e28bf7a2c0415e1732d81d64186c
- HF shard 2 SHA-256:
2a2e27563e78981c130349feece291c976cf7d5384690c6327795eef6d08d4c0
- GGUF SHA-256:
7e11f2295b101e9312f97075b8e48cabd8cc89539e92c8fa4218c4973aa31d8d
The v4 full field-holdout evaluation run was local_4b_finetuned_v4_field_holdout_20260611T011930Z. A separate 50-case evidence run was local_4b_finetuned_v4_evidence_20260611T0010Z.
V5 Checkpoint
The v5 artifact was trained as figment_sft_v5 and merged from Modal checkpoint /checkpoints/figment_sft_v5/figment-sft-v5-lora into /checkpoints/figment_sft_v5/figment-sft-v5-lora-merged-bf16.
Training data and merge summary:
- Training rows: 1300
- Train rows: 1170
- Validation rows: 130
- Navigator-full rows: 1100
- Focused-repair rows: 200
- Full corpus SHA-256:
3abc2dcb1f972ee6f536c273de69f72abe9a42e402a3548c451e442a3fcd4535
- Train split SHA-256:
08ad6b76e958249b50bece528e0b26f5d3ef090166d7e5e0d48ddc46101496c7
- Validation split SHA-256:
54aadd55ab41f00880483ff0beb08c9602aae23933efcabd328d1769617fbc1a
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- GGUF LFS SHA-256:
c7f9b38d267c2ab2b791b613e0227ce3d057e61b57b568b16ca501f2e516379c
The v5 field-holdout run was figment_sft_v5_field_workflow_holdout_modal_gpu_20260611_h100_gguf; it is retained as a regression artifact because it scored only 2/150 competence successes despite passing final JSON validation.
V6 Checkpoint
The v6 artifact was trained as figment_sft_v6 and merged from Modal checkpoint /checkpoints/figment_sft_v6/figment-sft-v6-lora into /checkpoints/figment_sft_v6/figment-sft-v6-lora-merged-bf16.
Training data and merge summary:
- Training rows: 2000
- Train rows: 1800
- Validation rows: 200
- Navigator-full rows: 1180
- Focused-repair rows: 820
- Full corpus SHA-256:
268cb36d0d36697006609f346b76c79dbf127f82837f5a1f76d47059b031c595
- Train split SHA-256:
b750779104e80a8a92c86437f9515da7a4ab97bc866c1e87f4d95fca269ab9c2
- Validation split SHA-256:
ca388117f77325a57c70af7d69145b429bd443a5ae134ce1ab419373154e25cf
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- GGUF LFS SHA-256:
92fb2bb4a8686230f050c1696e6df749fe49ec4d41221ab9100785afa7e34009
The v6 field-holdout run was figment_sft_v6_field_workflow_holdout_modal_gpu_20260611_h100_gguf.
V7 Checkpoint
The v7 artifact was trained as figment_sft_v7 and merged from Modal checkpoint /checkpoints/figment_sft_v7/figment-sft-v7-lora into /checkpoints/figment_sft_v7/figment-sft-v7-lora-merged-bf16.
Training data and merge summary:
- Training rows: 2800
- Train rows: 2520
- Validation rows: 280
- Navigator-full rows: 1740
- Focused-repair rows: 1060
- Full corpus SHA-256:
b8bc3830beb38577047dbb2b9760aa2845234e25f41457fbfc5ce25bb6821ac0
- Train split SHA-256:
283615b21446346a9090ad6d45e750f5812222625ddaa5d2a83a15f663cb7d04
- Validation split SHA-256:
fe7b683f5007ff1f3eaac2632c9d407a8671d23c944b17b192eae964c0bbaa8d
- Merge method:
peft.merge_and_unload(safe_merge=True)
- Merged dtype: BF16
- Base model:
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16
- GGUF LFS SHA-256:
d85f9dd7137453035ae8ec96bcee1998358ad5975bb9c842fe9b7a077c4002b9
The v7 field-holdout run was figment_sft_v7_field_workflow_holdout_modal_gpu_20260612_h100_gguf.
