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chore: update doc links (#2509)

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* chore: update doc links

* fix: address pr feedback
This commit is contained in:
NanoCode012
2025-04-11 20:53:18 +07:00
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parent 756a0559c1
commit 51267ded04
4 changed files with 17 additions and 19 deletions
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6
docs/config.qmd
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@@ -90,7 +90,7 @@ lora_on_cpu: true
# List[str]. Add plugins to extend the pipeline.
# See `src/axolotl/integrations` for the available plugins or doc below for more details.
# https://axolotl-ai-cloud.github.io/axolotl/docs/custom_integrations.html
# https://docs.axolotl.ai/docs/custom_integrations.html
plugins:
# - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
@@ -394,7 +394,7 @@ lora_fan_in_fan_out: false
# Apply custom LoRA autograd functions and activation function Triton kernels for
# speed and memory savings
# See: https://axolotl-ai-cloud.github.io/axolotl/docs/lora_optims.html
# See: https://docs.axolotl.ai/docs/lora_optims.html
lora_mlp_kernel: true
lora_qkv_kernel: true
lora_o_kernel: true
@@ -688,7 +688,7 @@ ddp_broadcast_buffers:
# Use in long context training to prevent OOM when sequences cannot fit into a single GPU's VRAM.
# E.g., if 4 GPUs are available, set this value to 2 to split each sequence into two equal-sized
# subsequences, or set to 4 to split into four equal-sized subsequences.
# See https://axolotl-ai-cloud.github.io/axolotl/docs/sequence_parallelism.html for more details.
# See https://docs.axolotl.ai/docs/sequence_parallelism.html for more details.
sequence_parallel_degree:
# Optional; strides across the key dimension. Larger values use more memory but should make training faster.
# Must evenly divide the number of KV heads in your model.

5
docs/dataset-formats/index.qmd
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@@ -457,10 +457,7 @@ datasets:
type: alpaca
```
Axolotl supports many kinds of instruction dataset. All of them can be found here (https://axolotl-ai-cloud.github.io/axolotl/docs/dataset-formats/inst_tune.html) with their respective type and sample row format.
Reference: [Instruction Dataset Documentation](inst_tune.qmd).
Axolotl supports many kinds of instruction dataset. All of them can be found in the [Instruction Dataset Documentation](inst_tune.qmd) with their respective type and sample row format.
#### Custom Instruct Prompt Format