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.runpod/LICENSE
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MIT License
Copyright (c) 2023 runpod-workers
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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.runpod/README.md
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<h1>LLM Training- Full finetune, LoRA, QLoRa etc. Llama/Mistral/Gemma</h1>
## RunPod Worker Images
Below is a summary of the available RunPod Worker images, categorized by image stability and CUDA version compatibility.
| Preview Image Tag | Development Image Tag |
-----------------------------------|-----------------------------------|
| `runpod/llm-finetuning:preview` | `runpod/llm-finetuning:dev`
# Configuration Options
This document outlines all available configuration options for training models. The configuration can be provided as a JSON request.
@@ -19,6 +9,7 @@ This document outlines all available configuration options for training models.
You can use these configuration Options:
1. As a JSON request body:
```json
{
"input": {
@@ -41,187 +32,180 @@ You can use these configuration Options:
### Model Configuration
| Option | Description | Default |
|--------|-------------|---------|
| `base_model` | Path to the base model (local or HuggingFace) | Required |
| `base_model_config` | Configuration path for the base model | Same as base_model |
| `revision_of_model` | Specific model revision from HuggingFace hub | Latest |
| `tokenizer_config` | Custom tokenizer configuration path | Optional |
| `model_type` | Type of model to load | AutoModelForCausalLM |
| `tokenizer_type` | Type of tokenizer to use | AutoTokenizer |
| `hub_model_id` | Repository ID where the model will be pushed on Hugging Face Hub (format: username/repo-name) | Optional |
| Option | Description | Default |
| ------------------- | --------------------------------------------------------------------------------------------- | -------------------- |
| `base_model` | Path to the base model (local or HuggingFace) | Required |
| `base_model_config` | Configuration path for the base model | Same as base_model |
| `revision_of_model` | Specific model revision from HuggingFace hub | Latest |
| `tokenizer_config` | Custom tokenizer configuration path | Optional |
| `model_type` | Type of model to load | AutoModelForCausalLM |
| `tokenizer_type` | Type of tokenizer to use | AutoTokenizer |
| `hub_model_id` | Repository ID where the model will be pushed on Hugging Face Hub (format: username/repo-name) | Optional |
## Model Family Identification
| Option | Default | Description |
|--------|---------|-------------|
| `is_falcon_derived_model` | `false` | Whether model is Falcon-based |
| `is_llama_derived_model` | `false` | Whether model is LLaMA-based |
| `is_qwen_derived_model` | `false` | Whether model is Qwen-based |
| Option | Default | Description |
| -------------------------- | ------- | ------------------------------ |
| `is_falcon_derived_model` | `false` | Whether model is Falcon-based |
| `is_llama_derived_model` | `false` | Whether model is LLaMA-based |
| `is_qwen_derived_model` | `false` | Whether model is Qwen-based |
| `is_mistral_derived_model` | `false` | Whether model is Mistral-based |
## Model Configuration Overrides
| Option | Default | Description |
|--------|---------|-------------|
| `overrides_of_model_config.rope_scaling.type` | `"linear"` | RoPE scaling type (linear/dynamic) |
| `overrides_of_model_config.rope_scaling.factor` | `1.0` | RoPE scaling factor |
| Option | Default | Description |
| ----------------------------------------------- | ---------- | ---------------------------------- |
| `overrides_of_model_config.rope_scaling.type` | `"linear"` | RoPE scaling type (linear/dynamic) |
