RAFT updates, BERTopic config, cleanup

raft/train_mistral_raft.py

@@ -1,5 +1,4 @@
 import argparse
-import json
 import os
 
 import torch
@@ -12,7 +11,7 @@ from trl import SFTConfig, SFTTrainer
 def main():
     ap = argparse.ArgumentParser()
     ap.add_argument("--train_jsonl", default="out/raft_train.jsonl")
-    ap.add_argument("--base_model", default="mistralai/Mistral-7B-Instruct-v0.3")
+    ap.add_argument("--base_model", default="mistralai/Mistral-7B-Instruct-v0.2")
     ap.add_argument("--out_dir", default="out/mistral_balitwin_lora")
     ap.add_argument("--max_seq_len", type=int, default=2048)
     ap.add_argument("--batch_size", type=int, default=1)
@@ -23,7 +22,7 @@ def main():
 
     os.makedirs(args.out_dir, exist_ok=True)
 
-    # QLoRA (4-bit) config (good default for 7B on limited VRAM)
+    # QLoRA (4-bit) config
     bnb_config = BitsAndBytesConfig(
         load_in_4bit=True,
         bnb_4bit_quant_type="nf4",
@@ -34,7 +33,6 @@ def main():
     )
 
     tokenizer = AutoTokenizer.from_pretrained(args.base_model, use_fast=True)
-    # Mistral usually has a valid chat template; keep it intact. :contentReference[oaicite:9]{index=9}
 
     model = AutoModelForCausalLM.from_pretrained(
         args.base_model,
@@ -43,7 +41,7 @@ def main():
         torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float16,
     )
 
-    # LoRA adapter config (tweak r/alpha if needed)
+    # LoRA adapter config
     peft_config = LoraConfig(
         r=16,
         lora_alpha=32,
@@ -75,7 +73,7 @@ def main():
         max_length=args.max_seq_len,
         bf16=torch.cuda.is_available(),
         fp16=not torch.cuda.is_available(),
-        assistant_only_loss=True,  # only learn from assistant turns in messages :contentReference[oaicite:10]{index=10}
+        assistant_only_loss=True,  # only learn from assistant turns in messages
         report_to=[],
     )
 
@@ -91,7 +89,7 @@ def main():
     trainer.save_model(args.out_dir)
     tokenizer.save_pretrained(args.out_dir)
 
-    print(f"Saved LoRA adapter to: {args.out_dir}")
+    print(f"Fertig! LoRA-Adapter gespeichert: {args.out_dir}")
 
 
 if __name__ == "__main__":