ALCHEMI Toolkit is a Python framework that uses GPUs to run atomic simulations and train machine-learned models for chemistry and materials science. Researchers and developers use it for molecular dynamics, geometry relaxation, and model training across one or multiple GPUs. Its catalogue add-ons guide coding agents in using the toolkit’s APIs and repository conventions.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add NVIDIA/nvalchemi-toolkit --skill nvalchemi-fine-tuninggit clone --depth 1 https://github.com/NVIDIA/nvalchemi-toolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/nvidia/nvalchemi-toolkit/nvalchemi-fine-tuning)<a href="https://agentmods.dev/skills/nvidia/nvalchemi-toolkit/nvalchemi-fine-tuning"><img src="https://agentmods.dev/badge/skills/nvidia/nvalchemi-toolkit/nvalchemi-fine-tuning/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/nvidia/nvalchemi-toolkit/nvalchemi-fine-tuning"><img src="https://agentmods.dev/badge/skills/nvidia/nvalchemi-toolkit/nvalchemi-fine-tuning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00112 | $0.02275 |
| Opus 5 | $0.00056 | $0.01137 |
| Sonnet 5 | $0.00022 | $0.00455 |
| Haiku 4.5 | $0.00011 | $0.00228 |
Grade A, and why
nvalchemi-fine-tuning scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
nvalchemi Fine Tuning
Overview
Use FineTuningStrategy when adapting pretrained weights to a new dataset,
objective, trainable parameter set, or model head. Link users to
docs/userguide/finetuning.md, docs/userguide/training.md,
docs/userguide/models.md, and docs/userguide/losses.md for full details.
import torch
from nvalchemi.training import (
CheckpointHook,
EnergyMSELoss,
FineTuningStrategy,
ForceMSELoss,
OptimizerConfig,
ValidationConfig,
create_model_spec,
default_training_fn,
)
CLI Usage
Use nvalchemi-training finetune when the user wants quick experimentation:
an offline JSON spec, a scaffold for a supported source model, a Rich intent
report, or direct CLI execution without needing full API knowledge. Use a
Python script with FineTuningStrategy when the user needs arbitrary code,
custom model construction, dynamic data routing, dynamic losses, or non-standard
orchestration. Use nvalchemi-training train init for training-from-scratch
specs. The main groups are train, finetune, schema (dump, template),
and spec (report, run). Fine-tuning sources live under finetune init:
checkpoint, mace, aimnet2, and custom.
Common flow:
nvalchemi-training finetune init mace small-0b \
--dataset data/train.zarr \
--output-dir runs/mace-ft \
--loss-dtype-policy prediction_to_target \
--out mace-ft.json
nvalchemi-training spec report mace-ft.json
nvalchemi-training spec run mace-ft.json
Use --loss-dtype-policy on finetune init or train init when the CLI
scaffold should serialize dtype alignment in strategy.loss_fn_spec. spec report renders the selected policy before execution.
Repeat --dataset to record a MultiDataset workflow. Use torchrun ... -m nvalchemi.training.cli spec run SPEC --distributed for DDP; the CLI initializes
DistributedManager, prepends DDPHook, builds the dataset(s), constructs the
strategy, and calls run(...).
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 255 lines · 112 tokens per session scan A e7a51ef341e5
nvalchemi-fine-tuning is a skill published in the GitHub repository NVIDIA/nvalchemi-toolkit (159 stars, last pushed 5d ago), licensed Apache-2.0. It adds 112 tokens to every session and 2,275 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.