| `overrides_of_model_config.rope_scaling.factor` | `1.0` | RoPE scaling factor |
### Model Loading Options
| Option | Description | Default |
|--------|-------------|---------|
| `load_in_8bit` | Load model in 8-bit precision | false |
| `load_in_4bit` | Load model in 4-bit precision | false |
| `bf16` | Use bfloat16 precision | false |
| `fp16` | Use float16 precision | false |
| `tf32` | Use tensor float 32 precision | false |
| Option | Description | Default |
| -------------- | ----------------------------- | ------- |
| `load_in_8bit` | Load model in 8-bit precision | false |
| `load_in_4bit` | Load model in 4-bit precision | false |
| `bf16` | Use bfloat16 precision | false |
| `fp16` | Use float16 precision | false |
| `tf32` | Use tensor float 32 precision | false |
## Memory and Device Settings
| Option | Default | Description |
|--------|---------|-------------|
| `gpu_memory_limit` | `"20GiB"` | GPU memory limit |
| `lora_on_cpu` | `false` | Load LoRA on CPU |
| `device_map` | `"auto"` | Device mapping strategy |
| `max_memory` | `null` | Max memory per device |
| Option | Default | Description |
| ------------------ | --------- | ----------------------- |
| `gpu_memory_limit` | `"20GiB"` | GPU memory limit |
| `lora_on_cpu` | `false` | Load LoRA on CPU |
| `device_map` | `"auto"` | Device mapping strategy |
| `max_memory` | `null` | Max memory per device |
## Training Hyperparameters
| Option | Default | Description |
|--------|---------|-------------|
| `gradient_accumulation_steps` | `1` | Gradient accumulation steps |
| `micro_batch_size` | `2` | Batch size per GPU |
| `eval_batch_size` | `null` | Evaluation batch size |
| `num_epochs` | `4` | Number of training epochs |
| `warmup_steps` | `100` | Warmup steps |
| `warmup_ratio` | `0.05` | Warmup ratio |
| `learning_rate` | `0.00003` | Learning rate |
| `lr_quadratic_warmup` | `false` | Quadratic warmup |
| `logging_steps` | `null` | Logging frequency |
| `eval_steps` | `null` | Evaluation frequency |
| `evals_per_epoch` | `null` | Evaluations per epoch |
| `save_strategy` | `"epoch"` | Checkpoint saving strategy |
| `save_steps` | `null` | Saving frequency |
| `saves_per_epoch` | `null` | Saves per epoch |
| `save_total_limit` | `null` | Maximum checkpoints to keep |
| `max_steps` | `null` | Maximum training steps |
| Option | Default | Description |
| ----------------------------- | --------- | --------------------------- |
| `gradient_accumulation_steps` | `1` | Gradient accumulation steps |
| `micro_batch_size` | `2` | Batch size per GPU |
| `eval_batch_size` | `null` | Evaluation batch size |
| `num_epochs` | `4` | Number of training epochs |
| `warmup_steps` | `100` | Warmup steps |
| `warmup_ratio` | `0.05` | Warmup ratio |
| `learning_rate` | `0.00003` | Learning rate |
| `lr_quadratic_warmup` | `false` | Quadratic warmup |
| `logging_steps` | `null` | Logging frequency |
| `eval_steps` | `null` | Evaluation frequency |
| `evals_per_epoch` | `null` | Evaluations per epoch |
| `save_strategy` | `"epoch"` | Checkpoint saving strategy |
| `save_steps` | `null` | Saving frequency |
| `saves_per_epoch` | `null` | Saves per epoch |
| `save_total_limit` | `null` | Maximum checkpoints to keep |
| `max_steps` | `null` | Maximum training steps |
### Dataset Configuration
```yaml
datasets:
- path: vicgalle/alpaca-gpt4 # HuggingFace dataset or TODO: You will be able to add the local path.
type: alpaca # Format type (alpaca, gpteacher, oasst, etc.)
ds_type: json # Dataset type
data_files: path/to/data # Source data files
train_on_split: train # Dataset split to use
- path: vicgalle/alpaca-gpt4 # HuggingFace dataset or TODO: You will be able to add the local path.
type: alpaca # Format type (alpaca, gpteacher, oasst, etc.)
