Provides guidance for fine-tuning LLMs using the Tinker cloud training API from Thinking Machines Lab. Use when running supervised fine-tuning, reinforcement learning (GRPO/PPO), or LoRA training on cloud GPUs via Tinker's managed infrastructure instead of local compute.
Provides guidance for fine-tuning LLMs using the Tinker cloud training API from Thinking Machines Lab. Use when running supervised fine-tuning, reinforcement learning (GRPO/PPO), or LoRA training on cloud GPUs via Tinker's managed infrastructure instead of local compute.
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
pip install tinker tinker-cookbook
# TINKER_API_KEY must be set — connect Tinker in the OpenScience dashboard to sync your API key.# Verify: [ -n "$TINKER_API_KEY" ] && echo "set" || echo "not set"
Workflow 1: Supervised Fine-Tuning (Cookbook)
Use this for standard SFT with JSONL or HuggingFace datasets.
Checklist
Prepare data in JSONL chat format ({"messages": [...]})
Choose base model (see model table below)
Set hyperparameters (LR, batch size, epochs)
Run training via Cookbook
Monitor metrics (train_mean_nll, test/nll)
Save and deploy weights
Implementation
import json
import chz
import asyncio
from tinker_cookbook.supervised import train
from tinker_cookbook.supervised.types import ChatDatasetBuilderCommonConfig
from tinker_cookbook.supervised.data import FromConversationFileBuilder
from tinker_cookbook.renderers import TrainOnWhat
from tinker_cookbook.model_info import get_recommended_renderer_name
from tinker_cookbook.hyperparam_utils import get_lr
from tinker_cookbook.tokenizer_utils import get_tokenizer
model_name = "Qwen/Qwen3-30B-A3B"
renderer_name = get_recommended_renderer_name(model_name)
num_epochs = 3
data_file = "training_data.jsonl"
common_config = ChatDatasetBuilderCommonConfig(
model_name_for_tokenizer=model_name,
renderer_name=renderer_name,
max_length=2048,
batch_size=128,
train_on_what=TrainOnWhat.ALL_ASSISTANT_MESSAGES,
)
dataset_builder = FromConversationFileBuilder(
common_config=common_config,
file_path=data_file,
)
blueprint = chz.Blueprint(train.Config).apply({
"log_path": "/tmp/sft-run",
"model_name": model_name,
"dataset_builder": dataset_builder,
"learning_rate": get_lr(model_name),
"lr_schedule": "linear",
"num_epochs": num_epochs,
"lora_rank": 32,
})
config = blueprint.make()
asyncio.run(train.main(config))
# --- Exact usage reporting (auto-captured by CLI) ---
tokenizer = get_tokenizer(model_name)
total_tokens = 0withopen(data_file) as f:
for line in f:
row = json.loads(line)
text = " ".join(m.get("content", "") for m in row.get("messages", []))
total_tokens += len(tokenizer.encode(text))
total_tokens *= num_epochs
print(f'\n[OPENSCIENCE_USAGE] {json.dumps({"service": "tinker", "event_type": "training", "model": model_name, "tokens_used": total_tokens})}')
Data Format
JSONL with chat messages (one per line):
{"messages":[{"role":"user","content":"Translate to French: hello"},{"role":"assistant","content":"bonjour"}]}
TrainOnWhat Options
Option
When to Use
ALL_ASSISTANT_MESSAGES
Standard SFT, multi-turn conversations
LAST_ASSISTANT_MESSAGE
Classification, chain-of-thought where only final answer matters
Workflow 2: Reinforcement Learning (GRPO-style)
Use this for training with reward functions — math reasoning, format compliance, verifiable tasks.
Checklist
Define reward function(s) that return float scores
Choose group size (16 recommended)
Set up sampling → reward → training loop
Monitor correct, format, reward/total, KL divergence
Keep KL below 0.01 for stable training
Implementation (Cookbook)
import json
import asyncio
import chz
from tinker_cookbook.rl import train
from tinker_cookbook.recipes.math_rl.math_env import Gsm8kDatasetBuilder
from tinker_cookbook import model_info
model_name = "meta-llama/Llama-3.1-8B"
renderer_name = model_info.get_recommended_renderer_name(model_name)
batch_size = 128
group_size = 16
max_tokens = 256
builder = Gsm8kDatasetBuilder(
batch_size=batch_size,
group_size=group_size,
renderer_name=renderer_name,
model_name_for_tokenizer=model_name,
)
blueprint = chz.Blueprint(train.Config).apply({
"model_name": model_name,
"log_path": "/tmp/rl-run",
"dataset_builder": builder,
"learning_rate": 4e-5,
"max_tokens": max_tokens,
})
config = blueprint.make()
asyncio.run(train.main(config))
# --- Exact usage reporting (auto-captured by CLI) ---# For RL: estimate from batch_size × group_size × max_tokens × num_batches# The exact count comes from the training loop — check /tmp/rl-run for logsimport glob, os
log_files = sorted(glob.glob("/tmp/rl-run/metrics*.json"))
total_tokens = 0for lf in log_files:
withopen(lf) as f:
for line in f:
m = json.loads(line)
total_tokens += m.get("num_tokens", 0)
if total_tokens == 0:
# Fallback estimate: batch_size × group_size × max_tokens × num_batches
total_tokens = batch_size * group_size * max_tokens * 100print(f'\n[OPENSCIENCE_USAGE] {json.dumps({"service": "tinker", "event_type": "training", "model": model_name, "tokens_used": total_tokens})}')
Custom RL with Low-Level API
