diff --git "a/Inference_JaisMerge.ipynb" "b/Inference_JaisMerge.ipynb" new file mode 100644--- /dev/null +++ "b/Inference_JaisMerge.ipynb" @@ -0,0 +1,2782 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n", + "import torch\n", + "\n", + "# Model and message setup\n", + "model_name = \"Solshine/Jais-590m-merged\"\n", + "user_message = \"What do you need to train a large language model?\" # This can be any user input\n", + "\n", + "# Structure the message with role-content pairing for compatibility with Jais-chat format\n", + "messages = [{\"role\": \"user\", \"content\": user_message}]\n", + "\n", + "# Initialize tokenizer with trust_remote_code for custom Arabic-English handling\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", + "\n", + "# Check if tokenizer is valid\n", + "if tokenizer is None:\n", + " raise ValueError(\"Tokenizer initialization failed!\")\n", + "\n", + "# Custom chat template including assistant role\n", + "def custom_chat_template(messages):\n", + " chat_prompt = \"\"\n", + " for message in messages:\n", + " role = message[\"role\"]\n", + " content = message[\"content\"]\n", + " chat_prompt += f\"{role}: {content}\\n\"\n", + " # Add assistant role to prompt the model's response\n", + " chat_prompt += \"assistant:\"\n", + " return chat_prompt\n", + "\n", + "# Generate the prompt\n", + "prompt = custom_chat_template(messages)\n", + "print(f\"Generated prompt:\\n{prompt}\")\n", + "\n", + "# Initialize the model\n", + "model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)\n", + "if model is None:\n", + " raise ValueError(\"Model initialization failed!\")\n", + "\n", + "# Move model to the appropriate device\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device)\n", + "\n", + "# Initialize the text generation pipeline\n", + "text_gen_pipeline = pipeline(\n", + " \"text-generation\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " device=device,\n", + " torch_dtype=torch.float16,\n", + " trust_remote_code=True\n", + ")\n", + "\n", + "# Generate text\n", + "try:\n", + " outputs = text_gen_pipeline(\n", + " prompt,\n", + " max_new_tokens=256,\n", + " do_sample=True,\n", + " temperature=0.7,\n", + " top_k=50,\n", + " top_p=0.95,\n", + " pad_token_id=tokenizer.eos_token_id # Ensure proper stopping\n", + " )\n", + " # Extract and print the assistant's response\n", + " generated_text = outputs[0][\"generated_text\"]\n", + " assistant_response = generated_text.split(\"assistant:\")[1].strip()\n", + " print(f\"Assistant's response:\\n{assistant_response}\")\n", + "except Exception as e:\n", + " print(f\"Error during text generation: {e}\")\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_AtEETxb-MUF", + "outputId": "5dcc1dfc-741c-491a-9423-b31647961126" + }, + "execution_count": 11, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Generated prompt:\n", + "user: What do you need to train a large language model?\n", + "assistant:\n", + "Assistant's response:\n", + "I need to train a model to recognize 10 different languages.\n", + "\n", + "How can I do this?\n", + "\n", + "A:\n", + "\n", + "How can I do this?