945c8aeb10
* fix: saving clones state dict
* fix: apply fix for only CP mode
* fix: add dropout check when using lora target param
* fix: re-add patch from transformers PR #39866
* feat: add moe quant to test by ved
* fix: try match target param properly end with
* fix: clear cache per param quant
* fix: attempt on-load quantize experts instead of post-load
* fix: attempt disable async load
* chore: add log
* chore: adjust log
* fix: remove cuda alloc for moe and enable async load
* chore: remove leftover logs
* chore: add extra empty cache
* fix(doc): clarify support
* fix: handle fsdp2 for paramwrapper dtensor
* feat: attempt to quant experts in 8bit mode too
* feat: attempt to release bf16 experts from vram
* feat: upgrade cce
* fix: fsdp2 init_sharded_param load int8/uint4 dtensor as
require_grad=true on init
* fix: remove unnecessary gc and empty cache
* Revert "fix: remove unnecessary gc and empty cache"
This reverts commit 1d54518990.
* fix: do not call full_tensor on non-dtensors
* fix: attempt to address fsdp2 with quant exp high loss
* fix: attempt lora quant experts wrong dim
* fix: ensure require_grad patch applied for lora 8bit
* fix: attempt lora 8bit fsdp2
* fix: attribute access on save for lora 8bit fsdp2
* fix: wrong weight attrib access
* chore(refactor): add config, re-arrange position of patches, clean
comments
* feat: add example docs
* chore: cherry pick trinity fixes from PR 3399
* chore: comments refactor; add guards
* fix: guard using wrong key
* fix: mamba save does not accept main process param
* fix: guard prevent double hook
* fix: move gc to upper scope
* chore: add comment on proxy forward patch
* fix: add comment to clarify
* feat: add test idempotency
* fix: AttributeError: `e_score_correction_bias` is not an nn.Parameter
* fix: AttributeError: 'NoneType' object has no attribute 'to'
* fix: update docs on cpu_ram_efficient_loading
1.6 KiB
1.6 KiB
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
-
Install Axolotl following the main from the installation guide.
-
Install Cut Cross Entropy to reduce training VRAM usage.
-
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.