Spaces:
Runtime error
Runtime error
File size: 8,324 Bytes
9bde38f c7fd3ee 374cf10 c7fd3ee 70d4b89 9bde38f c7fd3ee 6fb4c7e 9bde38f dde34f4 ef53e9f 374cf10 6fb4c7e 374cf10 6fb4c7e c7fd3ee 6fb4c7e dde34f4 6fb4c7e c7fd3ee 374cf10 c7fd3ee 6fb4c7e ef53e9f 374cf10 9bde38f dde34f4 ef53e9f c7fd3ee ef53e9f 9bde38f 374cf10 c7fd3ee 5a15785 9bde38f c7fd3ee 5a15785 c7fd3ee 5a15785 c7fd3ee 5a15785 c7fd3ee 5a15785 c7fd3ee 9bde38f ef53e9f dde34f4 ef53e9f 9bde38f 374cf10 dde34f4 9bde38f dde34f4 9bde38f ef53e9f c7fd3ee ef53e9f 6fb4c7e c7fd3ee 9bde38f c7fd3ee 9bde38f 5a15785 374cf10 5a15785 ef53e9f 374cf10 ef53e9f 6fb4c7e c7fd3ee dde34f4 9bde38f c7fd3ee dde34f4 c7fd3ee e146c91 c7fd3ee e146c91 c7fd3ee 5a15785 e146c91 9bde38f e146c91 dde34f4 e146c91 c7fd3ee dde34f4 e146c91 c7fd3ee 70d4b89 9bde38f dde34f4 9bde38f e146c91 9bde38f 374cf10 c7fd3ee 374cf10 dde34f4 c7fd3ee dde34f4 c7fd3ee 9bde38f c7fd3ee dde34f4 374cf10 dde34f4 374cf10 c7fd3ee dde34f4 374cf10 6fb4c7e c7fd3ee dde34f4 c7fd3ee dde34f4 c7fd3ee dde34f4 9bde38f dde34f4 374cf10 c7fd3ee dde34f4 c7fd3ee dde34f4 c7fd3ee dde34f4 c7fd3ee | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 | """
Dungeon Master LoRA Training - Qwen3.5-9B via Unsloth
======================================================
Resumes from step 200 checkpoint.
Unsloth bf16 LoRA (no 4-bit quantization).
Hardware: L40S 1x (48GB VRAM, $1.80/hr)
"""
import os, sys, time, torch, threading
from http.server import HTTPServer, BaseHTTPRequestHandler
os.environ["PYTHONUNBUFFERED"] = "1"
# ============================================================
# Health check server on port 7860
# ============================================================
STATUS = {"stage": "starting", "step": 200, "total": 2563, "loss": 0.0, "t": time.time()}
class H(BaseHTTPRequestHandler):
def do_GET(self):
self.send_response(200)
self.send_header("Content-Type", "text/html")
self.end_headers()
m = int(time.time() - STATUS["t"]) // 60
self.wfile.write(f"""<html><body style="font-family:monospace;padding:20px">
<h2>DM LoRA Training (resuming from step 200)</h2>
<p>Stage: {STATUS['stage']}</p>
<p>Step: {STATUS['step']}/{STATUS['total']}</p>
<p>Loss: {STATUS['loss']:.4f}</p>
<p>Elapsed: {m} min</p>
</body></html>""".encode())
def log_message(self, *a): pass
srv = HTTPServer(("0.0.0.0", 7860), H)
threading.Thread(target=srv.serve_forever, daemon=True).start()
print("Health check server on :7860", flush=True)
# ============================================================
# Auth
# ============================================================
from huggingface_hub import login, snapshot_download
hf_token = os.environ.get("HF_TOKEN")
if hf_token:
login(token=hf_token)
print("Logged in to HF Hub", flush=True)
else:
print("ERROR: No HF_TOKEN!", flush=True)
sys.exit(1)
# ============================================================
# Download checkpoint from Hub to resume
# ============================================================
STATUS["stage"] = "downloading checkpoint from Hub"
OUTPUT_REPO = "zprime/qwen3.5-9b-dungeon-master-lora"
CHECKPOINT_DIR = "/tmp/dm-lora/checkpoint-200"
print("Downloading step-200 checkpoint from Hub...", flush=True)
os.makedirs(CHECKPOINT_DIR, exist_ok=True)
# Download the last-checkpoint files into the checkpoint dir
snapshot_download(
repo_id=OUTPUT_REPO,
allow_patterns="last-checkpoint/*",
local_dir="/tmp/hub-checkpoint",
)
# Move files from last-checkpoint subfolder to checkpoint-200
import shutil
src = "/tmp/hub-checkpoint/last-checkpoint"
for f in os.listdir(src):
shutil.move(os.path.join(src, f), os.path.join(CHECKPOINT_DIR, f))
print(f"Checkpoint downloaded to {CHECKPOINT_DIR}", flush=True)
print(f"Files: {os.listdir(CHECKPOINT_DIR)}", flush=True)
# ============================================================
# Config
# ============================================================
MODEL_NAME = "unsloth/Qwen3.5-9B"
DATASET_ID = "chimbiwide/RolePlay-NPC-Quest"
MAX_SEQ_LENGTH = 2048
# ============================================================
# Load model via Unsloth
# ============================================================
STATUS["stage"] = "loading model via Unsloth"
print(f"Loading {MODEL_NAME} via Unsloth (bf16)...", flush=True)
from unsloth import FastLanguageModel
model, tokenizer = FastLanguageModel.from_pretrained(
model_name=MODEL_NAME,
max_seq_length=MAX_SEQ_LENGTH,
dtype=torch.bfloat16,
load_in_4bit=False,
)
print("Model loaded via Unsloth", flush=True)
