mirror of
https://github.com/SakanaAI/doc-to-lora.git
synced 2026-07-23 17:01:04 +02:00
66 lines
2.6 KiB
Markdown
66 lines
2.6 KiB
Markdown
<div align="center">
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<h1>Doc-to-LoRA</h1>
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<br>
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<img height="500px" src="assets/cover.png" />
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</div>
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---
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## 🚀 Python API Usage
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```python
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# caveat: this interface only supports non-batched inputs
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# for batched inference please see `src/ctx_to_lora/modeling/hypernet.py`
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import torch
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from ctx_to_lora.model_loading import get_tokenizer
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from ctx_to_lora.modeling.hypernet import ModulatedPretrainedModel
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# model loading
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checkpoint_path = ...
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state_dict = torch.load(checkpoint_path, weights_only=False)
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model = ModulatedPretrainedModel.from_state_dict(
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state_dict, train=False, use_sequence_packing=False
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)
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model.reset()
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tokenizer = get_tokenizer(model.base_model.name_or_path)
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# prepare data
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doc = open("data/sakana_wiki.txt", "r").read()
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chat = [{"role": "user", "content": "Summarize what Sakana AI does."}]
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chat_ids = tokenizer.apply_chat_template(
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chat,
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add_special_tokens=False,
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return_attention_mask=False,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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# calls after internalization will be influenced by internalized info
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model.internalize(doc)
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outputs = model.generate(input_ids=chat_ids, max_new_tokens=256)
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print(tokenizer.decode(outputs[0]))
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# remove internalized info
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model.reset()
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outputs = model.generate(input_ids=chat_ids, max_new_tokens=256)
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print(tokenizer.decode(outputs[0]))
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```
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### 🎮 Interactive Demo [WIP]
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```bash
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uv run demo/app.py
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```
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### 🧪 Experimental Scripts
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To run any of the following scripts, use `uv run $PATH_TO_SCRIPT` from the root of this project.
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| Experiment | Data prep | Training | Evaluation | Notes |
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| ------------------------------------ | ------------------------------------- | ----------------------------- | ---------------------------- | ----------------------------------------------------------------------------------------------------------------------------------- |
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| [Main experiment](scripts/main_exp/) | `scripts/main_exp/0-download_data.sh` | `scripts/main_exp/1-train.sh` | `scripts/main_exp/eval/*.sh` | Downloading data is fastest; regenerate only if you need fresh synthetic data. Evaluation scripts reproduce the main paper metrics. |
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| [NIAH](scripts/niah/) | `scripts/niah/0-gen_data.sh` | `scripts/niah/1-train.sh` | `scripts/niah/2-eval.sh` | Run the scripts in order; data generation only needs to happen once |
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