For full control over sampling, reward computation, and advantage centering:
import json
import tinker
from tinker import types
from tinker.types.tensor_data import TensorData
import torch
model_name = "meta-llama/Llama-3.1-8B"
service_client = tinker.ServiceClient()
training_client = service_client.create_lora_training_client(
base_model=model_name, rank=32
)
total_tokens = 0# Track exact tokens for billingfor batch_idx, batch_rows inenumerate(dataset):
path = training_client.save_weights_for_sampler(name=f"{batch_idx:06d}").result().path
sampling_client = service_client.create_sampling_client(model_path=path)
datums = []
for question, answer in batch_rows:
prompt = renderer.build_generation_prompt([{"role": "user", "content": question}])
prompt_tokens = prompt.to_ints()
result = sampling_client.sample(
prompt=prompt, num_samples=16,
sampling_params=types.SamplingParams(max_tokens=256, stop=renderer.get_stop_sequences()),
).result()
rewards = [compute_reward(seq, answer) for seq in result.sequences]
mean_reward = sum(rewards) / len(rewards)
advantages = [r - mean_reward for r in rewards]
ifall(a == 0for a in advantages):
continuefor seq, advantage inzip(result.sequences, advantages):
tokens = prompt_tokens + seq.tokens
ob_len = len(prompt_tokens) - 1
datum = types.Datum(
model_input=types.ModelInput.from_ints(tokens=tokens[:-1]),
loss_fn_inputs={
"target_tokens": TensorData.from_torch(torch.tensor(tokens[1:])),
"logprobs": TensorData.from_torch(torch.tensor([0.0]*ob_len + list(seq.logprobs))),
"advantages": TensorData.from_torch(torch.tensor([0.0]*ob_len + [advantage]*(len(tokens)-1-ob_len))),
},
)
datums.append(datum)
# Track exact token count from datums
total_tokens += sum(d.model_input.length() for d in datums)
fwd_bwd = training_client.forward_backward(datums, loss_fn="importance_sampling")
optim = training_client.optim_step(types.AdamParams(learning_rate=4e-5))
fwd_bwd.result(); optim.result()
# --- Exact usage reporting (auto-captured by CLI) ---print(f'\n[OPENSCIENCE_USAGE] {json.dumps({"service": "tinker", "event_type": "training", "model": model_name, "tokens_used": total_tokens})}')
Available RL Loss Functions
Loss
Use Case
importance_sampling
Standard policy gradient with off-policy correction
ppo
Clipped surrogate objective (PPO)
cispo
Clipped importance sampling PO
dro
Direct reward optimization with quadratic penalty
Available Models
Model
Type
Architecture
Train $/M tokens
Qwen3-4B-Instruct-2507
Instruction
Dense Compact
$0.22
Qwen3-8B
Hybrid
Dense Small
$0.40
Qwen3-30B-A3B
Hybrid
MoE Medium
$0.36
Qwen3-32B
Hybrid
Dense Medium
$1.47
Qwen3-VL-30B-A3B-Instruct
Vision
MoE Medium
$0.53
Llama-3.2-1B
Base
Dense Compact
$0.09
Llama-3.1-8B
Base
Dense Small
$0.40
Llama-3.1-70B
Base
Dense Large
$3.16
DeepSeek-V3.1
Hybrid
MoE Large
$3.38
GPT-OSS-120B
Reasoning
MoE Medium
$0.52
Model Selection Tips:
Cost efficiency: MoE models (Qwen3-30B-A3B at $0.36/M)
Experimentation: Start with 8B models
Vision tasks: Qwen3-VL-30B-A3B-Instruct
Reasoning: Hybrid or Reasoning models with chain-of-thought
See Evaluations for custom evaluators and LLM-as-judge.
Cost Estimation & Usage Tracking
Pre-Training Cost Estimation
ALWAYS estimate cost before starting Tinker training. Load the tinker-training-cost skill and use its pricing tables or calculate manually:
Training Cost = (total_tokens × epochs × train_price_per_million) / 1,000,000
Present the cost estimate to the user for approval before starting training.
Automatic Usage Reporting (Ground Truth)
CRITICAL: All training scripts MUST print a [OPENSCIENCE_USAGE] line at the end. The CLI automatically captures this and reports exact billing to the dashboard.
# Add this at the END of every training script:import json
print(f'\n[OPENSCIENCE_USAGE] {json.dumps({"service": "tinker", "event_type": "training", "model": model_name, "tokens_used": total_tokens})}')
How token counting works per workflow:
Cookbook SFT: Tokenize dataset with get_tokenizer(model_name), multiply by num_epochs
Cookbook RL: Parse training logs for num_tokens, or estimate from batch_size × group_size × max_tokens × batches
Low-level API: Sum datum.model_input.length() across all forward_backward() calls
The CLI bash tool scans output for [OPENSCIENCE_USAGE] markers and auto-reports to the dashboard — no manual reporting needed.
Common Issues
Problem
Solution
TINKER_API_KEY not set
export TINKER_API_KEY=your_key or check OpenScience credential sync
KL divergence > 0.01
Reduce learning rate, check group size
OOM on dataset loading
Use StreamingSupervisedDatasetFromHFDataset for large datasets
Reward stuck at 0
Debug reward function independently, check answer extraction
All advantages = 0
Increase group size, ensure reward variance across completions
Wrong tokenizer
Use model-specific tokenizer (see Models & LoRA reference)
Unknown learning rate schedule
Only "linear" and "constant" are supported; "cosine" does NOT work
Python 3.14 pydantic errors
Tinker requires Python 3.10-3.13; pydantic v1 is incompatible with 3.14+
Only 1 step per epoch
batch_size too large for dataset size; aim for 100+ steps per epoch
