ml-intern

ultra-ml-intern: ML engineering intern for Claude Code

ultra-instinct ML engineering intern for Claude Code. Reads papers, audits datasets, ships SFT/DPO/LoRA runs to Hugging Face.

ultra-ml-intern is a Claude Code plugin that gives Claude the workflow of an ML engineering intern. It researches ML papers, audits Hugging Face datasets, designs fine-tuning recipes (SFT, DPO, GRPO, LoRA, QLoRA, RLHF), and submits training jobs to HF Jobs with Trackio monitoring.

The procedural knowledge comes from huggingface/ml-intern, HF's standalone Python harness around the Claude API. This repo wires the same intelligence into Claude Code, Anthropic's official agentic harness for Claude. Same model, a more capable loop, and you bring your own Claude (Max subscription or API key) instead of paying for a second harness on top.

Works in any Claude Code surface: terminal CLI, IDE extensions, and the web app.

Install

# In any Claude Code session:
/plugin marketplace add infiniV/ultra-ml-intern
/plugin install ml-intern@ultra-ml-intern

Restart Claude Code, then verify with /plugin and /agents. The slash commands (/ml-intern, /ml-research, …) keep their short names; the ultra- prefix is just the package wrapper.

What you get:

• 1 skill: ml-intern (the workflow)
• 5 slash commands: /ml-intern, /ml-research, /ml-audit, /ml-preflight, /ml-train
• 3 subagents: ml-paper-researcher, dataset-auditor, training-job-architect
• 1 MCP server: Hugging Face (activates when HF_TOKEN is set)

Quickstart

> "fine-tune Qwen3-0.5B for math reasoning"

The skill activates automatically and walks the 6-step research-driven workflow:

1. Find the landmark paper for the task
2. Crawl the citation graph for recent SOTA
3. Read methodology sections (3, 4, 5) and extract the recipe
4. Validate the dataset and base model exist on Hub
5. Write a training script grounded in current TRL APIs
6. Pre-flight check → smoke test → full hf jobs run with Trackio monitoring

What it does

You ask	It does
"fine-tune X for Y"	Full pipeline: literature review → dataset audit → training-job design → smoke test → full run
"what's the best recipe for X"	Dispatches the ml-paper-researcher subagent; returns recipe + citations
"audit dataset Y"	Dispatches the dataset-auditor; returns schema, anomalies, GO/NO-GO verdict
"preflight train.py"	Catches missing push_to_hub, default 30m timeout, bf16 on T4, missing flash-attn install, before you spend cluster hours
"submit hf jobs run"	Walks pre-flight → cost estimate → smoke test → full submission → Trackio dashboard URL

Commands

Command	What it does
/ml-intern	The full pipeline: research → audit → train → ship
/ml-research	Literature review only: landmark paper, citation graph, extracted recipe
/ml-audit	Dataset audit only: schema, samples, anomalies, training-method recommendation
/ml-preflight	Sanity-check a training script before submission
/ml-train	Submit a training job. Local-first when a GPU is available, HF Jobs when not

Subagents

Subagent	Role
ml-paper-researcher	Crawls arXiv + cites the landmark paper, extracts the methodology section into a recipe
dataset-auditor	Inspects HF datasets: schema, sample rows, distribution checks, anomaly flagging
training-job-architect	Writes the TRL/Transformers training script + the hf jobs run command sized to your hardware

Training recipes supported

The plugin recognizes and writes scripts for:

• SFT (Supervised Fine-Tuning) for single-turn and multi-turn chat
• DPO (Direct Preference Optimization) on pairwise preference data
• GRPO (Group Relative Policy Optimization) for reasoning tasks, DeepSeek-style
• LoRA and QLoRA for parameter-efficient fine-tuning, including 4-bit quantization
• RLHF with the full reward-model plus PPO pipeline
• Continued pretraining for domain adaptation on raw text

All grounded in the current TRL API. The ml-paper-researcher reads the actual library source, not its training data, before writing imports.

Hardware sizing built in

The plugin knows the HF Jobs flavors (t4-small through a100x8) and picks one to fit your model: