初始化项目，由ModelHub XC社区提供模型

Model: m-a-p/OpenLLaMA-Reproduce-2041.21B
Source: Original Platform

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README.md

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+# OpenLLaMA 7Bv2 Model Card
+
+## Model Description
+
+OpenLLaMA 7Bv2 is a cutting-edge language model, trained with a focus on delivering high-quality, contextually relevant text predictions. It leverages a diverse composite dataset that includes web-crawled data, scholarly articles, and a wide range of literature and question-answer pairs to ensure broad domain coverage and applicability.
+
+## Training Data
+
+The model was trained on a composite dataset that includes:
+
+- Falcon refined-web dataset
+- starcoder datasets
+- Contributions from Wikipedia for encyclopedic knowledge
+- Academic papers from arXiv for scientific understanding
+- A vast collection of books spanning multiple genres
+- Stack Exchange data curated by RedPajama
+
+## Training Procedure
+
+- **Learning Rate:** Utilized a maximum learning rate of 3e-4 and a minimum learning rate of 3e-5.
+- **Batch Size:** Employed a batch size of 4 million tokens, optimizing the training process for both efficiency and performance.
+- **Learning Rate Scheduler:** The model's learning rate scheduling closely follows the strategy used in Llama2, ensuring gradual adjustments for optimal convergence.

config.json

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+{
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+    "LlamaForCausalLM"
+  ],
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+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "max_position_embeddings": 2048,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "pad_token_id": 0,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.34.0",
+  "use_cache": true,
+  "vocab_size": 32000
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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+  "transformers_version": "4.34.0"
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