Instructions to use build-small-hackathon/figment-finetuned-model-archive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use build-small-hackathon/figment-finetuned-model-archive with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="build-small-hackathon/figment-finetuned-model-archive")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("build-small-hackathon/figment-finetuned-model-archive", dtype="auto")

PEFT
How to use build-small-hackathon/figment-finetuned-model-archive with PEFT:
```
Task type is invalid.
```

llama-cpp-python

How to use build-small-hackathon/figment-finetuned-model-archive with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="build-small-hackathon/figment-finetuned-model-archive",
	filename="figment_sft_v10/figment-sft-v10-lora-merged-bf16.bf16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use build-small-hackathon/figment-finetuned-model-archive with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf build-small-hackathon/figment-finetuned-model-archive:BF16
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf build-small-hackathon/figment-finetuned-model-archive:BF16
# Run inference directly in the terminal:
llama cli -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf build-small-hackathon/figment-finetuned-model-archive:BF16
# Run inference directly in the terminal:
./llama-cli -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf build-small-hackathon/figment-finetuned-model-archive:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Use Docker

docker model run hf.co/build-small-hackathon/figment-finetuned-model-archive:BF16

LM Studio
Jan

vLLM

How to use build-small-hackathon/figment-finetuned-model-archive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "build-small-hackathon/figment-finetuned-model-archive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/figment-finetuned-model-archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/build-small-hackathon/figment-finetuned-model-archive:BF16

SGLang

How to use build-small-hackathon/figment-finetuned-model-archive with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "build-small-hackathon/figment-finetuned-model-archive" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/figment-finetuned-model-archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "build-small-hackathon/figment-finetuned-model-archive" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "build-small-hackathon/figment-finetuned-model-archive",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use build-small-hackathon/figment-finetuned-model-archive with Ollama:
```
ollama run hf.co/build-small-hackathon/figment-finetuned-model-archive:BF16
```

Unsloth Studio

How to use build-small-hackathon/figment-finetuned-model-archive with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/figment-finetuned-model-archive to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for build-small-hackathon/figment-finetuned-model-archive to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for build-small-hackathon/figment-finetuned-model-archive to start chatting

How to use build-small-hackathon/figment-finetuned-model-archive with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "build-small-hackathon/figment-finetuned-model-archive:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use build-small-hackathon/figment-finetuned-model-archive with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf build-small-hackathon/figment-finetuned-model-archive:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default build-small-hackathon/figment-finetuned-model-archive:BF16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use build-small-hackathon/figment-finetuned-model-archive with Docker Model Runner:
```
docker model run hf.co/build-small-hackathon/figment-finetuned-model-archive:BF16
```

Lemonade

How to use build-small-hackathon/figment-finetuned-model-archive with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull build-small-hackathon/figment-finetuned-model-archive:BF16

Run and chat with the model

lemonade run user.figment-finetuned-model-archive-BF16

List all available models

lemonade list

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.

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GGUF

Model size

4B params

Architecture

nemotron_h

Hardware compatibility

16-bit

Model tree for build-small-hackathon/figment-finetuned-model-archive

Base model

nvidia/NVIDIA-Nemotron-Nano-12B-v2-Base

Finetuned

nvidia/NVIDIA-Nemotron-Nano-12B-v2

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figment-finetuned-model-archive

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

Model tree for build-small-hackathon/figment-finetuned-model-archive

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