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Finetune ArceeAI's Trinity with Axolotl
Trinity is a family of open weight MoE models trained by Arcee.ai.
This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
Getting started
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Install Axolotl following the main from the installation guide.
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Install Cut Cross Entropy to reduce training VRAM usage.
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Run the finetuning example:
axolotl train examples/trinity/trinity-nano-preview-qlora.yaml
This config uses about 24.9 GiB VRAM (w/o CCE).
Let us know how it goes. Happy finetuning! 🚀
TIPS
- For inference, the official Arcee.ai team recommends
top_p: 0.75,temperature: 0.15,top_k: 50, andmin_p: 0.06. - You can run a full finetuning by removing the
adapter: qloraandload_in_4bit: truefrom the config. - Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.
Optimization Guides
Please check the Optimizations doc.