Added prompt based testing of text generation models

tests/baselines/bloomz_7b1.json

@@ -0,0 +1,12 @@
+{
+    "prompt": {
+        "model_name_or_path": "/root/software/data/pytorch/bloom/models--bigscience--bloom-7b1/snapshots/e83e90ba86f87f74aa2731cdab25ccf33976bd66/",
+        "max_new_tokens": 1024,
+        "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve",
+        "distribution": {
+            "single_card": {
+                "throughput": 31.06
+            }
+        }
+    }
+}

tests/baselines/llama_2_13b_hf.json

@@ -0,0 +1,12 @@
+{
+    "prompt": {
+        "model_name_or_path": "/root/software/data/pytorch/llama-v2/test-01/13B-hf",
+        "max_new_tokens": 1024,
+        "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve",
+        "distribution": {
+            "single_card": {
+                "throughput": 70.60
+            }
+        }
+    }
+}