\n", + "\n", + "You could do this in two ways:\n", + "\n", + "Create a trained model using the provided source data (and the data it produces is not in your control)\n", + "Create a trained model using a different source data (and the data it produces is in your control)\n", + "\n", + "The first way is much easier to implement than the second. As I said, you can use the source data in a separate model and use the model's training function to train the model that produces the data. I'm not sure if this is what you want or not, but it's possible.\n", + "\n", + "A:\n", + "\n", + "If you are training a model for 10 different languages, then you will need to train a model that recognizes 10 different languages. \n", + "This is possible, but it is not easy.\n", + "You can train a model for a specific language, say English, by training a model for that language. Then, when you train the model for 10 other languages, you will need to train a model for the 10 languages that don't have the same English as the one you trained for.\n", + "This is what\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "The following is retained for scientific interest, as it prints a large series of tensors and some tensor operations and responds as if to a different prompt (perhaps could have research potential):\n", + "\n", + "```\n", + "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n", + "import torch\n", + "\n", + "# Model and message setup\n", + "model_name = \"Solshine/Jais-590m-merged\"\n", + "messages = [{\"role\": \"user\", \"content\": \"Explain how transformers work in machine learning\"}]\n", + "\n", + "# Initialize tokenizer with trust_remote_code for custom Arabic-English handling\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n", + "\n", + "# Check if tokenizer is valid\n", + "if tokenizer is None:\n", + " raise ValueError(\"Tokenizer initialization failed!\")\n", + "\n", + "# Test simple tokenization\n", + "test_prompt = \"This is a test prompt.\"\n", + "tokenized_prompt = tokenizer(test_prompt, return_tensors=\"pt\")\n", + "print(f\"Tokenized output: {tokenized_prompt}\")\n", + "\n", + "# Custom chat template for structuring messages\n", + "def custom_chat_template(messages):\n", + " chat_prompt = \"\"\n", + " for message in messages:\n", + " role = message[\"role\"]\n", + " content = message[\"content\"]\n", + " chat_prompt += f\"{role}: {content}\\n\"\n", + " return chat_prompt\n", + "\n", + "# Generate the prompt manually from structured messages\n", + "prompt = custom_chat_template(messages)\n", + "print(f\"Generated prompt: {prompt}\")\n", + "\n", + "# Initialize the model manually\n", + "model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)\n", + "\n", + "# Check if model is valid\n", + "if model is None:\n", + " raise ValueError(\"Model initialization failed!