tinker

Original：🇺🇸 English

Translated

Fine-tune LLMs using the Tinker API. Covers supervised fine-tuning, reinforcement learning, LoRA training, vision-language models, and both high-level Cookbook patterns and low-level API usage.

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Sourcesundial-org/skills

Added on2026-03-29

NPX Install

npx skill4agent add sundial-org/skills tinker

SKILL.md Content

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Tinker API - LLM Fine-Tuning

Overview

Tinker is a training API for large language models from Thinking Machines Lab. It provides:

Supervised Fine-Tuning (SFT): Train models on instruction/completion pairs
Reinforcement Learning (RL): PPO and policy gradient losses; cookbook patterns include GRPO-like group rollouts/advantage centering
Vision-Language Models: VLM support via Qwen3-VL
LoRA Training: Efficient parameter-efficient fine-tuning

Two abstraction levels:

Tinker Cookbook: High-level patterns with automatic training loops
Low-Level API: Manual control for custom training logic

Quick Reference

Topic	Reference
Setup & Core Concepts	Getting Started
API Classes & Types	API Reference
Supervised Learning	Supervised Learning
RL Training	Reinforcement Learning
Loss Functions	Loss Functions
Chat Templates	Rendering
Models & LoRA	Models & LoRA
Example Scripts	Recipes

Installation

bash

pip install tinker tinker-cookbook
export TINKER_API_KEY=your_api_key_here

Minimal Example

python

import numpy as np
import tinker
from tinker import types

# Create clients
service_client = tinker.ServiceClient()
training_client = service_client.create_lora_training_client(
    base_model="Qwen/Qwen3-30B-A3B", rank=32
)
tokenizer = training_client.get_tokenizer()

# Prepare data
prompt = "English: hello\nPig Latin:"
completion = " ello-hay\n"
prompt_tokens = tokenizer.encode(prompt, add_special_tokens=True)
completion_tokens = tokenizer.encode(completion, add_special_tokens=False)
tokens = prompt_tokens + completion_tokens
weights = np.array(([0] * len(prompt_tokens)) + ([1] * len(completion_tokens)), dtype=np.float32)
target_tokens = np.array(tokens[1:], dtype=np.int64)

datum = types.Datum(
    model_input=types.ModelInput.from_ints(tokens=tokens[:-1]),
    loss_fn_inputs={
        "target_tokens": target_tokens,
        "weights": weights[1:]
    }
)

# Train
fwdbwd = training_client.forward_backward([datum], "cross_entropy")
optim = training_client.optim_step(types.AdamParams(learning_rate=1e-4))
fwdbwd.result(); optim.result()

# Sample
sampling_client = training_client.save_weights_and_get_sampling_client(name="v1")
result = sampling_client.sample(
    prompt=types.ModelInput.from_ints(tokens=tokenizer.encode("English: world\nPig Latin:", add_special_tokens=True)),
    sampling_params=types.SamplingParams(max_tokens=20),
    num_samples=1
).result()
print(tokenizer.decode(result.sequences[0].tokens))

Common Imports