ds_type: json # Dataset type
data_files: path/to/data # Source data files
train_on_split: train # Dataset split to use
```
## Chat Template Settings
| Option | Default | Description |
|--------|---------|-------------|
| `chat_template` | `"tokenizer_default"` | Chat template type |
| `chat_template_jinja` | `null` | Custom Jinja template |
| Option | Default | Description |
| ------------------------ | -------------------------------- | ---------------------- |
| `chat_template` | `"tokenizer_default"` | Chat template type |
| `chat_template_jinja` | `null` | Custom Jinja template |
| `default_system_message` | `"You are a helpful assistant."` | Default system message |
## Dataset Processing
| Option | Default | Description |
|--------|---------|-------------|
| `dataset_prepared_path` | `"data/last_run_prepared"` | Path for prepared dataset |
| `push_dataset_to_hub` | `""` | Push dataset to HF hub |
| `dataset_processes` | `4` | Number of preprocessing processes |
| `dataset_keep_in_memory` | `false` | Keep dataset in memory |
| `shuffle_merged_datasets` | `true` | Shuffle merged datasets |
| `dataset_exact_deduplication` | `true` | Deduplicate datasets |
| Option | Default | Description |
| ----------------------------- | -------------------------- | --------------------------------- |
| `dataset_prepared_path` | `"data/last_run_prepared"` | Path for prepared dataset |
| `push_dataset_to_hub` | `""` | Push dataset to HF hub |
| `dataset_processes` | `4` | Number of preprocessing processes |
| `dataset_keep_in_memory` | `false` | Keep dataset in memory |
| `shuffle_merged_datasets` | `true` | Shuffle merged datasets |
| `dataset_exact_deduplication` | `true` | Deduplicate datasets |
## LoRA Configuration
| Option | Default | Description |
|--------|---------|-------------|
| `adapter` | `"lora"` | Adapter type (lora/qlora) |
| `lora_model_dir` | `""` | Directory with pretrained LoRA |
| `lora_r` | `8` | LoRA attention dimension |
| `lora_alpha` | `16` | LoRA alpha parameter |
| `lora_dropout` | `0.05` | LoRA dropout |
| `lora_target_modules` | `["q_proj", "v_proj"]` | Modules to apply LoRA |
| `lora_target_linear` | `false` | Target all linear modules |
| `peft_layers_to_transform` | `[]` | Layers to transform |
| `lora_modules_to_save` | `[]` | Modules to save |
| `lora_fan_in_fan_out` | `false` | Fan in/out structure |
| Option | Default | Description |
| -------------------------- | ---------------------- | ------------------------------ |
| `adapter` | `"lora"` | Adapter type (lora/qlora) |
| `lora_model_dir` | `""` | Directory with pretrained LoRA |
| `lora_r` | `8` | LoRA attention dimension |
| `lora_alpha` | `16` | LoRA alpha parameter |
| `lora_dropout` | `0.05` | LoRA dropout |
| `lora_target_modules` | `["q_proj", "v_proj"]` | Modules to apply LoRA |
| `lora_target_linear` | `false` | Target all linear modules |
| `peft_layers_to_transform` | `[]` | Layers to transform |
| `lora_modules_to_save` | `[]` | Modules to save |
| `lora_fan_in_fan_out` | `false` | Fan in/out structure |
## Optimization Settings
| Option | Default | Description |
|--------|---------|-------------|
| `train_on_inputs` | `false` | Train on input prompts |
| `group_by_length` | `false` | Group by sequence length |
| `gradient_checkpointing` | `false` | Use gradient checkpointing |
| `early_stopping_patience` | `3` | Early stopping patience |
| Option | Default | Description |
| ------------------------- | ------- | -------------------------- |
| `train_on_inputs` | `false` | Train on input prompts |
| `group_by_length` | `false` | Group by sequence length |
| `gradient_checkpointing` | `false` | Use gradient checkpointing |
| `early_stopping_patience` | `3` | Early stopping patience |
## Learning Rate Scheduling
| Option | Default | Description |
|--------|---------|-------------|
| `lr_scheduler` | `"cosine"` | Scheduler type |
| `lr_scheduler_kwargs` | `{}` | Scheduler parameters |
| `cosine_min_lr_ratio` | `null` | Minimum LR ratio |
| `cosine_constant_lr_ratio` | `null` | Constant LR ratio |
| `lr_div_factor` | `null` | LR division factor |
| Option | Default | Description |
| -------------------------- | ---------- | -------------------- |
| `lr_scheduler` | `"cosine"` | Scheduler type |
| `lr_scheduler_kwargs` | `{}` | Scheduler parameters |
| `cosine_min_lr_ratio` | `null` | Minimum LR ratio |
| `cosine_constant_lr_ratio` | `null` | Constant LR ratio |
