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1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3# Load the model
4model = AutoModelForCausalLM.from_pretrained("tkkagency/together-ft-model")
5tokenizer = AutoTokenizer.from_pretrained("tkkagency/together-ft-model")
6
7# Generate text
8prompt = "Your prompt here"
9inputs = tokenizer(prompt, return_tensors="pt")
10outputs = model.generate(**inputs, max_new_tokens=200)
11print(tokenizer.decode(outputs[0], skip_special_tokens=True))1from transformers import AutoModelForCausalLM, AutoTokenizer
2from peft import PeftModel
3
4# Load base model (replace with actual base model)
5base_model_id = "meta-llama/Llama-2-7b-hf" # Update this!
6model = AutoModelForCausalLM.from_pretrained(base_model_id)
7tokenizer = AutoTokenizer.from_pretrained(base_model_id)
8
9# Load adapter
10model = PeftModel.from_pretrained(model, "tkkagency/together-ft-model")
11
12# Generate
13inputs = tokenizer("Your prompt", return_tensors="pt")
14outputs = model.generate(**inputs)
15print(tokenizer.decode(outputs[0]))