AstrAI/assets/docs/params.md

Parameter Documentation

Training Parameters

Basic Parameters

Parameter	Description	Default Value
--train_type	Training type (seq, sft, dpo, grpo)	required
--model_type	Model type for AutoModel loading (e.g., transformer)	transformer
--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	4
--accumulation_steps	Gradient accumulation steps	1

Learning Rate Scheduling

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

Checkpoint

Parameter	Description	Default Value
--ckpt_interval	Checkpoint save interval (iterations)	5000
--ckpt_dir	Checkpoint save directory	checkpoint
--resume_dir	Resume training from specified path	-

Optimizer Parameters

Parameter	Description	Default Value
--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 Value
--random_seed	Random seed	3407
--num_workers	DataLoader workers	0
--prefetch_factor	Prefetch factor for dataloader	None
--pin_memory	Enable pin_memory	False
--no_pin_memory	Disable pin_memory	-

Distributed Training

Parameter	Description	Default Value
--nprocs	Number of GPUs	1
--device_type	Device type (cuda/cpu)	cuda

Other Parameters

Parameter	Description	Default Value
--window_size	Maximum input sequence length	model config max_len
--stride	Input sequence stride	-
--dpo_beta	DPO beta value	0.1
--grpo_clip_eps	GRPO clip epsilon	0.2
--grpo_kl_coef	GRPO KL coefficient	0.01
--grpo_group_size	GRPO group size	4
--label_smoothing	Label smoothing parameter	0.1
--start_epoch	Starting epoch	0
--start_batch	Starting batch	0

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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 Tokenizer
from astrai.inference import InferenceEngine, GenerationRequest

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

# Load tokenizer
tokenizer = Tokenizer("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: 2026-04-09