AstrAI/assets/docs/params.md

Parameter Documentation

Training Parameters

Basic Parameters

Parameter	Description	Default
--train_type	Training type (seq, sft, dpo, grpo)	required
--data_root_path	Dataset root directory	required
--param_path	Model parameters or checkpoint path	required
--n_epoch	Total training epochs	1
--batch_size	Batch size	1
--accumulation_steps	Gradient accumulation steps between optimizer steps	1

Learning Rate Scheduling

Parameter	Description	Default
--warmup_steps	Warmup steps	1000
--max_lr	Maximum learning rate (cosine decay after warmup)	3e-4
--max_grad_norm	Maximum gradient norm for clipping	1.0

Optimizer (AdamW)

Parameter	Description	Default
--adamw_beta1	AdamW beta1	0.9
--adamw_beta2	AdamW beta2	0.95
--adamw_weight_decay	AdamW weight decay	0.01

Data Loading

Parameter	Description	Default
--window_size	Max input sequence length	model config max_len
--stride	Stride for sliding window over sequences	None
--random_seed	Random seed for reproducibility	3407
--num_workers	DataLoader worker processes	4
--no_pin_memory	Disable pin_memory (enabled by default)	(flag)

Checkpoint & Resume

Parameter	Description	Default
--ckpt_interval	Iterations between checkpoints	5000
--ckpt_dir	Checkpoint save directory	checkpoint
--start_epoch	Resume from epoch (0 = from scratch)	0
--start_batch	Resume from batch iteration	0

Distributed Training

Parameter	Description	Default
--nprocs	Number of GPUs / processes	1
--device_type	Device type	cuda

Strategy-specific

Parameter	Description	Default	Used by
--dpo_beta	DPO beta value	0.1	dpo
--label_smoothing	Label smoothing for cross-entropy loss	0.1	seq, sft
--group_size	GRPO group size	4	grpo
--grpo_clip_eps	GRPO clipping epsilon	0.2	grpo
--grpo_kl_coef	GRPO KL penalty coefficient	0.01	grpo
--grpo_sync_interval	GRPO ref_model sync interval (steps)	200	grpo

Usage Example

python scripts/tools/train.py \
  --train_type seq \
  --data_root_path /path/to/dataset \
  --param_path /path/to/model \
  --n_epoch 3 \
  --batch_size 4 \
  --accumulation_steps 8 \
  --max_lr 3e-4 \
  --warmup_steps 2000 \
  --max_grad_norm 1.0 \
  --ckpt_interval 5000 \
  --ckpt_dir ./checkpoints \
  --num_workers 4 \
  --nprocs 1 \
  --device_type cuda

---

Generation Parameters

GenerationRequest Parameters

Parameter	Description	Default Value
messages	List of message dictionaries (role, content)	required
temperature	Sampling temperature (higher = more random)	1.0
top_p	Nucleus sampling threshold	1.0
top_k	Top-k sampling count	50
max_len	Maximum generation length	1024
stream	Whether to stream output	False

Usage Example

import torch
from astrai.model import AutoModel
from astrai.tokenize import AutoTokenizer
from astrai.inference import InferenceEngine, GenerationRequest

# Load model using AutoModel
model = AutoModel.from_pretrained("your_model_dir")

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("your_model_dir")

# Create engine with separate model and tokenizer
engine = InferenceEngine(
    model=model,
    tokenizer=tokenizer,
)

# Build request with messages format
request = GenerationRequest(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello"},
    ],
    temperature=0.8,
    top_p=0.95,
    top_k=50,
    max_len=1024,
)

# Generate (streaming)
for token in engine.generate_with_request(request):
    print(token, end="", flush=True)

# Or use simple generate interface
result = engine.generate(
    prompt="Hello",
    stream=False,
    max_tokens=1024,
    temperature=0.8,
    top_p=0.95,
    top_k=50,
)

Generation Modes

Mode	Description
stream=True	Streaming output, yields token by token
stream=False	Non-streaming output, returns complete result

Document Update Time: 2026-05-14