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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.

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.

Figment Finetuned Model Archive

Contents

Intended Use

Model Details

V1 Pilot Checkpoint

V2 Checkpoint

V3 Checkpoint

V4 Checkpoint

V5 Checkpoint

V6 Checkpoint

V7 Checkpoint

V8-V14p Checkpoints

Training Data

Evaluation

Safety and Limitations

License and Attribution

Citation