synthetic-sciences/openscience is an AI workbench that carries out scientific research by reading papers, forming hypotheses, writing and running code, conducting experiments, analyzing results, and preparing reports. Researchers use it for work in machine learning, biology, physics, and chemistry with remote or local models. Catalogue add-ons extend its scientific workflows through skills and instructions.
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 agentmods add skills/synthetic-sciences/openscience/tinkernpx skills add synthetic-sciences/openscience --skill tinkergit clone --depth 1 https://github.com/synthetic-sciences/openscienceWrote 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/synthetic-sciences/openscience/tinker)<a href="https://agentmods.dev/skills/synthetic-sciences/openscience/tinker"><img src="https://agentmods.dev/badge/skills/synthetic-sciences/openscience/tinker.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00062 | $0.04372 |
| Opus 5 | $0.00031 | $0.02186 |
| Sonnet 5 | $0.00012 | $0.00874 |
| Haiku 4.5 | $0.00006 | $0.00437 |
Grade A, and why
tinker-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 yesterday.
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 — 468 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tinker API - Cloud LLM Fine-Tuning
Expert guidance for fine-tuning large language models using Tinker's managed cloud training API. Tinker handles GPU allocation, model hosting, and distributed training — you write the training logic, Tinker runs it on cloud infrastructure.
When to Use This Skill
Use Tinker when you need to:
- Fine-tune models up to 235B parameters without managing GPU infrastructure
- Run LoRA training on Qwen, Llama, DeepSeek, or GPT-OSS models
- Train vision-language models (Qwen3-VL)
- Implement custom RL loops (GRPO, PPO, importance sampling) on cloud GPUs
- Iterate quickly with a training API that handles hardware provisioning
Do NOT use Tinker when:
- You need full fine-tuning (not LoRA) — Tinker only supports LoRA
- You need to train custom architectures — Tinker supports specific model families
- You want to use your own GPUs — use Axolotl, Unsloth, or LLaMA-Factory instead
- You need offline/air-gapped training
Tinker vs Alternatives:
| Need | Use |
|---|---|
| Managed cloud LoRA training | Tinker |
| Local GPU fine-tuning | Axolotl, Unsloth, LLaMA-Factory |
| Full parameter fine-tuning | DeepSpeed + Transformers |
| RLHF with TRL locally | TRL + GRPO skill |
| Quantized training | Unsloth, bitsandbytes |
Quick Reference
| Topic | Reference |
|---|---|
| Setup & Core Concepts | Getting Started |
| API Classes & Types | API Reference |
| Supervised Learning | Supervised Learning |
| RL Training & Environments | Reinforcement Learning |
| DPO, RLHF & Distillation | DPO & Preference Learning |
| Loss Functions | Loss Functions |
| Chat Templates | Rendering |
| Models & LoRA | Models & LoRA |
| Evaluations | Evaluations |
| Example Scripts | Recipes |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/api-reference.md 3.9 KB
- references/dpo-and-preference.md 6.0 KB
- references/evaluations.md 5.3 KB
- references/getting-started.md 4.7 KB
- references/loss-functions.md 4.5 KB
- references/models-and-lora.md 4.9 KB
- references/recipes.md 10 KB
- references/reinforcement-learning.md 11 KB
- references/rendering.md 6.1 KB
- references/supervised-learning.md 7.9 KB
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.
- yesterday First seen · 468 lines · 62 tokens per session scan A dc6f6fa33901
tinker-fine-tuning is a skill published in the GitHub repository synthetic-sciences/openscience (3,473 stars, last pushed today), licensed Apache-2.0. It adds 62 tokens to every session and 4,372 once invoked, about $0.0003 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 2026-09-03.
Other skills, from other repositories
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
model_finetuning
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.
fine-tuning-with-trl
Fine-tune LLMs using reinforcement learning with TRL - SFT for instruction tuning, DPO for preference alignment, PPO/GRPO for reward optimization, and reward model training. Use when need RLHF, align model with preferences, or train from human feedback. Works with HuggingFace Transformers.