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"""
GPT-OSS Medical o1 SFT Training Configuration
Dataset: FreedomIntelligence/medical-o1-reasoning-SFT
Format: Question | Complex_CoT | Response → GPT-OSS Harmony text
This configuration uses GPT-OSS Harmony formatting to combine the medical
dataset's question, chain-of-thought (Complex_CoT), and final response into a
single assistant turn, with optional system and developer messages.
"""
from config.train_gpt_oss_custom import GPTOSSEnhancedCustomConfig
# Medical-o1 SFT configuration for GPT-OSS
config = GPTOSSEnhancedCustomConfig(
# ============================================================================
# DATASET CONFIGURATION
# ============================================================================
dataset_name="FreedomIntelligence/medical-o1-reasoning-SFT",
dataset_config="en", # Use English split by default (can be changed to en_mix/zh/zh_mix)
dataset_split="train",
dataset_format="medical_o1_sft", # Enable medical formatter in training script
# Field mapping and prefixes
input_field="Question", # used for length filtering pre-format
target_field="Response", # used for length filtering pre-format
question_field="Question",
reasoning_field="Complex_CoT",
response_field="Response",
reason_prefix="Reasoning: ",
answer_prefix="Final Answer: ",
# GPT-OSS Harmony formatting
use_harmony_format=True,
use_chat_template=False,
system_message=(
"You are GPT-Tonic, a large language model trained by TonicAI."
),
developer_message=(
"You are are GPT-Tonic, an intelligent assistant that always answers health-related queries scientifically."
),
chat_template_kwargs={
"add_generation_prompt": True,
"tokenize": False,
"reasoning_effort": "low",
"model_identity": "You are GPT-Tonic, a large language model trained by TonicAI.",
"builtin_tools": [],
},
# Filtering & sampling
filter_bad_entries=False,
max_samples=None,
min_length=10,
max_length=2048,
# ============================================================================
# TRAINING HYPERPARAMETERS
# ============================================================================
num_train_epochs=2.0,
batch_size=4,
gradient_accumulation_steps=4,
learning_rate=2e-4,
min_lr=2e-5,
weight_decay=0.01,
warmup_ratio=0.03,
warmup_steps=50,
max_grad_norm=1.0,
# Scheduler: use broadly compatible cosine by default to avoid TRL signature issues
scheduler="cosine",
lr_scheduler_kwargs={},
# Sequence length
max_seq_length=2048,
# ============================================================================
# MIXED PRECISION / PERFORMANCE
# ============================================================================
fp16=False,
bf16=True,
tf32=True,
dataloader_num_workers=4,
dataloader_pin_memory=True,
dataloader_prefetch_factor=2,
group_by_length=True,
remove_unused_columns=True,
# ============================================================================
# LORA & QUANTIZATION
# ============================================================================
use_lora=True,
lora_config={
"r": 16,
"lora_alpha": 32,
"lora_dropout": 0.05,
"target_modules": "all-linear",
"target_parameters": [
"7.mlp.experts.gate_up_proj",
"7.mlp.experts.down_proj",
"15.mlp.experts.gate_up_proj",
"15.mlp.experts.down_proj",
"23.mlp.experts.gate_up_proj",
"23.mlp.experts.down_proj",
],
"bias": "none",
"task_type": "CAUSAL_LM",
},
use_quantization=True,
quantization_config={
"dequantize": True,
"load_in_4bit": False,
# Optional MXFP4 config is auto-applied by training script if available
},
# ============================================================================
# LOGGING & EVAL
# ============================================================================
eval_strategy="steps",
eval_steps=100,
logging_steps=10,
save_strategy="steps",
save_steps=500,
save_total_limit=3,
save_only_model=True,
metric_for_best_model="eval_loss",
greater_is_better=False,
load_best_model_at_end=False,
eval_accumulation_steps=2,
eval_batch_size=1,
eval_ratio=0.001,
test_ratio=0.0005,
# ============================================================================
# MONITORING & HUB
# ============================================================================
enable_tracking=True,
log_artifacts=False,
log_metrics=True,
log_config=True,
push_to_hub=False,
hub_model_id=None,
hub_private_repo=False,
)
# Quick summary for visibility when the config is imported
print("\n🩺 GPT-OSS Medical o1 SFT Configuration")
print("=" * 60)
print(f"📊 Dataset: {config.dataset_name} [{config.dataset_config}] (medical_o1_sft)")
print(f"📈 Training: {config.num_train_epochs} epoch | batch {config.batch_size} x acc {config.gradient_accumulation_steps}")
print(f"🧠 LoRA Rank: {config.lora_config['r']}")
print(f"📏 Sequence Length: {config.max_seq_length}")
print(f"🎵 Harmony Format: {'Enabled' if config.use_harmony_format else 'Disabled'}")
print("=" * 60)
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