mistral-7b-openorca-cot-4bit

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from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig from peft import PeftModel import torch

repo_name = "your_username/mistral-7b-openorca-cot-4bit" # Replace with your repo name base_model_name = "mistralai/Mistral-7B-v0.1"

Load the tokenizer

tokenizer = AutoTokenizer.from_pretrained(repo_name)

Load the base model with 4-bit quantization

bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True ) base_model = AutoModelForCausalLM.from_pretrained( base_model_name, quantization_config=bnb_config, device_map="auto", trust_remote_code=True )

Load the LoRA adapter

model = PeftModel.from_pretrained(base_model, repo_name)

Inference (same as before)

model.eval() prompt = "<s>[INST] Explain the process of photosynthesis in simple terms. [/INST]" inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=150, eos_token_id=tokenizer.eos_token_id) response = tokenizer.decode(outputs[0], skip_special_tokens=True) print("\n--- Inference Example ---") print(response)

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