File size: 3,419 Bytes
5fc8c9d |
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 |
# okto_version: "1.2"
PROJECT "CompleteV12Example"
DESCRIPTION "Complete example demonstrating all v1.2 features"
ENV {
accelerator: "gpu"
min_memory: "16GB"
precision: "fp16"
backend: "oktoseek"
install_missing: true
}
DATASET {
train: "examples/datasets/demo_train.jsonl"
validation: "examples/datasets/demo_train.jsonl"
format: "jsonl"
type: "chat"
language: "en"
}
MODEL {
name: "complete-v12-model"
base: "google/flan-t5-base"
device: "cuda"
ADAPTER {
type: "lora"
path: "./adapters/my-adapter"
rank: 16
alpha: 32
}
}
TRAIN {
epochs: 10
batch_size: 32
learning_rate: 0.0001
optimizer: "adamw"
scheduler: "cosine"
device: "cuda"
checkpoint_steps: 100
}
METRICS {
accuracy
loss
perplexity
f1
confidence
}
MONITOR {
metrics: [
"loss",
"val_loss",
"accuracy",
"gpu_usage",
"ram_usage",
"throughput",
"latency",
"confidence"
]
notify_if {
loss > 2.0
gpu_usage > 90%
temperature > 85
hallucination_score > 0.5
}
log_to: "logs/training.log"
}
BEHAVIOR {
mode: "chat"
personality: "assistant"
verbosity: "medium"
language: "en"
avoid: ["politics", "violence", "hate"]
fallback: "How can I help you?"
prompt_style: "User: {input}\nAssistant:"
}
STABILITY {
stop_if_nan: true
stop_if_diverges: true
min_improvement: 0.001
}
EXPLORER {
try {
lr: [0.0003, 0.0001]
batch_size: [16, 32]
}
max_tests: 4
pick_best_by: "val_loss"
}
CONTROL {
on_epoch_end {
SAVE model
LOG "Epoch completed"
IF loss > 2.0 {
SET LR = 0.00005
LOG "High loss detected"
WHEN gpu_usage > 90% {
SET batch_size = 16
LOG "Reducing batch size due to GPU pressure"
}
}
IF val_loss > 2.5 {
STOP_TRAINING
}
IF accuracy > 0.9 {
SAVE "best_model"
LOG "High accuracy reached"
}
}
validate_every: 200
WHEN gpu_memory < 12GB {
SET batch_size = 16
}
EVERY 500 steps {
SAVE checkpoint
}
}
INFERENCE {
mode: "chat"
format: "User: {input}\nAssistant:"
exit_command: "/exit"
params {
temperature: 0.7
max_length: 120
top_p: 0.9
beams: 2
do_sample: true
}
CONTROL {
IF confidence < 0.3 { RETRY }
IF hallucination_score > 0.5 { REPLACE WITH "I'm not certain about that." }
}
}
GUARD {
prevent {
hallucination
toxicity
bias
data_leak
unsafe_code
}
detect_using: ["classifier", "regex", "embedding"]
on_violation {
REPLACE
with_message: "Sorry, this request is not allowed."
}
}
SECURITY {
input_validation {
max_length: 500
disallow_patterns: [
"<script>",
"DROP TABLE",
"rm -rf"
]
}
output_validation {
prevent_data_leak: true
mask_personal_info: true
}
rate_limit {
max_requests_per_minute: 60
}
encryption {
algorithm: "AES-256"
}
}
EXPORT {
format: ["okm", "onnx", "gguf"]
path: "export/"
}
DEPLOY {
target: "api"
host: "0.0.0.0"
endpoint: "/chatbot"
requires_auth: true
port: 9000
max_concurrent_requests: 100
protocol: "http"
format: "onnx"
}
|