How do I install the PDF To Tinker Training skill?

Run npx skillmds@latest add timsonner/pdf-to-tinker-training in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is PDF To Tinker Training safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with PDF To Tinker Training?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is PDF To Tinker Training free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published PDF To Tinker Training?

timsonner (@timsonner) published this skill. Their other Agent Skills are listed on their SkillMD profile.

PDF To Tinker Training by timsonner

Name: PDF To Tinker Training
Author: timsonner

File contents

Trigger

User asks to create fine-tuning data from PDF for thinkingmachines/Inkling using Tinker.

Requirements

Zero-Binary / Zero-Batch enforcement: execute via sys.executable, no compiled wrappers (see Hermes AGENTS.md: plugins/skills extend at edges, core stays pure Python).
Tokenizer: training_client.get_tokenizer() (provided by Tinker SDK; requires full tinker env; tml_tokenizers is SDK-internal, not on PyPI).
Tinker SDK: pip install tinker (v0.23.0 verified). types.Datum, types.ModelInput.from_ints, cross_entropy loss.
Tinker base_model IDs verified from docs (tinker/models/): thinkingmachines/Inkling (64K), thinkingmachines/Inkling:peft:262144 (256K). SDK 400 with thinkingmachines/inkling indicates case-sensitive ID; use exact docs string.

Tinker Datum format (verified from tinker-docs quickstart)

datum = types.Datum(
    model_input=types.ModelInput.from_ints(tokens=input_tokens),
    loss_fn_inputs=dict(
        weights=weights,           # 0 = prompt, 1 = completion
        target_tokens=target_tokens  # shift by 1 from input
    )
)

Used with training_client.forward_backward_async(data=[datum], loss_fn="cross_entropy") via create_lora_training_client.

Chunking strategy

By headings when detectable; fall back to page-based with configurable overlap.
Each chunk → instruction (heading/summary) + response (body text).
Command/tool extraction: many books include terminal commands in images (screenshots), not text. For text commands, scan chunks for backticks / $ patterns. For image-embedded commands: extract images via fitz page.get_images(), apply OCR (e.g., tesseract or vision model) to recover terminal text. Extracted commands become supervised pairs (instruction = "How to use ?", response = usage + context).

Final Workflow (verified — generic, no hard-coded paths)

Extract any PDF (--pdf <path>) → filter → chunk
Generate SL pairs → <output_path> (.jsonl, default: <pdf_basename>_training_full.jsonl)
Train: scripts/train_lora.py (LoRA + cross_entropy) with create_lora_training_client
Format: tinker_datum_supervised_lora (loss: cross_entropy, weights: ..., LoRA)
Image commands: fitz.get_images() + OCR

Verification

Run: python chunk_pdf.py --pdf <path>

---name: pdf-to-tinker-trainingdescription: Split PDF books (variable formats) into supervised Tinker Datum pairs for Inkling LoRA fine-tuning via pure Python (sys.executable, Zero-Binary).---## TriggerUser asks to create fine-tuning data from PDF for thinkingmachines/Inkling using Tinker.## Requirements- Zero-Binary / Zero-Batch enforcement: execute via `sys.executable`, no compiled wrappers (see Hermes `AGENTS.md`: plugins/skills extend at edges, core stays pure Python).- Tokenizer: `training_client.get_tokenizer()` (provided by Tinker SDK; requires full `tinker` env; `tml_tokenizers` is SDK-internal, not on PyPI).- Tinker SDK: `pip install tinker` (v0.23.0 verified). `types.Datum`, `types.ModelInput.from_ints`, `cross_entropy` loss.- Tinker `base_model` IDs verified from docs (`tinker/models/`): `thinkingmachines/Inkling` (64K), `thinkingmachines/Inkling:peft:262144` (256K). SDK `400` with `thinkingmachines/inkling` indicates case-sensitive ID; use exact docs string.## Tinker Datum format (verified from tinker-docs quickstart)```datum = types.Datum( model_input=types.ModelInput.from_ints(tokens=input_tokens), loss_fn_inputs=dict( weights=weights, # 0 = prompt, 1 = completion target_tokens=target_tokens # shift by 1 from input ))```Used with `training_client.forward_backward_async(data=[datum], loss_fn="cross_entropy")` via `create_lora_training_client`.## Chunking strategy- By headings when detectable; fall back to page-based with configurable overlap.- Each chunk → instruction (heading/summary) + response (body text).- **Command/tool extraction:** many books include terminal commands in images (screenshots), not text. For text commands, scan chunks for backticks / `$ ` patterns. For image-embedded commands: extract images via `fitz` page.get_images(), apply OCR (e.g., `tesseract` or vision model) to recover terminal text. Extracted commands become supervised pairs (instruction = "How to use <command>?", response = usage + context).## Final Workflow (verified — generic, no hard-coded paths)1. Extract any PDF (`--pdf <path>`) → filter → chunk2. Generate SL pairs → `<output_path>` (.jsonl, default: `<pdf_basename>_training_full.jsonl`)3. Train: `scripts/train_lora.py` (LoRA + `cross_entropy`) with `create_lora_training_client`4. Format: `tinker_datum_supervised_lora` (`loss: cross_entropy`, `weights: ...`, LoRA)5. Image commands: `fitz.get_images()` + OCR## VerificationRun: `python chunk_pdf.py --pdf <path>`

Frequently asked questions

PDF To Tinker Training