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wandb/
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QLoRA: Efficient Finetuning of Quantized LLMs
| [Paper](https://arxiv.org/abs/2305.14314) | [Adapter Weights](https://huggingface.co/timdettmers) | [Demo](https://huggingface.co/spaces/uwnlp/guanaco-playground-tgi) |
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version: '3.8'
services:
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project:
type: website
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codecov:
require_ci_to_pass: yes
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<p align="center">
<picture>
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FAQs
- Can you train StableLM with this? Yes, but only with a single GPU atm. Multi GPU support is coming soon! Just waiting on this [PR](https://github.com/huggingface/transformers/pull/22874)
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default_language_version:
python: python3
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**/axolotl.egg-info
configs
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data/*.jsonl filter=lfs diff=lfs merge=lfs -text
generic skill
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yaml-language-server: $schema=https://coderabbit.ai/integrations/schema.v2.json
language: "en-US"
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root = true
[*]
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[run]
source = axolotl
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[bandit]
exclude = tests
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Byte-compiled / optimized / DLL files
__pycache__/
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<p align="center">
<img src="https://raw.githubusercontent.com/huggingface/alignment-handbook/main/assets/handbook.png">
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Making a release
> [!NOTE]
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*.bak
.gitattributes
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repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
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Contributor Covenant Code of Conduct
We as members, contributors, and leaders pledge to make participation in our
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How to contribute to TRL?
Everyone is welcome to contribute, and we value everybody's contribution. Code contributions are not the only way to help the community. Answering questions, helping others, and improving the documentation are also immensely valuable.
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TRL - Transformer Reinforcement Learning
<div style="text-align: center">
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Byte-compiled / optimized / DLL files
__pycache__/
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