Bobbie Model Webeweb Set 02rar Hot !!better!! 🎁 Recent

| Stage | Command / Code | Notes | |-------|----------------|-------| | | data/ folder with train.jsonl where each line is "prompt":"…", "completion":"…" | Keep the style consistent; 5 k‑10 k examples are a good starting point. | | Create a tokenizer (if needed) | python -m transformers.tools.convert_tokenizer --model_name_or_path bobbie_model/tokenizer --save_directory ./my_tokenizer | Skip if you reuse the original tokenizer. | | Launch a Trainer (HuggingFace Trainer works for most causal models) | python - <<EOF\nfrom transformers import Trainer, TrainingArguments, AutoModelForCausalLM, AutoTokenizer\nmodel = AutoModelForCausalLM.from_pretrained('bobbie_model', torch_dtype=torch.float16).to(DEVICE)\n tokenizer = AutoTokenizer.from_pretrained('bobbie_model')\ntrain_dataset = load_dataset('json', data_files='data/train.jsonl')['train']\nargs = TrainingArguments(output_dir='bobbie_finetuned',\n per_device_train_batch_size=4,\n gradient_accumulation_steps=4,\n num_train_epochs=3,\n learning_rate=5e-5,\n fp16=True)\ntrainer = Trainer(model=model, args=args, train_dataset=train_dataset, tokenizer=tokenizer)\ntrainer.train()\nEOF | Adjust batch size, epochs, and LR to fit your GPU memory. | | Save & Test | trainer.save_model('bobbie_finetuned/') then use the same inference script with MODEL_DIR='bobbie_finetuned' . | Verify that the output now reflects your brand’s voice. |

# Option B – Custom class (uncomment & adapt) # model = BobbieVisionLanguageModel.from_pretrained( # checkpoint_path=WEIGHTS, # config_path=CONFIG_JSON, # device=DEVICE # ) bobbie model webeweb set 02rar hot