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tinker-training-cost
GitHub用于计算Tinker微调任务的训练成本。通过加载对应模型的分词器统计数据集Token数，结合最新定价表估算费用，支持多种主流模型及JSONL格式输入。
Trigger Scenarios
估算Tinker LLM训练成本
统计数据集中的Token数量
比较不同模型的训练价格
Install
npx skills add synthetic-sciences/openscience --skill tinker-training-cost -g -y
SKILL.md
Frontmatter
{
"name": "tinker-training-cost",
"tags": [
"Tinker",
"Training Cost",
"Token Counting",
"Fine-Tuning",
"Pricing"
],
"author": "Synthetic Sciences",
"license": "MIT",
"version": "1.0.0",
"category": "cloud-compute",
"description": "Calculates training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.",
"dependencies": [
"transformers>=4.40.0"
]
}
Tinker Training Cost Calculator
Calculate training costs for Tinker fine-tuning jobs by tokenizing your dataset with the correct model tokenizer and applying current pricing.
Quick Start
Use the bundled script to calculate training costs:
# List available models and pricing
python scripts/calculate_cost.py --list-models
# Calculate cost for a JSONL dataset
python scripts/calculate_cost.py training_data.jsonl --model Qwen3-8B --epochs 3
# Output as JSON
python scripts/calculate_cost.py training_data.jsonl --model Llama-3.1-70B --json
The script:
- Loads the correct tokenizer for the selected model
- Counts tokens in your JSONL file (supports chat, text, and instruction formats)
- Calculates the estimated training cost
Cost Formula
Training Cost = (total_tokens × epochs × train_price_per_million) / 1_000_000
Where:
total_tokens= tokens in your training dataset (from tokenization)epochs= number of training passes (default: 3)train_price_per_million= model-specific training rate from pricing table
Tinker Pricing
All prices as of January 5, 2026 Source: https://thinkingmachines.ai/tinker/
All prices are in USD per million tokens.
| Category | Description |
|---|---|
| Prefill | Processing input context (inference) |
| Sample | Generating output tokens (inference) |
| Train | Training/fine-tuning tokens |
Qwen Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Qwen3-4B-Instruct-2507 | $0.07 | $0.22 | $0.22 |
| Qwen3-8B | $0.13 | $0.40 | $0.40 |
| Qwen3-30B-A3B | $0.12 | $0.30 | $0.36 |
| Qwen3-VL-30B-A3B-Instruct | $0.18 | $0.44 | $0.53 |
| Qwen3-32B | $0.49 | $1.47 | $1.47 |
| Qwen3-235B-Instruct-2507 | $0.68 | $1.70 | $2.04 |
| Qwen3-VL-235B-A22B-Instruct | $1.02 | $2.56 | $3.07 |
Llama Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Llama-3.2-1B | $0.03 | $0.09 | $0.09 |
| Llama-3.2-3B | $0.06 | $0.18 | $0.18 |
| Llama-3.1-8B | $0.13 | $0.40 | $0.40 |
| Llama-3.1-70B | $1.05 | $3.16 | $3.16 |
DeepSeek Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| DeepSeek-V3.1 | $1.13 | $2.81 | $3.38 |
GPT-OSS Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| GPT-OSS-120B | $0.18 | $0.44 | $0.52 |
| GPT-OSS-20B | $0.12 | $0.30 | $0.36 |
Moonshot Models
| Model | Prefill | Sample | Train |
|---|---|---|---|
| Kimi-K2-Thinking | $0.98 | $2.44 | $2.93 |
Model-to-Tokenizer Mapping
Use the correct HuggingFace tokenizer for accurate token counting:
| Model | HuggingFace Tokenizer |
|---|---|
| Qwen3-4B-Instruct-2507 | Qwen/Qwen3-4B |
| Qwen3-8B | Qwen/Qwen3-8B |
| Qwen3-30B-A3B | Qwen/Qwen3-30B-A3B |
| Qwen3-32B | Qwen/Qwen3-32B |
| Qwen3-235B-Instruct-2507 | Qwen/Qwen3-235B-A22B-Instruct |
| Qwen3-VL-* | Qwen/Qwen2.5-VL-7B-Instruct (shared VL tokenizer) |
| Llama-3.2-1B | meta-llama/Llama-3.2-1B-Instruct |
| Llama-3.2-3B | meta-llama/Llama-3.2-3B-Instruct |
| Llama-3.1-8B | meta-llama/Llama-3.1-8B-Instruct |
| Llama-3.1-70B | meta-llama/Llama-3.1-70B-Instruct |
| DeepSeek-V3.1 | deepseek-ai/DeepSeek-V3 |
| GPT-OSS-* | Qwen/Qwen3-8B (compatible tokenizer) |
| Kimi-K2-Thinking | moonshotai/Kimi-K2-Instruct |
Tokenization
The bundled scripts/calculate_cost.py handles tokenization automatically. For custom use:
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-8B", trust_remote_code=True)
token_count = len(tokenizer.encode("Your training text here"))
Supported JSONL Formats
The script handles these training data formats:
Chat format (recommended):
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]}
Text format:
{"text": "Your training text here"}
Instruction format (Alpaca-style):
{"instruction": "...", "input": "...", "output": "..."}
Quick Cost Examples
Example 1: Qwen3-8B on 1M tokens, 3 epochs
Dataset tokens: 1,000,000
Training tokens: 1,000,000 × 3 = 3,000,000
Cost: 3.0M × $0.40/M = $1.20
Example 2: Llama-3.1-70B on 5M tokens, 2 epochs
Dataset tokens: 5,000,000
Training tokens: 5,000,000 × 2 = 10,000,000
Cost: 10.0M × $3.16/M = $31.60
Example 3: Qwen3-235B on 2M tokens, 4 epochs
Dataset tokens: 2,000,000
Training tokens: 2,000,000 × 4 = 8,000,000
Cost: 8.0M × $2.04/M = $16.32
Important Notes
- LoRA Fine-Tuning: Tinker uses Low-Rank Adaptation (LoRA), not full fine-tuning
- Token Counting: Always use the model's native tokenizer for accurate counts - different tokenizers produce different token counts for the same text
- Vision Models: VL models have higher costs due to image processing overhead
- trust_remote_code: Required for some tokenizers (Qwen, DeepSeek)
Version History
- e9844a4 Current 2026-07-11 17:22
Dependencies
-
required
transformers>=4.40.0