# ============================================================
# Add LoRA via Unsloth
# ============================================================
STATUS["stage"] = "adding LoRA"
model = FastLanguageModel.get_peft_model(
model,
r=16,
target_modules=[
"q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",
],
lora_alpha=32,
lora_dropout=0,
bias="none",
use_gradient_checkpointing=True,
random_state=42,
)
print("LoRA added: r=16, alpha=32", flush=True)
# ============================================================
# Trackio
# ============================================================
try:
import trackio
trackio.init(name="dm-lora-resume-200", project=OUTPUT_REPO)
print("Trackio enabled", flush=True)
REPORT_TO = "trackio"
except Exception as e:
print(f"Trackio warning: {e}", flush=True)
REPORT_TO = "none"
# ============================================================
# Load dataset
# ============================================================
STATUS["stage"] = "loading dataset"
print(f"Loading dataset: {DATASET_ID}", flush=True)
from datasets import load_dataset
dataset = load_dataset(DATASET_ID, split="train")
print(f"Dataset: {len(dataset)} examples", flush=True)
# ============================================================
# Formatting function
# ============================================================
def formatting_func(examples):
texts = []
for messages in examples["messages"]:
text = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=False
)
texts.append(text)
return {"text": texts}
print("Formatting dataset with chat template...", flush=True)
dataset = dataset.map(formatting_func, batched=True, remove_columns=["messages"])
print(f"Dataset formatted: {len(dataset)} examples", flush=True)
# ============================================================
# Training config — same as before so resume works
# ============================================================
STATUS["stage"] = "initializing trainer"
from trl import SFTConfig, SFTTrainer
from transformers import TrainerCallback
training_args = SFTConfig(
output_dir="/tmp/dm-lora",
num_train_epochs=1,
per_device_train_batch_size=1,
gradient_accumulation_steps=8,
learning_rate=2e-4,
lr_scheduler_type="cosine",
warmup_steps=100,
weight_decay=0.01,
max_length=MAX_SEQ_LENGTH,
dataset_text_field="text",
bf16=True,
gradient_checkpointing=True,
gradient_checkpointing_kwargs={"use_reentrant": False},
logging_strategy="steps",
logging_steps=5,
logging_first_step=True,
disable_tqdm=True,
report_to=REPORT_TO,
save_strategy="steps",
save_steps=200,
save_total_limit=3,
push_to_hub=True,
hub_model_id=OUTPUT_REPO,
hub_strategy="checkpoint",
seed=42,
dataloader_num_workers=2,
optim="adamw_8bit",
)
print("Initializing SFTTrainer...", flush=True)
trainer = SFTTrainer(
model=model,
args=training_args,
train_dataset=dataset,
processing_class=tokenizer,
)
total_steps = 2563
STATUS["total"] = total_steps
print(f"Resuming training from step 200 / {total_steps}", flush=True)
print("=" * 60, flush=True)
# Status callback
class SC(TrainerCallback):
def on_log(self, args, state, control, logs=None, **kwargs):
if logs:
STATUS["step"] = state.global_step
STATUS["loss"] = logs.get("loss", 0.0)
print(f"[Step {state.global_step}/{total_steps}] loss={logs.get('loss','?')}, lr={logs.get('learning_rate','?')}", flush=True)
trainer.add_callback(SC())
# ============================================================
# Resume training from checkpoint
# ============================================================
STATUS["stage"] = "training (resumed from step 200)"
print(f"Resuming from {CHECKPOINT_DIR}...", flush=True)
t0 = time.time()
trainer.train(resume_from_checkpoint=CHECKPOINT_DIR)
mins = (time.time() - t0) / 60
print(f"Training done in {mins:.1f} min!", flush=True)
# ============================================================
# Save & push
# ============================================================
STATUS["stage"] = "saving"
print("Saving final model...", flush=True)
trainer.save_model()
print("Pushing to Hub...", flush=True)
trainer.push_to_hub(commit_message="Dungeon Master LoRA - FINAL - Unsloth bf16 r=16")
print(f"DONE! https://huggingface.co/{OUTPUT_REPO}", flush=True)
STATUS["stage"] = "COMPLETE - SET HARDWARE TO CPU!"
print("=" * 60, flush=True)
print("TRAINING COMPLETE!", flush=True)
print(f"Adapter: https://huggingface.co/{OUTPUT_REPO}", flush=True)
print("GO TO SETTINGS -> SET HARDWARE TO CPU TO STOP BILLING!", flush=True)
print("=" * 60, flush=True)
srv.serve_forever()
|