Commit config to generate training data

promethean-config.py

@@ -0,0 +1,59 @@
+from datasets import load_dataset
+from promethean.datasets import hub_prompts, HubSplit, Dataset, Prompts
+from promethean.extract import Extractor, ClientOpts
+from promethean.lora import LoraSettings
+import os
+
+output_dir="output"
+uncensor_ds_name = "Guilherme34/uncensor"
+uncensor_ds = load_dataset(uncensor_ds_name, split="train")
+def uncensor_items():
+    for row in uncensor_ds:
+        for message in row["messages"]:
+            if message["role"] == "user":
+                yield message["content"]
+                break
+
+extractor = Extractor(
+    teacher="hf:mlabonne/Llama-3.1-70B-Instruct-lorablated",
+    max_concurrent=8,
+    output_dir=output_dir,
+    client_opts=ClientOpts(
+        base_url="https://glhf.chat/api/openai/v1",
+        api_key=os.environ["GLHF_API_KEY"],
+    ),
+    dataset=Dataset(
+        train=[
+            Prompts(
+                output_path=f"hub/{uncensor_ds_name}.jsonl",
+                count=lambda: len(uncensor_ds),
+                items=uncensor_items,
+            ),
+            hub_prompts(
+                name="mlabonne/harmful_behaviors",
+                text_field="text",
+                split=HubSplit(name="train"),
+            ),
+        ],
+        eval=[
+            hub_prompts(
+                name="mlabonne/harmful_behaviors",
+                text_field="text",
+                split=HubSplit(name="test"),
+            ),
+        ],
+    ),
+)
+
+lora_settings = LoraSettings(
+    lora_r=32,
+    lora_alpha=16,
+    lora_dropout=0.01,
+    num_epochs=2,
+    learning_rate=4e-4,
+    warmup_steps=10,
+)
+axolotl_config = lora_settings.llama_70b_axolotl(extractor.output_dataset())
+
+extractor.run()
+axolotl_config.save(output_dir)