tinker

Tinker (Thinking Machines Lab)

Low-level training API that abstracts distributed LLM fine-tuning without hiding the knobs. Write a simple Python script on a CPU machine; Tinker executes the GPU-heavy computation on remote clusters.

Official Sources:

• Tinker Documentation
• Tinker SDK GitHub
• Tinker Cookbook GitHub
• Blog: Announcing Tinker

Philosophy

You control	Tinker handles
Data, RL environments, loss functions	Distributed GPU training at scale
Training loop logic, hyperparameters	Hardware failures, scheduling, reliability
Evaluations, custom algorithms	Efficient execution up to 235B-param models

Not a black box — full algorithmic control. Changing models requires changing a single string.

Quick Start

# 1. Sign up: https://auth.thinkingmachines.ai/sign-up
# 2. Get API key: https://tinker-console.thinkingmachines.ai
export TINKER_API_KEY=<your-key>

# 3. Install
pip install tinker

# 4. Install cookbook (for recipes and helpers)
git clone https://github.com/thinking-machines-lab/tinker-cookbook
cd tinker-cookbook && pip install -e .

Core API (4 Primitives)

import tinker

# Create a training client
service_client = tinker.ServiceClient()
training_client = service_client.create_lora_training_client(
    base_model="Qwen/Qwen3-8B",
    rank=32,
)

# 1. FORWARD_BACKWARD — compute gradients
training_client.forward_backward(data, loss_fn="cross_entropy")

# 2. OPTIM_STEP — update weights
training_client.optim_step(tinker.AdamParams(learning_rate=1e-4))

# 3. SAVE + SAMPLE — checkpoint and generate
sampling_client = training_client.save_weights_and_get_sampling_client("step-100")
responses = sampling_client.sample(
    prompt=[tinker.TextChunk(text="Hello")],
    num_samples=4,
    sampling_params=tinker.SamplingParams(temperature=0.7, max_tokens=256),
)

# 4. SAVE_STATE — full checkpoint (weights + optimizer)
training_client.save_state("checkpoint-100")