V8-V14p Checkpoints
The v8-v14p artifacts continue the corrected field-workflow training loop. Each checkpoint was merged from its Modal LoRA adapter into the same BF16 base with peft.merge_and_unload(safe_merge=True) and converted to BF16 GGUF for local llama.cpp evaluation.
| Version | Training rows | Train rows | Validation rows | Navigator rows | Focused-repair rows | Full corpus SHA-256 | Train split SHA-256 | Validation split SHA-256 | GGUF LFS SHA-256 |
|---|
| V8 | 3200 | 2880 | 320 | 2140 | 1060 | fbf2adb675d01c007f6defc0292d04574d671bd64cd112310771bb4f5161cecc | e4d81265d0d7d56443fd6afd91cd996c546e680b7e53e7200b09321c4bae56f5 | 4668f8f8aa558fe2e765feae91a82c661753da3836265e6485d1225629b55097 | d45660834ce2f9229d0e43ed3ac6bd041dba876f54ff1cc384889b9594b5e78d |
| V9 | 3600 | 3240 | 360 | 2540 | 1060 | ceb106258d4149305582620b5c4c308a7aa5854b6125e2c1d14b0d98cf5bbd6b | b3556bae88e13f980b22509a5463e192556515cdaf868f65f16ef4db41079513 | e2f4e13c516bea567e5f3501fc0b11143c100bca1afc65f907cb1ad27b211a85 | 79ec6bfb55895c90ed4188d9e4052730ac07f2f5c6fe49c5fd7ef44c7e0a7d16 |
| V10 | 4400 | 3960 | 440 | 3340 | 1060 | 6ba2a10a4f6afb3ba9a061ec966a68122b1c520b832b5e8e110de3900c2968bd | 2497bca472e188d202939e9a729d338e8fe6f30913d96e2b396339d421f7de4d | 256a1674930e57ccc7d511ea0825ef487a115bbf3c2aa79e7f2a4cc933c198fd | 85bc2978be155e1cdf12b42c8ccf84e1c1b65ad2da6b463d7be726d33cbd31aa |
| V11 | 5200 | 4680 | 520 | 4140 | 1060 | 867c5622aded6a73657e37f0a1468fb5edcfcc5c30c4d0e8eb7b5024a4786051 | 3e1606855dadfc0e67f4d45f4c98e729b697d095be2b351d0aa159c71f347eb3 | 970c8d00aeed2bec1bc069ae229ffc7865988d21f31dfc37de784cbd8b771b52 | cb5c99e32660547941a681853c30eff47cd2a9aee837fbdd3ee17684b44d4fd2 |
| V12 | 4960 | 4464 | 496 | 3900 | 1060 | 9e7ba0caab6137be3bf9936b8a0cd2aa70679d467e3555d507ad5af063fb3a4e | fe009fcf471cc61ddeb7e7aa7d993ad5dd28d4c58238050751d42fcfd3b79098 | bdbd3e51d0bf354da25b449de2d4164561f6f8d453cbbe5a196853b9eba40b23 | 164ebf943919b4c27a54dbce3380bc156bbad3c6e893f1d185d35801eac015b7 |
| V13 | 4465 | 4017 | 448 | 3405 | 1060 | e7d5f55259c4a0cbfc81e16c31a8a374837c654ea8e5723434ac882ce835da2b | 43106d3af0f494ca5ead39290f3ad142c7a1f73e46a98759a92aed7814083290 | f16c98ea146a7a17785a761d50df00efbc5781b272085ca5885540c0a33a0645 | 1cedcc48d2edf82f31ebd20d8885bdd7b72d07b8d551b19838394ba57a1f2e1e |
| V14p | 5335 | 4801 | 534 | 4275 | 1060 | b455460870c70c2072491b754ed128e04cee7e63f4876cd6e6bacc92164788d9 | 378379eccba716001eb30a4bee05948a5bcb34ef2caa6801442be733c0f5fff6 | aaf5d7c0b3236c98f7d29fcd9898ea6d6789978d468fb1e26d64b40097d2b86e | 53de48e5f7a7fa22af7a682686adcf6c0be7c5c1fe72f72ea39d80bd68333f72 |
Training Data
The model artifacts use synthetic and de-identified datasets generated inside the Figment project. Published training corpora are available in the dataset repository build-small-hackathon/figment-eval-traces under configs figment_sft_v1 through figment_sft_v14p. The dataset files are not duplicated in this model repository.
The examples were synthetic. They were designed to teach Figment's harness behavior, not to store medical knowledge. They included full navigator outputs and focused repair tasks for schema, citations/pathways, SBAR handoff fields, missing observations, protocol urgency, and forbidden clinical language.
Evaluation
For later eval-trace artifacts, see the dataset repository build-small-hackathon/figment-eval-traces.
