158 lines
4.5 KiB
Markdown
158 lines
4.5 KiB
Markdown
# Parameter Documentation
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## Training Parameters
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### Basic Parameters
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--train_type` | Training type (`seq`, `sft`, `dpo`, `grpo`) | required |
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| `--data_root_path` | Dataset root directory | required |
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| `--param_path` | Model parameters or checkpoint path | required |
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| `--n_epoch` | Total training epochs | 1 |
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| `--batch_size` | Batch size | 1 |
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| `--accumulation_steps` | Gradient accumulation steps between optimizer steps | 1 |
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### Learning Rate Scheduling
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--warmup_steps` | Warmup steps | 1000 |
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| `--max_lr` | Maximum learning rate (cosine decay after warmup) | 3e-4 |
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| `--max_grad_norm` | Maximum gradient norm for clipping | 1.0 |
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### Optimizer (AdamW)
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--adamw_beta1` | AdamW beta1 | 0.9 |
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| `--adamw_beta2` | AdamW beta2 | 0.95 |
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| `--adamw_weight_decay` | AdamW weight decay | 0.01 |
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### Data Loading
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--window_size` | Max input sequence length | model config `max_len` |
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| `--stride` | Stride for sliding window over sequences | None |
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| `--random_seed` | Random seed for reproducibility | 3407 |
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| `--num_workers` | DataLoader worker processes | 4 |
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| `--no_pin_memory` | Disable pin_memory (enabled by default) | (flag) |
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### Checkpoint & Resume
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--ckpt_interval` | Iterations between checkpoints | 5000 |
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| `--ckpt_dir` | Checkpoint save directory | checkpoint |
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| `--start_epoch` | Resume from epoch (0 = from scratch) | 0 |
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| `--start_batch` | Resume from batch iteration | 0 |
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### Distributed Training
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| Parameter | Description | Default |
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|-----------|-------------|---------|
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| `--nprocs` | Number of GPUs / processes | 1 |
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| `--device_type` | Device type | cuda |
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### Strategy-specific
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| Parameter | Description | Default | Used by |
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|-----------|-------------|---------|---------|
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| `--dpo_beta` | DPO beta value | 0.1 | `dpo` |
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| `--label_smoothing` | Label smoothing for cross-entropy loss | 0.1 | `seq`, `sft` |
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| `--group_size` | GRPO group size | 4 | `grpo` |
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| `--grpo_clip_eps` | GRPO clipping epsilon | 0.2 | `grpo` |
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| `--grpo_kl_coef` | GRPO KL penalty coefficient | 0.01 | `grpo` |
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| `--grpo_sync_interval` | GRPO ref_model sync interval (steps) | 200 | `grpo` |
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### Usage Example
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```bash
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python scripts/tools/train.py \
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--train_type seq \
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--data_root_path /path/to/dataset \
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--param_path /path/to/model \
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--n_epoch 3 \
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--batch_size 4 \
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--accumulation_steps 8 \
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--max_lr 3e-4 \
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--warmup_steps 2000 \
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--max_grad_norm 1.0 \
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--ckpt_interval 5000 \
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--ckpt_dir ./checkpoints \
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--num_workers 4 \
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--nprocs 1 \
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--device_type cuda
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```
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---
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## Generation Parameters
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### GenerationRequest Parameters
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| Parameter | Description | Default Value |
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|-----------|-------------|---------------|
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| `messages` | List of message dictionaries (role, content) | required |
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| `temperature` | Sampling temperature (higher = more random) | 1.0 |
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| `top_p` | Nucleus sampling threshold | 1.0 |
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| `top_k` | Top-k sampling count | 50 |
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| `max_len` | Maximum generation length | 1024 |
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| `stream` | Whether to stream output | False |
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### Usage Example
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```python
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import torch
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from astrai.model import AutoModel
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from astrai.tokenize import AutoTokenizer
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from astrai.inference import InferenceEngine, GenerationRequest
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# Load model using AutoModel
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model = AutoModel.from_pretrained("your_model_dir")
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# Load tokenizer
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tokenizer = AutoTokenizer.from_pretrained("your_model_dir")
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# Create engine with separate model and tokenizer
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engine = InferenceEngine(
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model=model,
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tokenizer=tokenizer,
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)
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# Build request with messages format
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request = GenerationRequest(
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": "Hello"},
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],
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temperature=0.8,
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top_p=0.95,
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top_k=50,
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max_len=1024,
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)
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# Generate (streaming)
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for token in engine.generate_with_request(request):
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print(token, end="", flush=True)
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# Or use simple generate interface
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result = engine.generate(
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prompt="Hello",
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stream=False,
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max_tokens=1024,
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temperature=0.8,
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top_p=0.95,
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top_k=50,
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)
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```
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### Generation Modes
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| Mode | Description |
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|------|-------------|
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| `stream=True` | Streaming output, yields token by token |
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| `stream=False` | Non-streaming output, returns complete result |
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> Document Update Time: 2026-04-09 |