Guide for the weight lifecycle — downloading trained weights from Tinker, merging LoRA adapters into HuggingFace models, and publishing to HuggingFace Hub. Use when the user asks about exporting, downloading, merging, or publishing trained model weights.
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The tinker_cookbook.weights subpackage provides a standard pipeline for trained weight management: download → build → publish.
Read these for details:
tinker_cookbook/weights/__init__.py — API overview and workflow exampletinker_cookbook/weights/_download.py — Download implementationtinker_cookbook/weights/_export.py — LoRA merge implementationtinker_cookbook/weights/_publish.py — HuggingFace Hub publishdocs/download-weights.mdx — Download guidedocs/publish-weights.mdx — Publishing guidedocs/save-load.mdx — Checkpointing (save_weights_for_sampler vs save_state)from tinker_cookbook import weights
# Step 1: Download adapter from Tinker
adapter_dir = weights.download(
tinker_path="tinker://run-id/sampler_weights/final",
output_dir="./adapter",
)
# Step 2: Merge LoRA adapter into base model
weights.build_hf_model(
base_model="Qwen/Qwen3.5-35B-A3B",
adapter_path=adapter_dir,
output_path="./model",
dtype="bfloat16", # or "float16", "float32"
)
# Step 3: Publish to HuggingFace Hub
url = weights.publish_to_hf_hub(
model_path="./model",
repo_id="user/my-finetuned-model",
private=True,
)
weights.download()Downloads and extracts a checkpoint archive from Tinker.
adapter_dir = weights.download(
tinker_path="tinker://run-id/sampler_weights/final", # Tinker checkpoint path
output_dir="./adapter", # Local directory to extract to
base_url=None, # Optional custom Tinker API URL
)
# Returns: path to extracted directory
weights.build_hf_model()Merges a LoRA adapter into a base model, producing a full HuggingFace model.
weights.build_hf_model(
base_model="Qwen/Qwen3-8B", # HF model name or local path
adapter_path="./adapter", # Directory with adapter_model.safetensors
output_path="./model", # Where to save merged model
dtype="bfloat16", # Weight dtype
trust_remote_code=None, # Override HF_TRUST_REMOTE_CODE
)
weights.publish_to_hf_hub()Pushes a local model directory to HuggingFace Hub.
url = weights.publish_to_hf_hub(
model_path="./model", # Local model directory
repo_id="user/my-finetuned-model", # HF repo ID
private=True, # Private repo
token=None, # HF token (uses HF_TOKEN env var if None)
)
# Returns: URL to published repo
weights.build_lora_adapter() (not yet implemented)Convert Tinker LoRA adapter to standard format for vLLM/SGLang. Currently raises NotImplementedError — use build_hf_model() instead.
During training, there are two types of checkpoints:
save_state() — Full state (weights + optimizer). Used for resuming training.save_weights_for_sampler() — Weights only. Used for sampling and export.The weights.download() function works with sampler weights (save_weights_for_sampler checkpoints).
download() expects a tinker:// path from save_weights_for_sampler, not save_statebuild_hf_model() requires the base model to be downloadable from HuggingFaceHF_TOKEN environment variable for private models and publishingdtype="bfloat16" is recommended for most modelsNo reviews yet. Tell others what you thought.
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Guide for checkpointing — saving, loading, and resuming training with CheckpointRecord. Use when the user asks about saving weights, resuming training, checkpoint management, or the checkpoint lifecycle.
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Guide for testing conventions and CI pipelines — unit tests, integration smoke tests, pytest markers, and GitHub Actions workflows. Use when the user asks about testing, CI, running tests, or adding tests for a recipe.
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Guide for using completers — TokenCompleter and MessageCompleter for text generation during RL rollouts and evaluation. Use when the user asks about generating text, completing messages, or using completers in RL environments.
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Guide for contributing to the tinker-cookbook repo — development setup, code style, type checking, PR process, and design conventions. Use when the user asks about how to contribute, set up the dev environment, code style, or project conventions.
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Guide for dataset construction — SupervisedDatasetBuilder, RLDatasetBuilder, ChatDatasetBuilder, and custom dataset creation from JSONL, HuggingFace, or conversation data. Use when the user asks about datasets, data loading, data preparation, or custom data formats.
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Set up and run knowledge distillation (on-policy, off-policy, or multi-teacher) from a teacher model to a student model using the Tinker API. Use when the user wants to distill knowledge, compress models, or train a student from a teacher.
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