neuronxcc-2.15.128.0+56dc5a86/0_REGISTRY/0.0.25.dev0/training/llama/meta-llama/Llama-3.2-1B-Instruct/2b57fd9e716577f99cfd.json

{"architectures": ["LlamaForCausalLM"], "attention_bias": false, "attention_dropout": 0.0, "bos_token_id": 128256, "eos_token_id": 128257, "head_dim": 64, "hidden_act": "silu", "hidden_size": 2048, "initializer_range": 0.02, "intermediate_size": 8192, "max_position_embeddings": 131072, "mlp_bias": false, "model_type": "llama", "neuron": {"compiler_version": "2.15.128.0+56dc5a86", "input_specs": {"chosen": ["<|im_start|>user\nwhats the difference between blue green deployments and trunk based development?<|im_end|>\n<|im_start|>assistant\nBlue-green deployments and trunk-based development are two distinct strategies used in software development and deployment processes, each having its own set of benefits and use cases. Let's take a closer look at each strategy to understand their differences.\n\n**Blue-Green Deployments:**\n\nThis is a deployment strategy that aims to minimize downtime and reduce the risk associated with deploying new software versions. It works by having two identical production environments, called Blue and Green. At any given time, one of these environments is live and serves user traffic, while the other is idle.\n\nWhen deploying a new version of the software, the idle environment (say, Green) is updated, and thorough testing is performed. Once the new version is confirmed to be stable, traffic is gradually switched from the live environment (Blue) to the updated one (Green). If any issues arise during this switch, it's easy to revert back to the older version (Blue) without affecting the user experience. This strategy is known for its ability to facilitate quick rollbacks and minimize downtime during deployments.\n\n**Trunk-Based Development:**\n\nOn the other hand, trunk-based development is a software development practice that focuses on having developers work collaboratively on a single \"trunk\" or \"mainline\" branch within a version control system (like Git). This strategy encourages small, frequent code commits and aims to reduce the complexity of managing multiple branches for different features or releases.\n\nDevelopers working with trunk-based development follow two core principles \u2013 they merge their changes back into the trunk as soon as possible and keep the trunk stable at all times. This promotes transparency, allows early detection of integration issues, speeds up the overall development process and ultimately leads to faster releases.\n\nIn summary, blue-green deployments and trunk-based development serve different purposes in software development lifecycle. Blue-green deployments primarily address the deployment process, allowing for seamless, low-risk deployment of new software versions. Meanwhile, trunk-based development is a software development practice that aims to bring efficiency, collaboration, and transparency to the development process. When used together, these strategies can contribute to a more streamlined and effective software delivery pipeline.<|im_end|>\n"], "chosen_attention_mask": [1, 512], "chosen_input_ids": [1, 512], "chosen_labels": [1, 512], "prompt": ["whats the difference between blue green deployments and trunk based development?"], "prompt_attention_mask": [1, 512], "prompt_input_ids": [1, 512], "question": ["whats the difference between blue green deployments and trunk based development?"], "rejected": ["<|im_start|>user\nwhats the difference between blue green deployments and trunk based development?<|im_end|>\n<|im_start|>assistant\nTrunk-based development is a software development methodology in which developers continuously integrate changes into a shared repository, called the trunk, using a source control system. Blue-green deployment is a type of continuous delivery technique that ensures that new software deployments do not impact stability or availability of production systems. Instead, new code is deployed to a blue-green environment where it is thoroughly tested and monitored before being deployed to production.<|im_end|>\n"], "rejected_attention_mask": [1, 512], "rejected_input_ids": [1, 512], "rejected_labels": [1, 512], "source": ["sharegpt"]}, "model_class": "PeftModelForCausalLM", "num_neuron_cores_per_node": 2, "pipeline_parallel_size": 1, "precision": "bfloat16", "tensor_parallel_size": 1, "training": true}, "num_attention_heads": 32, "num_hidden_layers": 16, "num_key_value_heads": 8, "pad_token_id": 128257, "pretraining_tp": 1, "rms_norm_eps": 1e-05, "rope_scaling": {"factor": 32.0, "high_freq_factor": 4.0, "low_freq_factor": 1.0, "original_max_position_embeddings": 8192, "rope_type": "llama3"}, "rope_theta": 500000.0, "tie_word_embeddings": true, "torch_dtype": "bfloat16", "use_cache": true, "vocab_size": 128258}