File size: 6,143 Bytes
b5d1ae7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | """Consolidated Training: all 3 speculative-action models in one A100 job.
Trains sequentially with explicit cleanup between models:
1. Qwen3-1.7B proposer (SFT on action prediction)
2. Qwen3-4B verifier (SFT on ACCEPT/REJECT)
3. Qwen3-8B proposer (SFT on action prediction)
Each model is: loaded → trained → evaluated → saved+pushed → deleted → CUDA cache cleared.
Requires: transformers>=4.51, trl, torch, datasets, accelerate, peft, huggingface_hub
"""
import gc
import torch
HUB = "narcolepticchicken"
# ═══════════════════════════════════════════════════════════════
# 1. TRAIN 1.7B PROPOSER
# ═══════════════════════════════════════════════════════════════
print("\n" + "=" * 60)
print("1/3: TRAINING 1.7B PROPOSER")
print("=" * 60)
from datasets import load_dataset
from trl import SFTConfig, SFTTrainer
sft_ds = load_dataset(f"{HUB}/speculative-sft-v3-main")
print(f"SFT data: {len(sft_ds['train'])} train, {len(sft_ds['test'])} test")
args_1b = SFTConfig(
output_dir="./out_1b",
hub_model_id=f"{HUB}/speculative-proposer-v3-1.7b",
max_length=2048,
packing=False,
learning_rate=2e-5,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
num_train_epochs=3,
bf16=True,
gradient_checkpointing=True,
logging_steps=5,
logging_first_step=True,
save_strategy="epoch",
push_to_hub=True,
disable_tqdm=True,
report_to="none",
)
trainer = SFTTrainer(
model="Qwen/Qwen3-1.7B",
args=args_1b,
train_dataset=sft_ds["train"],
eval_dataset=sft_ds["test"],
)
trainer.train()
trainer.save_model()
trainer.push_to_hub()
metrics = trainer.evaluate()
print(f"1.7B eval loss: {metrics.get('eval_loss', 'N/A')}")
del trainer
gc.collect()
torch.cuda.empty_cache()
print("1.7B proposer ✓")
# ═══════════════════════════════════════════════════════════════
# 2. TRAIN 4B VERIFIER
# ═══════════════════════════════════════════════════════════════
print("\n" + "=" * 60)
print("2/3: TRAINING 4B VERIFIER")
print("=" * 60)
from datasets import Dataset
VERIFIER_SYSTEM = (
"You are an action verifier. Given conversation context and a proposed next action, "
"determine if the proposal is correct. Respond with exactly ACCEPT or REJECT."
)
verif_raw = load_dataset(f"{HUB}/speculative-verifier-v3-main")
sft_rows = []
for split_name in ["train", "test"]:
for row in verif_raw[split_name]:
ctx = row["context"]
proposal = row["proposal"]
label = row["label"]
answer = "ACCEPT" if label == 1 else "REJECT"
msgs = [{"role": "system", "content": VERIFIER_SYSTEM}]
for m in ctx[-6:]:
msgs.append({"role": m["role"], "content": str(m["content"])[:400]})
msgs.append({
"role": "user",
"content": f"Proposed next action: {proposal}\n\nIs this the correct next action? ACCEPT or REJECT?"
})
msgs.append({"role": "assistant", "content": answer})
sft_rows.append({"messages": msgs, "split": split_name})
v_train = Dataset.from_list([{"messages": r["messages"]} for r in sft_rows if r["split"] == "train"])
v_test = Dataset.from_list([{"messages": r["messages"]} for r in sft_rows if r["split"] == "test"])
print(f"Verifier SFT: {len(v_train)} train, {len(v_test)} test")
args_4b = SFTConfig(
output_dir="./out_4b",
hub_model_id=f"{HUB}/speculative-verifier-v3-4b",
max_length=2048,
packing=False,
learning_rate=2e-5,
per_device_train_batch_size=4,
gradient_accumulation_steps=4,
num_train_epochs=2,
bf16=True,
gradient_checkpointing=True,
logging_steps=10,
logging_first_step=True,
save_strategy="epoch",
push_to_hub=True,
disable_tqdm=True,
report_to="none",
)
trainer = SFTTrainer(
model="Qwen/Qwen3-4B",
args=args_4b,
train_dataset=v_train,
eval_dataset=v_test,
)
trainer.train()
trainer.save_model()
trainer.push_to_hub()
metrics = trainer.evaluate()
print(f"4B verifier eval loss: {metrics.get('eval_loss', 'N/A')}")
del trainer
gc.collect()
torch.cuda.empty_cache()
print("4B verifier ✓")
# ═══════════════════════════════════════════════════════════════
# 3. TRAIN 8B PROPOSER
# ═══════════════════════════════════════════════════════════════
print("\n" + "=" * 60)
print("3/3: TRAINING 8B PROPOSER")
print("=" * 60)
args_8b = SFTConfig(
output_dir="./out_8b",
hub_model_id=f"{HUB}/speculative-proposer-v3-8b",
max_length=2048,
packing=False,
learning_rate=2e-5,
per_device_train_batch_size=2, # smaller batch to fit 8B on 80GB
gradient_accumulation_steps=8, # effective batch = 16 (same as others)
num_train_epochs=3,
bf16=True,
gradient_checkpointing=True,
logging_steps=5,
logging_first_step=True,
save_strategy="epoch",
push_to_hub=True,
disable_tqdm=True,
report_to="none",
)
trainer = SFTTrainer(
model="Qwen/Qwen3-8B",
args=args_8b,
train_dataset=sft_ds["train"],
eval_dataset=sft_ds["test"],
)
trainer.train()
trainer.save_model()
trainer.push_to_hub()
metrics = trainer.evaluate()
print(f"8B eval loss: {metrics.get('eval_loss', 'N/A')}")
print("\n" + "=" * 60)
print("ALL THREE MODELS TRAINED SUCCESSFULLY!")
print(f" {HUB}/speculative-proposer-v3-1.7b")
print(f" {HUB}/speculative-verifier-v3-4b")
print(f" {HUB}/speculative-proposer-v3-8b")
print("=" * 60)
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