\")\n", + "\n", + "# Test a forward pass with the model to see if outputs are returned correctly\n", + "try:\n", + " input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n", + " outputs = model(input_ids)\n", + " print(outputs)\n", + "except Exception as e:\n", + " print(f\"Error during model forward pass: {e}\")\n", + "\n", + "# If forward pass succeeds, use the pipeline for text generation\n", + "text_gen_pipeline = pipeline(\n", + " \"text-generation\",\n", + " model=model,\n", + " tokenizer=tokenizer,\n", + " torch_dtype=torch.float16,\n", + " device_map=\"auto\",\n", + " trust_remote_code=True\n", + ")\n", + "\n", + "# Execute text generation using the prompt\n", + "try:\n", + " outputs = text_gen_pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)\n", + " print(outputs[0][\"generated_text\"])\n", + "except Exception as e:\n", + " print(f\"Error during text generation: {e}\")\n", + "\n", + "```\n", + "\n", + "Returned:\n", + "```\n", + "Tokenized output: {'input_ids': tensor([[ 2883, 455, 321, 1978, 18071, 14]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1]])}\n", + "Generated prompt: user: Explain how transformers work in machine learning\n", + "\n", + "Hardware accelerator e.g. GPU is available in the environment, but no `device` argument is passed to the `Pipeline` object. Model will be on CPU.\n", + "CausalLMOutputWithCrossAttentions(loss=None, logits=tensor([[[ 1.4381, 2.8144, 5.6775, ..., -5.8403, -3.5928, -7.3339],\n", + " [ 2.1716, 0.4066, -0.3233, ..., -3.5967, -2.3428, -6.4091],\n", + " [ 1.5052, 3.4263, 2.3469, ..., 0.4482, -3.6540, -2.2990],\n", + " ...,\n", + " [ 3.6780, 2.7022, 4.3952, ..., -10.3083, -0.6451, -8.5470],\n", + " [ 5.5732, 5.2990, 6.6358, ..., -12.6449, -3.6089, -13.1777],\n", + " [ 0.6401, 5.1542, 4.3479, ..., -4.2185, -5.2455, -5.9905]]],\n", + " grad_fn=), past_key_values=((tensor([[[[-2.0959e+00, 1.4371e+00, 2.6447e+00, ..., -1.9006e+00,\n", + " 1.6817e-01, -1.3891e-01],\n", + " [ 1.8986e+00, -3.8827e+00, 3.0188e+00, ..., 7.5139e-02,\n", + " 5.1403e+00, 1.3825e+00],\n", + " [ 2.3830e+00, -1.7409e+00, 1.2259e+00, ..., -1.6876e+00,\n", + " 1.5156e+00, -1.0992e+00],\n", + " ...,\n", + " [-4.7027e-01, -2.1040e+00, -5.6962e-01, ..., -1.8081e+00,\n", + " 6.8220e-02, -2.5923e-01],\n", + " [-1.2099e+00, 2.3139e+00, 7.9305e-01, ..., -2.4751e-01,\n", + " -1.7727e+00, 1.6477e-01],\n", + " [-3.5747e-01, -8.7559e-01, 1.7207e+00, ..., -1.2237e+00,\n", + " -2.3608e+00, -5.4497e-01]],\n", + "\n", + " [[ 1.1596e-01, 6.4766e-01, -5.6451e-01, ..., -6.9970e-01,\n", + " 2.3296e+00, -3.4225e-01],\n", + " [ 8.4867e-01, 3.9504e-01, 1.7041e+00, ..., 3.6139e-01,\n", + " -2.4949e+00, 1.5250e+00],\n", + " [ 2.4554e-01, 2.2534e+00, 8.8036e-01, ..., 1.8584e-01,\n", + " -3.2067e+00, 9.0587e-02],\n", + " ...,\n", + " [-2.4442e+00, 3.1507e+00, -2.0643e+00, ..., 3.7998e+00,\n", + " -7.9083e-01, -7.6744e-02],\n", + " [ 1.7323e-01, -1.7960e+00, -6.3591e-01, ..., 1.7554e+00,\n", + " 4.2594e-01, -2.6391e+00],\n", + " [ 1.1166e+00, 2.3311e-02, 1.1413e-01, ..., 4.1283e-01,\n", + " 6.9779e-01, 5.8137e-01]],\n", + "\n", + " [[ 1.9033e+00, 6.5698e+00, -4.1076e-01, ..., -6.3750e-01,\n", + " -1.8385e+00, -7.8635e-01],\n", + " [-4.9120e-01, -1.0998e+00, -1.9142e+00, ..., 1.6793e-01,\n", + " 3.1094e-02, -6.9535e-01],\n", + " [ 1.0240e+00, -1.9203e+00, 2.0203e+00, ..., -2.0730e+00,\n", + " 6.6193e-01, -2.4453e+00],\n", + " ...,\n", + " [-1.5037e+00, 1.1116e+00, 3.8565e-01, ..., 1.5658e+00,\n", + " -1.5497e+00, -9.2532e-01],\n", + " [-2.5657e+00, -2.8893e+00, -1.4844e+00, ..., 