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https://github.com/h2oai/h2o-wizardlm
Open-Source Implementation of WizardLM to turn documents into Q:A pairs for LLM fine-tuning
https://github.com/h2oai/h2o-wizardlm
gpt llm opensource vicuna wizardlm
Last synced: 10 days ago
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Open-Source Implementation of WizardLM to turn documents into Q:A pairs for LLM fine-tuning
- Host: GitHub
- URL: https://github.com/h2oai/h2o-wizardlm
- Owner: h2oai
- License: apache-2.0
- Created: 2023-05-12T23:05:38.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-06-07T17:32:59.000Z (over 1 year ago)
- Last Synced: 2024-08-01T19:41:34.324Z (4 months ago)
- Topics: gpt, llm, opensource, vicuna, wizardlm
- Language: Python
- Homepage: https://h2o.ai
- Size: 149 KB
- Stars: 291
- Watchers: 66
- Forks: 27
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# wizardlm
Open-Source Implementation of WizardLM to turn documents into Q:A pairs for LLM fine-tuning
Automatically creates high-complexity instructions from existing instruct-tuned LLM models, for further fine-tuning. Towards truly open ChatGPT clones, no Vicuna/ShareGPT TOS-violation, everything can be based on top of Apache 2.0 models and data.
- Input: Instruction-tuned LLM and (optional) seed prompts (or document corpus, coming soon)
- Output: Set of high-complexity instruction prompts (and responses)
Based on https://arxiv.org/abs/2304.12244
## Example
- Starting (seed) prompt: "What's trending in science & technology?"
- Auto-generated prompt: "As a researcher in the field of artificial intelligence (AI) and healthcare, you have been tasked with conducting a comprehensive study of the potential of AI in healthcare. Your research must be based on sources that are at least 10 years old, and you must use a minimum of 15 academic sources. Your study must be at least 30 pages long and include a detailed analysis of the current state of AI in healthcare, its potential future developments, and the challenges that need to be addressed to fully realize its potential. Additionally, you must provide a critical evaluation of the existing literature on AI in healthcare, identifying gaps and areas for further research. However, you have been given the additional task of creating a series of recommendations for healthcare organizations and policymakers to help them make informed decisions about the implementation of AI in healthcare. Your recommendations must be evidence-based and take into account the ethical implications of AI in healthcare."
## Installation
Create a Python3.10 environment and install the dependencies:
```bash
pip install -r requirements.txt
```
## Create WizardLM dataset
Edit the base model, and the number of desired rows in `wizardlm.py`, then run it:
```bash
python wizardlm.py
```
You will end up with a file called `wizard_lm.uuid.json` where uuid is a random string. Example files are placed in [this folder](./sample_autogenerated_instructions/).
## Known issues
- Slow, even with pipeline and batching
- Requires reasonably good instruct-tuned LLM for current prompting logic
- Auto-generated prompts are good, but responses can be empty, at least for junelee/wizard-vicuna-13b
## Roadmap
- Speed up
- Improve generated responses
- Allow complexity control
- Handle instruction/input vs just instruction, to enable summarization/code
- Use in complete Apache 2.0 loop, Open LLaMa + oasst1 -> wizardlm -> iterate.