An integration layer connecting Atropos (https://github.com/NousResearch/atropos) with the Thinking Machines Tinker API (https://thinkingmachines.ai/tinker/). This package enables seamless model training with Atropos environments from your local machine, abstracting away compute management and infrastructure concerns.

pip install -e .

First, obtain a Tinker API key from https://tinker-console.thinkingmachines.ai/keys.

Run the following commands in separate terminal windows to start a training run:

# Terminal 1: Start Atropos API
run-api

# Terminal 2: Start training
export TINKER_API_KEY="<your-key>"
python launch_training.py --config configs/default.yaml

# Terminal 3: Start environment (use built-in or any Atropos environment)
python tinker_atropos/environments/gsm8k_tinker.py serve --config configs/default.yaml

This runs a 50-step training example with Llama-3.1-8B-Instruct on the GSM8k environment.

You can use any existing Atropos environment directly with Tinker! Just point to it and pass your Tinker config:

# Use any Atropos environment with Tinker training
python /path/to/atropos/environment.py serve --config /path/to/your_tinker_config.yaml

# Terminal 1: Start Atropos API
run-api

# Terminal 2: Start Tinker training with math config
export TINKER_API_KEY="<your-key>"
python launch_training.py --config configs/math_config.yaml

# Terminal 3: Use Atropos math environment with Tinker config
python ~/atropos/environments/math_server.py serve --config configs/math_config.yaml

Atropos environments support a --config flag that loads your Tinker config (which follows the standard Atropos format with a tinker section). The environment uses:

• The env section for environment configuration
• The openai section for inference server configuration
• The tinker section is used by the trainer (ignored by the environment)

No modifications needed - any Atropos environment using managed_server is compatible!

This repo includes an example environment in tinker_atropos/environments/:

• gsm8k_tinker.py - Math reasoning with GSM8k dataset

These demonstrate integration patterns and custom reward functions.

The trainer outputs a Tinker path at the end of training:

tinker://<training_run_id>/sampler_weights/final

where <training_run_id> is the Training Run ID from https://tinker-console.thinkingmachines.ai.

To download the weights, set the TINKER_PATH in tinker_atropos/utils/download_weights.py and run:

python tinker_atropos/utils/download_weights.py

Weights will be saved to the specified location.

Both the trainer and environment support YAML configuration files to ensure parameter consistency across your training pipeline. The provided environment file (gsm8k_tinker.py) references a configuration path that should match the trainer's configuration. Update the CONFIG_PATH variable in the environment file when switching between configurations.

# Use default configuration
python launch_training.py

# Use a specific config file
python launch_training.py --config configs/quick_test.yaml

# Override parameters via CLI
python launch_training.py --config configs/default.yaml --num-steps 100 --no-wandb

• default.yaml - Standard GSM8k config (50 steps, batch_size=128, Llama-3.1-8B)
• quick_test.yaml - Quick test (10 steps, Llama-3.1-8B, no wandb)

Configs follow the Atropos format with a tinker section for Tinker-specific settings:

env:
  # Standard Atropos environment config
  group_size: 16
  batch_size: 128
  tokenizer_name: "meta-llama/Llama-3.1-8B-Instruct"
  # ... more settings

openai:
  # Standard Atropos server config
  - model_name: "meta-llama/Llama-3.1-8B-Instruct"
    base_url: "http://localhost:8001/v1"
    # ... more settings

tinker:
  # Tinker-specific training config
  lora_rank: 32
  learning_rate: 0.00004
  # ... more settings

See configs/default.yaml for the complete configuration options.

To use an existing Atropos environment config with Tinker:

1. Add a tinker section with training parameters:

   tinker:
     lora_rank: 32
     learning_rate: 0.00004
     max_token_trainer_length: 2048
     checkpoint_dir: "./checkpoints/"
     save_checkpoint_interval: 0
     wandb_project: "my-project"
     wandb_group: null
     wandb_run_name: "my-run"

2. Ensure your environment uses managed_server with stop sequences:

   async with self.server.managed_server(tokenizer=self.tokenizer) as managed:
       chat_completion = await managed.chat_completion(
           messages=messages,
           n=self.config.group_size,
           max_tokens=self.config.max_token_length,
           temperature=1.0,
           stop=[self.tokenizer.eos_token_id],  # Add stop sequences
       )

3. That's it! The config is ready to use with both Atropos and Tinker.

from tinker_atropos.config import TinkerAtroposConfig
from tinker_atropos.trainer import TinkerAtroposTrainer

# Load from YAML
config = TinkerAtroposConfig.from_yaml("configs/default.yaml")

# Access nested config values
print(f"Model: {config.env.tokenizer_name}")
print(f"LoRA rank: {config.tinker.lora_rank}")
print(f"Group size: {config.env.group_size}")

# Or use convenience properties
print(f"Model: {config.base_model}")  # -> config.env.tokenizer_name
print(f"LoRA rank: {config.lora_rank}")  # -> config.tinker.lora_rank
print(f"Learning rate: {config.learning_rate}")  # -> config.tinker.learning_rate

# Use defaults (creates default config)
config = TinkerAtroposConfig()

# Initialize and run trainer
trainer = TinkerAtroposTrainer(config=config)
await trainer.run()

python -m pytest tinker_atropos/tests/ -v

The Tinker Rate Card and available models are listed here: https://tinker-console.thinkingmachines.ai/rate-card

• Atropos: https://github.com/NousResearch/atropos
• Tinker: https://tinker-docs.thinkingmachines.ai