| `lr_div_factor` | `null` | LR division factor |
## Optimizer Settings
| Option | Default | Description |
|--------|---------|-------------|
| `optimizer` | `"adamw_hf"` | Optimizer choice |
| `optim_args` | `{}` | Optimizer arguments |
| `optim_target_modules` | `[]` | Target modules |
| `weight_decay` | `null` | Weight decay |
| `adam_beta1` | `null` | Adam beta1 |
| `adam_beta2` | `null` | Adam beta2 |
| `adam_epsilon` | `null` | Adam epsilon |
| `max_grad_norm` | `null` | Gradient clipping |
| Option | Default | Description |
| ---------------------- | ------------ | ------------------- |
| `optimizer` | `"adamw_hf"` | Optimizer choice |
| `optim_args` | `{}` | Optimizer arguments |
| `optim_target_modules` | `[]` | Target modules |
| `weight_decay` | `null` | Weight decay |
| `adam_beta1` | `null` | Adam beta1 |
| `adam_beta2` | `null` | Adam beta2 |
| `adam_epsilon` | `null` | Adam epsilon |
| `max_grad_norm` | `null` | Gradient clipping |
## Attention Implementations
| Option | Default | Description |
|--------|---------|-------------|
| `flash_optimum` | `false` | Use better transformers |
| `xformers_attention` | `false` | Use xformers |
| `flash_attention` | `false` | Use flash attention |
| Option | Default | Description |
| -------------------------- | ------- | ----------------------------- |
| `flash_optimum` | `false` | Use better transformers |
| `xformers_attention` | `false` | Use xformers |
| `flash_attention` | `false` | Use flash attention |
| `flash_attn_cross_entropy` | `false` | Flash attention cross entropy |
| `flash_attn_rms_norm` | `false` | Flash attention RMS norm |
| `flash_attn_fuse_qkv` | `false` | Fuse QKV operations |
| `flash_attn_fuse_mlp` | `false` | Fuse MLP operations |
| `sdp_attention` | `false` | Use scaled dot product |
| `s2_attention` | `false` | Use shifted sparse attention |
| `flash_attn_rms_norm` | `false` | Flash attention RMS norm |
| `flash_attn_fuse_qkv` | `false` | Fuse QKV operations |
| `flash_attn_fuse_mlp` | `false` | Fuse MLP operations |
| `sdp_attention` | `false` | Use scaled dot product |
| `s2_attention` | `false` | Use shifted sparse attention |
## Tokenizer Modifications
| Option | Default | Description |
|--------|---------|-------------|
| `special_tokens` | - | Special tokens to add/modify |
| `tokens` | `[]` | Additional tokens |
| Option | Default | Description |
| ---------------- | ------- | ---------------------------- |
| `special_tokens` | - | Special tokens to add/modify |
| `tokens` | `[]` | Additional tokens |
## Distributed Training
| Option | Default | Description |
|--------|---------|-------------|
| `fsdp` | `null` | FSDP configuration |
| `fsdp_config` | `null` | FSDP config options |
| `deepspeed` | `null` | Deepspeed config path |
| `ddp_timeout` | `null` | DDP timeout |
| `ddp_bucket_cap_mb` | `null` | DDP bucket capacity |
| `ddp_broadcast_buffers` | `null` | DDP broadcast buffers |
| Option | Default | Description |
| ----------------------- | ------- | --------------------- |
| `fsdp` | `null` | FSDP configuration |
| `fsdp_config` | `null` | FSDP config options |
| `deepspeed` | `null` | Deepspeed config path |
| `ddp_timeout` | `null` | DDP timeout |
| `ddp_bucket_cap_mb` | `null` | DDP bucket capacity |
| `ddp_broadcast_buffers` | `null` | DDP broadcast buffers |
<details>
<summary><h3>Example Configuration Request:</h3></summary>
@@ -299,20 +283,21 @@ Here's a complete example for fine-tuning a LLaMA model using LoRA:
}
}
```
</details>
### Advanced Features
#### Wandb Integration
- `wandb_project`: Project name for Weights & Biases
- `wandb_entity`: Team name in W&B
- `wandb_watch`: Monitor model with W&B
- `wandb_name`: Name of the W&B run
- `wandb_run_id`: ID for the W&B run
#### Performance Optimization
- `sample_packing`: Enable efficient sequence packing
- `eval_sample_packing`: Use sequence packing during evaluation
- `torch_compile`: Enable PyTorch 2.0 compilation
@@ -336,8 +321,6 @@ The following optimizers are supported:
- `sgd`: Stochastic Gradient Descent
- `adagrad`: Adagrad optimizer
## Notes
- Set `load_in_8bit: true` or `load_in_4bit: true` for memory-efficient training
@@ -347,10 +330,6 @@ The following optimizers are supported:
For more detailed information, please refer to the [documentation](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html).
### Errors:
### Errors:
- if you face any issues with the Flash Attention-2, Delete yoor worker and Re-start.