2.1481e-01,\n", + " -2.2691e+00, 4.1295e+00],\n", + " [-3.2430e-01, -5.2063e-01, -4.1375e-01, ..., -6.0822e-01,\n", + " 1.8253e-01, -1.0165e+00]],\n", + "\n", + " ...,\n", + "\n", + " [[ 1.0998e+00, -2.8009e+00, 4.1890e-01, ..., 7.7646e-01,\n", + " 1.8189e+00, -1.8591e+00],\n", + " [ 7.7233e-01, -7.2483e-01, 1.0653e-01, ..., -1.8673e+00,\n", + " 7.0754e-01, -3.8661e-01],\n", + " [ 6.4154e+00, 8.9653e-01, -6.9185e-01, ..., -7.6714e+00,\n", + " 2.3530e+00, -6.7575e-01],\n", + " ...,\n", + " [ 1.4332e+00, 2.1372e+00, 2.3786e+00, ..., -4.0503e-01,\n", + " 1.2687e+00, 1.1465e+00],\n", + " [ 1.1003e+00, -7.8606e+00, -1.1714e+00, ..., -1.3486e+00,\n", + " 3.3691e+00, 3.7973e+00],\n", + " [ 1.0350e-01, -4.5418e-01, -9.3553e-02, ..., -1.7816e+00,\n", + " 1.6643e+00, -2.9312e-01]],\n", + "\n", + " [[-6.7624e-01, 1.1944e+00, -2.1212e+00, ..., 6.3293e-01,\n", + " 2.2173e+00, 2.3537e+00],\n", + " [ 2.2979e-01, 1.0303e+00, 1.5898e+00, ..., 1.1350e+00,\n", + " 9.6039e-01, 4.4299e-01],\n", + " [-2.5542e-01, 2.3424e+00, 1.6710e+00, ..., -3.7434e+00,\n", + " 8.6190e-01, 3.6006e-02],\n", + " ...,\n", + " [-2.2991e+00, 2.6369e+00, 2.0235e+00, ..., -1.1271e+00,\n", + " 1.2475e+00, -7.5392e-01],\n", + " [-9.0163e-01, 5.8975e-01, 9.0165e-01, ..., 4.6972e-01,\n", + " 2.5632e+00, 7.9832e-01],\n", + " [-1.1616e+00, -3.3571e-01, 6.8647e-01, ..., -2.1542e-01,\n", + " 3.1655e-01, 4.0013e+00]],\n", + "\n", + " [[ 3.6125e-01, -2.1739e+00, 1.0388e+00, ..., 2.9361e-03,\n", + " -1.6513e+00, 1.4179e+00],\n", + " [-4.2136e-03, 3.8282e-01, 3.0475e+00, ..., 1.1573e+00,\n", + " 3.0323e-01, 7.3041e-01],\n", + " [-1.0284e+00, 4.1546e-01, 2.1133e+00, ..., 5.1776e-01,\n", + " 3.4128e+00, 6.9672e-01],\n", + " ...,\n", + " [-6.3754e-01, -1.8174e+00, 1.4696e-01, ..., 1.2360e-01,\n", + " -1.5590e+00, 1.1297e+00],\n", + " [ 3.2085e-01, -4.9139e-01, -2.0590e+00, ..., 1.1270e+00,\n", + " -1.7491e-01, -3.5091e-01],\n", + " [ 6.5438e-03, 2.3912e-01, -1.2407e+00, ..., -9.1464e-01,\n", + " -1.9471e-01, -5.8140e-01]]]], grad_fn=), tensor([[[[-1.7722e-01, -7.5062e-01, 1.2471e+00, ..., 7.2505e-01,\n", + " -4.5737e-01, 7.3375e-01],\n", + " [ 3.5285e-02, -1.7030e-03, -1.5364e-01, ..., 4.2070e-01,\n", + " 9.1569e-01, -9.9045e-02],\n", + " [-2.9776e-01, 6.7802e-01, -2.2362e-01, ..., 8.1722e-01,\n", + " -4.2224e-01, -1.3325e-01],\n", + " ...,\n", + " [ 1.6811e-01, -3.7726e-02, -2.8807e-01, ..., 8.7379e-01,\n", + " 8.5474e-01, 2.3332e-01],\n", + " [-4.0813e-01, 1.1733e+00, 1.1222e+00, ..., -6.1166e-01,\n", + " -3.6553e-01, -5.9500e-03],\n", + " [ 1.1505e-01, 3.8303e-01, 2.8465e-01, ..., 1.2986e-01,\n", + " -1.8902e-01, 2.5937e-02]],\n", + "\n", + " [[-2.7348e-01, -4.2412e-02, 2.2548e-01, ..., 4.8838e-01,\n", + " -2.2269e-01, 4.8359e+00],\n", + " [-2.8123e-02, 9.4699e-02, 1.2933e-01, ..., -1.0129e-01,\n", + " -3.5998e-02, -3.3200e+00],\n", + " [-6.5313e-01, 4.5089e-01, -2.1384e-02, ..., -5.4310e-01,\n", + " 5.7361e-01, -2.0281e+00],\n", + " ...,\n", + " [-3.2258e-01, 4.3000e-02, -1.6003e-01, ..., -1.2903e-01,\n", + " -7.8107e-01, 1.4752e+00],\n", + " [-4.3784e-01, -2.1444e-01, -1.8129e-01, ..., 2.7558e-01,\n", + " -2.2486e-01, 1.5549e+00],\n", + " [ 1.4645e-01, 