Fix(doc): Minor doc changes for peft and modal (#2462) [skip ci]
* fix(doc): document peft configs * fix(doc): explain modal env vs secrets difference * fix(doc): clarify evaluate vs lm-eval * fix: clarify what is performance
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docs/cli.qmd
40
docs/cli.qmd
@@ -170,7 +170,7 @@ axolotl merge-sharded-fsdp-weights config.yml
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### evaluate
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Evaluates a model's performance using metrics specified in the config.
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Evaluates a model's performance (loss etc) on the train and eval datasets.
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```bash
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# Basic evaluation
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@@ -197,6 +197,8 @@ lm_eval_batch_size: # Batch size for evaluation
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output_dir: # Directory to save evaluation results
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```
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See [LM Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) for more details.
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## Legacy CLI Usage
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While the new Click-based CLI is preferred, Axolotl still supports the legacy module-based CLI:
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@@ -235,7 +237,7 @@ Create a cloud config YAML with your Modal settings:
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```yaml
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# cloud_config.yml
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provider: modal
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gpu: a100 # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
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gpu: a100 # Supported: l40s, a100-40gb, a100-80gb, a10g, h100, t4, l4
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gpu_count: 1 # Number of GPUs to use
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timeout: 86400 # Maximum runtime in seconds (24 hours)
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branch: main # Git branch to use (optional)
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@@ -248,7 +250,7 @@ volumes: # Persistent storage volumes
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- name: axolotl-artifacts
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mount: /workspace/artifacts
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env: # Environment variables
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secrets: # Secrets to inject
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- WANDB_API_KEY
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- HF_TOKEN
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```
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@@ -274,15 +276,27 @@ axolotl lm-eval config.yml --cloud cloud_config.yml
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### Cloud Configuration Options
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```yaml
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provider: # compute provider, currently only `modal` is supported
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gpu: # GPU type to use
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gpu_count: # Number of GPUs (default: 1)
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memory: # RAM in GB (default: 128)
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timeout: # Maximum runtime in seconds
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provider: # compute provider, currently only `modal` is supported
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gpu: # GPU type to use
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gpu_count: # Number of GPUs (default: 1)
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memory: # RAM in GB (default: 128)
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timeout: # Maximum runtime in seconds
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timeout_preprocess: # Preprocessing timeout
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branch: # Git branch to use
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docker_tag: # Custom Docker image tag
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volumes: # List of persistent storage volumes
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env: # Environment variables to pass
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secrets: # Secrets to inject
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branch: # Git branch to use
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docker_tag: # Custom Docker image tag
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volumes: # List of persistent storage volumes
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# Environment variables to pass. Can be specified in two ways:
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# 1. As a string: Will load the value from the host computer's environment variables
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# 2. As a key-value pair: Will use the specified value directly
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# Example:
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# env:
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# - CUSTOM_VAR # Loads from host's $CUSTOM_VAR
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# - {CUSTOM_VAR: "value"} # Uses "value" directly
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env:
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# Secrets to inject. Same input format as `env` but for sensitive data.
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secrets:
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# - HF_TOKEN
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# - WANDB_API_KEY
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```
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