merged-3-2-llama.ipynb

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    "%%capture\n",
    "%pip install -U bitsandbytes\n",
    "%pip install transformers==4.44.2\n",
    "%pip install -U accelerate\n",
    "%pip install -U peft\n",
    "%pip install -U trl"
   ]
  },
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     "text": [
      "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
      "Token is valid (permission: fineGrained).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "from huggingface_hub import login\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "user_secrets = UserSecretsClient()\n",
    "\n",
    "hf_token = user_secrets.get_secret(\"HUGGINGFACE_TOKEN\")\n",
    "login(token = hf_token)"
   ]
  },
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   "source": [
    "base_model_url = \"meta-llama/Llama-3.2-3B-Instruct\"\n",
    "new_model_url = \"yuva44/llama-3.2-3b-it-Ecommerce-ChatBot\""
   ]
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   ],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, pipeline\n",
    "from peft import PeftModel\n",
    "import torch\n",
    "from trl import setup_chat_format\n",
    "# Reload tokenizer and model\n",
    "tokenizer = AutoTokenizer.from_pretrained(base_model_url)\n",
    "\n",
    "base_model_reload= AutoModelForCausalLM.from_pretrained(\n",
    "    base_model_url,\n",
    "    low_cpu_mem_usage=True,\n",
    "    return_dict=True,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=\"auto\",\n",
    ")"
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   "source": [
    "# Merge adapter with base model\n",
    "base_model_reload, tokenizer = setup_chat_format(base_model_reload, tokenizer)\n",
    "model = PeftModel.from_pretrained(base_model_reload, new_model_url)\n",
    "\n",
    "model = model.merge_and_unload()"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "\n",
      "Sure! I understand your need to explore the various payment methods we accept. Let me guide you through the different options available to you:\n",
      "\n",
      "1. **Credit/Debit Card**: We accept major credit and debit cards like Visa, Mastercard, and American Express.\n",
      "2. **PayPal**: You can make secure payments using your PayPal account.\n",
      "3. **Bank Transfer**: If you prefer a more traditional payment method, you can make a direct bank transfer.\n",
      "4. **Apple Pay**: For Apple users, we offer the convenience of Apple Pay for a seamless payment experience.\n",
      "5. **Google Pay**: Android users can also enjoy the ease of making payments with Google Pay.\n",
      "\n",
      "These are just a few of the payment methods we offer. If you have\n"
     ]
    }
   ],
   "source": [
    "instruction = \"\"\"You are a top-rated customer service agent named John. \n",
    "    Be polite to customers and answer all their questions.\n",
    "    \"\"\"\n",
    "\n",
    "messages = [{\"role\": \"system\", \"content\": instruction},\n",
    "    {\"role\": \"user\", \"content\": \"I have to see what payment payment modalities are accepted\"}]\n",
    "\n",
    "prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "    \n",
    "inputs = tokenizer(prompt, return_tensors='pt', padding=True, truncation=True).to(\"cuda\")\n",
    "\n",
    "outputs = model.generate(**inputs, max_new_tokens=150, num_return_sequences=1)\n",
    "\n",
    "text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "print(text.split(\"assistant\")[1])"
   ]
  },
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   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "import os\n",
    "api = HfApi()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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     "status": "failed"
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   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'repo_id' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[8], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m api\u001b[38;5;241m.\u001b[39mcreate_repo(\n\u001b[0;32m----> 2\u001b[0m     repo_id\u001b[38;5;241m=\u001b[39m\u001b[43mrepo_id\u001b[49m,\n\u001b[1;32m      3\u001b[0m     token\u001b[38;5;241m=\u001b[39mhf_token,\n\u001b[1;32m      4\u001b[0m     private\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[1;32m      5\u001b[0m     exist_ok\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      8\u001b[0m \u001b[38;5;66;03m# Save model and tokenizer locally\u001b[39;00m\n\u001b[1;32m      9\u001b[0m local_path \u001b[38;5;241m=\u001b[39m model_name\n",
      "\u001b[0;31mNameError\u001b[0m: name 'repo_id' is not defined"
     ]
    }
   ],
   "source": [
    "api.create_repo(\n",
    "    repo_id=repo_id,\n",
    "    token=hf_token,\n",
    "    private=False,\n",
    "    exist_ok=True\n",
    ")\n",
    "\n",
    "# Save model and tokenizer locally\n",
    "local_path = model_name\n",
    "model.save_pretrained(local_path, safe_serialization=True)\n",
    "tokenizer.save_pretrained(local_path)"
   ]
  },
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   "outputs": [],
   "source": [
    "api.upload_folder(\n",
    "    folder_path=local_path,\n",
    "    repo_id=repo_id,\n",
    "    repo_type=\"model\",\n",
    "    token=hf_token,\n",
    "    commit_message=\"Upload model and tokenizer files\"\n",
    ")\n",
    "\n",
    "print(f\"Model successfully uploaded to https://huggingface.co/{repo_id}\")"
   ]
  }
 ],
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