1.2385e-02, -7.2829e-02, ..., 2.0373e-01,\n", + " -3.5118e-02, -3.3530e+00]],\n", + "\n", + " [[ 3.1843e-03, -4.7337e-01, -4.1628e-02, ..., 4.2861e-01,\n", + " 3.6663e-01, 2.8123e-02],\n", + " [ 1.7046e-01, -2.9456e-01, -4.5556e-01, ..., -4.1222e-02,\n", + " 1.1345e-01, 8.3244e-02],\n", + " [-7.2180e-01, 1.0595e+00, -3.4917e-01, ..., -2.4321e-01,\n", + " -9.3386e-01, -2.8490e-02],\n", + " ...,\n", + " [ 2.6423e-01, -1.5209e-01, -7.5666e-01, ..., 4.8821e-02,\n", + " 2.8479e-01, 5.0622e-01],\n", + " [-1.5765e-01, -6.9506e-01, -8.4984e-01, ..., 7.0024e-02,\n", + " -6.9823e-02, -2.3585e-01],\n", + " [-1.1093e-01, 3.7112e-03, -2.6220e-01, ..., -4.7609e-02,\n", + " 5.5389e-02, 4.0055e-03]],\n", + "\n", + " ...,\n", + "\n", + " [[ 4.0521e-01, 2.0802e-01, -3.2607e-01, ..., 1.1893e+00,\n", + " 5.2978e-01, -1.3754e+00],\n", + " [ 2.7671e-02, 5.2713e-02, -1.9272e-01, ..., -1.6999e-01,\n", + " 1.4314e-01, 4.0836e-02],\n", + " [ 4.9556e-01, 7.4002e-01, -5.3053e-01, ..., -2.8131e-01,\n", + " -8.8308e-02, 1.2126e+00],\n", + " ...,\n", + " [ 3.4379e-03, -5.3727e-01, -4.8390e-01, ..., 2.7330e-02,\n", + " -2.0382e+00, -5.9170e-01],\n", + " [ 4.5388e-01, 3.4651e-01, 4.7605e-01, ..., 7.5199e-01,\n", + " -9.8493e-02, 8.0423e-01],\n", + " [-1.7376e-01, 1.9928e-01, -6.1178e-02, ..., 1.7092e-01,\n", + " 4.2461e-02, -1.1678e-01]],\n", + "\n", + " [[ 3.3922e-01, -6.7137e-01, -2.9993e-01, ..., -4.3287e-01,\n", + " 2.9316e-01, -1.6222e-01],\n", + " [-2.7066e-02, 3.3367e-01, -2.1537e-01, ..., -2.7477e-02,\n", + " -3.6513e-01, 9.2140e-02],\n", + " [ 8.2060e-01, 7.7214e-01, 1.3224e+00, ..., -1.1097e+00,\n", 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1.8218e+00]]]], grad_fn=)), (tensor([[[[ 7.0487, -3.7967, -1.2482, ..., 4.6963, -2.4953, 2.7542],\n", + " [ 1.4861, -3.0416, -1.9340, ..., 2.4253, -1.6048, 3.5681],\n", + " [ 2.7856, -3.6501, 0.3291, ..., -2.4429, -1.7826, -5.0351],\n", + " ...,\n", + " [ 0.1303, 0.8540, -6.2618, ..., -3.0420, -6.7278, 1.7225],\n", + " [ 0.5830, 1.4434, -3.9872, ..., -1.1438, -5.8263, -4.1240],\n", + " [ 1.2076, -5.9053, 0.1566, ..., 0.3718, -4.8597, -2.9647]],\n", + "\n", + " [[ 2.6820, -0.7584, 1.8520, ..., 1.7389, 8.9374, -5.7469],\n", + " [ 4.1453, -2.9870, 4.1253, ..., 1.8786, 10.1109, -5.4388],\n", + " [ 1.8921, 3.7793, 4.6014, ..., 0.1691, 8.0833, -4.4128],\n", + " ...,\n", + " [ -2.7595, -4.0721, -0.5622, ..., 3.0032, 6.7185, -5.0222],\n", + " [ -0.0991, 2.9769, -0.1570, ..., 6.9956, 2.9741, -5.4409],\n", + " [ 1.0835, 0.8875, 3.9177, ..., -1.4409, 8.2249, -7.9429]],\n", + "\n", + " [[ 8.6201, -1.1019, 2.0970, ..., 0.9597, -1.7780, 0.2651],\n", + " [ 1.5133, -0.7364, 1.9729, ..., -2.1197, 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..., 1.0022e+00,\n", + " -4.3106e+00, -5.3493e+00],\n", + " [ 1.1990e+00, 2.1105e+00, 5.6925e+00, ..., -1.1562e+00,\n", + " -1.1074e+01, 6.8826e+00],\n", + " ...,\n", + " [-3.8100e+00, -5.4378e-01, -1.5964e+00, ..., -4.9590e+00,\n", + " -4.0523e+00, -1.0648e+00],\n", + " [-6.7710e-01, 3.1329e+00, -2.1364e+00, ..., -4.2632e+00,\n", + " -2.0440e+00, 5.7333e+00],\n", + " [ 1.4246e+00, 1.6135e+00, -3.3376e+00, ..., -2.0743e+00,\n", + " -1.1537e+00, 