Observed v2 locked-harness evaluation:
| Metric | V2 locked 50-case eval |
|---|
| Total cases | 50 |
| Competence successes | 33/50 |
| Raw configured-model successes | 33/50 |
| Focused-repair successes | 0 |
| Full fallback uses | 0 |
| Final validation successes | 50/50 |
| Model-visible fields retained | 627/650 |
Observed v3 field-holdout evaluation:
| Metric | V3 field holdout |
|---|
| Total cases | 150 |
| Competence successes | 107/150 |
| Raw configured-model successes | 93/150 |
| Focused-repair successes | 14 |
| Full fallback uses | 2 |
| Final validation successes | 148/150 |
| Model-visible fields retained | 1836/1950 |
Observed v4 evaluations:
| Metric | V4 50-case eval | V4 field holdout |
|---|
| Total cases | 50 | 150 |
| Competence successes | 37/50 | 109/150 |
| Raw configured-model successes | 37/50 | 109/150 |
| Expected-label successes | 14/50 | 149/150 |
| Full fallback uses | 0 | 2 |
| Final validation successes | 50/50 | 148/150 |
| Model-visible fields retained | 624/650 | 1846/1950 |
Observed v5-v7 field-holdout evaluations:
| Metric | V5 field holdout | V6 field holdout | V7 field holdout |
|---|
| Total cases | 150 | 150 | 150 |
| Competence successes | 2/150 | 142/150 | 148/150 |
| Raw configured-model successes | 2/150 | 142/150 | 148/150 |
| Expected-label successes | 150/150 | 146/150 | 145/150 |
| Full fallback uses | 0 | 0 | 0 |
| Final validation successes | 150/150 | 150/150 | 150/150 |
| Deterministic patch count | 302 | 21 | 4 |
| Model-visible field pass rate | 0.8451 | 0.9892 | 0.9979 |
| Mean latency | 4512.943 ms | 4407.568 ms | 4344.942 ms |
| P95 latency | 4714.603 ms | 4606.824 ms | 4565.243 ms |
Observed v8-v14p corrected field-holdout evaluations:
| Metric | V8 | V9 | V10 | V11 | V12 | V13 | V14p | V14p repair-union |
|---|
| Total cases | 150 | 150 | 150 | 150 | 150 | 150 | 150 | 150 |
| Competence successes | 146/150 | 146/150 | 147/150 | 145/150 | 146/150 | 146/150 | 146/150 | 150/150 |
| Raw configured-model successes | 146/150 | 146/150 | 147/150 | 143/150 | 146/150 | 145/150 | 146/150 | 146/150 |
| Expected-label successes | 150/150 | 150/150 | 150/150 | 148/150 | 150/150 | 149/150 | 150/150 | 150/150 |
| Full fallback uses | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Final validation successes | 150/150 | 150/150 | 150/150 | 150/150 | 150/150 | 150/150 | 150/150 | 150/150 |
| Deterministic patch count | 8 | 8 | 6 | 23 | 8 | 15 | 8 | 0 |
| Model-visible field pass rate | 0.9959 | 0.9959 | 0.9969 | 0.9882 | 0.9959 | 0.9923 | 0.9959 | 1.0000 |
| Mean latency | 4282.523 ms | 4338.318 ms | 4341.864 ms | 4491.265 ms | 4932.291 ms | 4362.790 ms | 5142.904 ms | 4505.249 ms |
| P95 latency | 4477.092 ms | 4613.479 ms | 4602.941 ms | 4664.988 ms | 5181.336 ms | 4532.570 ms | 5642.153 ms | 4708.240 ms |
Safety and Limitations
- Prototype only; not a medical device.
- Synthetic/de-identified scenarios only.
- The model must not diagnose, prescribe, dose medication, or autonomously triage.
- Deterministic red-flag rules and validators remain part of the Figment runtime. The model artifact alone is not the full safety system.
- Outputs require trained responder review and local protocol/supervisor/clinician judgment.
- The checkpoints may produce malformed, incomplete, unsupported, or overconfident outputs without the Figment harness.
License and Attribution
This archive is derived from
nvidia/NVIDIA-Nemotron-3-Nano-4B-BF16 and is governed by the same upstream
NVIDIA Nemotron Open Model License. Review the upstream model card and license before reuse. The Figment application code is Apache-2.0, and Figment synthetic datasets are documented separately as CC-BY-4.0 where published.
Citation
No paper is associated with these artifacts. Please cite the base model according to NVIDIA's guidance and cite this repository if using the Figment artifacts directly.
