Instructions to use tzchen07/Nemotron-3-Super-120B-project-health-sft-e1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tzchen07/Nemotron-3-Super-120B-project-health-sft-e1 with Transformers:

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

pipe = pipeline("text-generation", model="tzchen07/Nemotron-3-Super-120B-project-health-sft-e1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tzchen07/Nemotron-3-Super-120B-project-health-sft-e1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tzchen07/Nemotron-3-Super-120B-project-health-sft-e1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tzchen07/Nemotron-3-Super-120B-project-health-sft-e1

SGLang

How to use tzchen07/Nemotron-3-Super-120B-project-health-sft-e1 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 "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1" \
    --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": "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1",
		"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 "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1" \
        --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": "tzchen07/Nemotron-3-Super-120B-project-health-sft-e1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tzchen07/Nemotron-3-Super-120B-project-health-sft-e1 with Docker Model Runner:
```
docker model run hf.co/tzchen07/Nemotron-3-Super-120B-project-health-sft-e1
```

Configuration Parsing Warning:Invalid JSON for config file config.json

Nemotron-3-Super-120B — Project-Health Risk Prediction SFT (iter001b · E1)

Full-parameter SFT of NVIDIA Nemotron-3-Super-120B (hybrid Mamba-2 + Latent-MoE, 512 experts) for project-health risk prediction: given a project snapshot at a cut-off time T, the model reasons through open questions and emits a strict <PredictedRisks> block of <Risk> items (Statement / Reasoning / Tag / Severity / Time / Likelihood / Mitigation).

This is checkpoint E1 — the winner of training iteration 001b.

Results (20 held-out eval projects, greedy decode)

Model	D1 recall	D2 precision	D3 calibration	D4 reasoning	D5 format	Total /25
E1 (this model, 1 epoch)	2.72	3.44	2.61	3.29	4.95	17.01
base (Nemotron-3-Super-120B)	2.60	3.18	2.36	3.19	4.78	16.11
E2 (2 epochs)	2.79	2.81	2.59	3.03	4.60	15.82

E1 beats the base model by +0.9/25 (wins 10/20 examples) and is degeneration-free: 0/20 repetition loops and 20/20 valid schema under greedy decoding — versus the prior SFT run, which looped catastrophically (one output repeated a phrase ×159 to the 32K cap). Scored per an internal risk-prediction rubric (D1–D5).

Training

Recipe: 1 epoch, LR 2e-6 (cosine, ~5% warmup), weight_decay 0.1, seq_length 10240, bf16.
Stack: Megatron-Core + Megatron-Bridge, TP=8 / EP=8 / DP=2 on 16×H200, CPU-offloaded distributed optimizer.
Key fix vs the prior run: corrected the chat-SFT loss mask so loss is computed on assistant tokens only (the prior run trained on the full prompt, which — together with over-training — caused greedy repetition/non-termination). 1 epoch is the sweet spot; 2 epochs over-trains (worse precision + a degeneration).

Intended use & limitations

Use: project-health risk forecasting / triage assistance from structured project snapshots.
Known limitation (next-iteration target): like the base model, it tends to over-rate severity (CRITICAL/HIGH) on projects that ultimately resolve benignly, and under-recalls long-tail/low-severity risks.
Inference: use the model's chat template with thinking enabled; greedy decoding is safe.

Fine-tuned on internal project data — private.

Downloads last month: 20

Safetensors

Model size

124B params

Tensor type

F32

BF16