3.0445e+00]],\n", + "\n", + " ...,\n", + "\n", + " [[ 6.3180e-01, -4.0086e-02, 8.2667e-01, ..., -2.4852e-01,\n", + " 8.6989e-01, 5.9128e-01],\n", + " [ 3.8711e+00, 6.6113e+00, 1.3583e+00, ..., -2.1021e+00,\n", + " -2.6829e+00, -5.0486e-01],\n", + " [-6.2327e+00, -7.2837e-04, 5.3459e-01, ..., -1.9899e+00,\n", + " -5.7745e+00, 3.0020e+00],\n", + " ...,\n", + " [-3.0400e-01, 3.8460e+00, -2.0601e+00, ..., 4.3909e+00,\n", + " 7.3954e+00, 5.5978e+00],\n", + " [-1.8946e+00, -4.5076e-02, -4.9969e+00, ..., -3.3246e-01,\n", + " -3.0848e+00, 1.2372e-01],\n", + " [-2.2662e+00, 8.2540e-01, 1.7064e+00, ..., 1.2472e+00,\n", + " -1.2903e+00, -1.6899e-01]],\n", + "\n", + " [[-8.2854e-01, -6.4474e-01, -4.2275e+00, ..., -6.2586e-01,\n", + " -3.9464e-02, 7.9444e-02],\n", + " [ 1.3858e+00, -9.5476e-01, 3.0804e-01, ..., 2.6732e+00,\n", + " 3.4749e-01, 4.6335e-01],\n", + " [ 4.2968e+00, 1.1857e+00, -2.0413e-01, ..., -7.1986e-01,\n", + " 2.3442e+00, 6.7370e+00],\n", + " ...,\n", + " [ 1.2540e+00, -8.4921e-01, -2.3424e-01, ..., 6.7792e+00,\n", + " 3.5434e-01, -7.2555e+00],\n", + " [-3.9043e+00, 2.7737e+00, -5.0697e+00, ..., 3.0143e+00,\n", + " 6.0278e+00, -3.2343e+00],\n", + " [-9.2953e-01, -1.9789e-02, -1.5562e+00, ..., 9.0401e-01,\n", + " 2.4637e-01, -3.0282e-01]],\n", + "\n", + " [[ 5.0030e-01, -1.6396e-01, 5.3002e-01, ..., 1.3563e-01,\n", + " -4.9493e-02, -2.3070e-01],\n", + " [ 6.8965e-01, -2.7964e+00, -1.4698e-01, ..., -2.2025e+00,\n", + " 1.3924e+00, 2.8182e+00],\n", + " [-5.8953e-01, 4.5176e+00, -5.6953e+00, ..., 1.0517e+00,\n", + " -4.3820e+00, 1.0718e+01],\n", + " ...,\n", + " [-4.3672e+00, -3.4756e+00, 6.9281e+00, ..., 2.8438e+00,\n", + " -1.2872e+00, 5.6297e+00],\n", + " [ 1.2491e+00, 7.5083e-01, -2.4921e+00, ..., -2.5765e+00,\n", + " 2.1224e-01, 2.1005e+00],\n", + " [ 1.8954e+00, -8.4113e-01, 2.3986e+00, ..., 5.8147e-03,\n", + " 1.6109e-01, -2.4565e-01]]]], grad_fn=))), hidden_states=None, attentions=None, cross_attentions=None)\n", + "user: Explain how transformers work in machine learning\n", + "\n", + "I am using the following code to create a model in Python using the Tensorflow TensorFlow library. I have a model which is trained on an external dataset and I have a model which is trained on an internal dataset. My question is, what is the most efficient way to save the model's parameters in a different folder or to use the model from another machine?\n", + "# Create model\n", + "from tensorflow.keras.models import Model\n", + "from tensorflow.keras.layers import Dense, Dropout, Activation\n", + "import os\n", + "\n", + "# Load model\n", + "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n", + "from tensorflow.keras.preprocessing.image import image_data\n", + "from tensorflow.keras.layers import Dense, Dropout, Activation\n", + "import os\n", + "\n", + "model_path = 'path/to/model'\n", + "data_path = 'path/to/data'\n", + "\n", + "# Load data\n", + "images = image_data.load_img(data_path, img_size=(640,480))\n", + "labels = image_data.load_label(data_path, labels_size=(1,20))\n", + "\n", + "model = Model(inputs=[images, labels\n", + "```" + ], + "metadata": { + "id": "6D3XOT8r__5N" + } + } + ] +} \ No newline at end of file