Instructions to use authentrics/nemotron-training-dynamics with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use authentrics/nemotron-training-dynamics with Transformers:

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

pipe = pipeline("text-generation", model="authentrics/nemotron-training-dynamics")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("authentrics/nemotron-training-dynamics", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use authentrics/nemotron-training-dynamics with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "authentrics/nemotron-training-dynamics"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "authentrics/nemotron-training-dynamics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/authentrics/nemotron-training-dynamics

SGLang

How to use authentrics/nemotron-training-dynamics 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 "authentrics/nemotron-training-dynamics" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "authentrics/nemotron-training-dynamics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "authentrics/nemotron-training-dynamics" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "authentrics/nemotron-training-dynamics",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use authentrics/nemotron-training-dynamics with Docker Model Runner:
```
docker model run hf.co/authentrics/nemotron-training-dynamics
```

Diagnosing What Changed Across a Model's Training Run

Tracking parameter and activation drift across the NVIDIA Nemotron Cascade 8B checkpoint lineage to see what changed, where, and when during training.

Authentrics is a high-performance neural-network analysis library (Python wheel over a C++ core). It audits and maintains model checkpoints: parameter/behavioral drift, compliant data removal without full retraining, and loss-driven optimization without backprop. Analysis runs locally on your machine — only project metadata (names, descriptions) is exchanged with Authentrics servers, never your model weights.

What this demo shows

static_analysis — audit parameter drift between pairs of sequential checkpoints.
activation_analysis — catch behavioral drift in intermediate activations.

Reproduce this analysis

The public code and outputs behind this demo live in https://github.com/Authentrics-ai/authentrics-model-analysis-experiments:

src/analysis/nemotron_cascade_static.py
src/analysis/nemotron_cascade_dynamic.py
src/analysis/render_nemotron_dashboard.py Outputs (JSON + Plotly HTML dashboards) are published under output/nemotron_cascade/.

Weights: Training-dynamics walkthrough on the NVIDIA Nemotron Cascade 8B checkpoints; no derived weights are published. Reproduce the analysis locally with the SDK below.

Reproduce it yourself

pip install authentrics            # Linux x86_64, Python 3.11–3.13
authrx init                        # paste API key (stored at ~/.local/state/authentrics/api_key)
# or, for CI / non-interactive:
export AUTHRX_API_KEY=<your_api_key>

Generate an API key and read the full docs at https://app.authentrics.ai/.

Produced with the Authentrics SDK v0.35.1 — checkpoint analysis that runs locally on your own hardware; only project metadata ever leaves your machine, never your weights.

Model tree for authentrics/nemotron-training-dynamics

Base model

nvidia/Nemotron-Cascade-8B-Intermediate-ckpts

Finetuned

(1)

this model

Collection including authentrics/nemotron-training-dynamics

Diagnosing What Changed Across a Model's Training Run

Collection

Tracking parameter and activation drift across the NVIDIA Nemotron Cascade 8B checkpoint lineage to see what changed, where, and when. • 1 item • Updated 1 day ago

authentrics
/

nemotron-training-dynamics

Diagnosing What Changed Across a Model's Training Run

What this demo shows

Reproduce this analysis

Reproduce it yourself

Links

Model tree for authentrics/nemotron-training-dynamics

Collection including authentrics/nemotron-training-dynamics

Diagnosing What Changed Across a Model's Training Run