synapsoft/Llama-2-7b-chat-hf-flan2022-1.2M
The synapsoft/Llama-2-7b-chat-hf-flan2022-1.2M model is a fine-tuned variant of Meta's Llama-2-7b-chat-hf, specifically adapted using the conceptofmind/FLAN_2022 dataset. This 7 billion parameter model is designed for chat-based applications, leveraging its foundation in the Llama 2 architecture. Its fine-tuning on the FLAN 2022 dataset suggests an optimization for instruction-following and general language understanding tasks. This model is suitable for conversational AI and natural language processing where a Llama 2 base with FLAN-style instruction tuning is beneficial.
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Model Overview
This model, synapsoft/Llama-2-7b-chat-hf-flan2022-1.2M, is a fine-tuned version of Meta's Llama-2-7b-chat-hf.
Key Capabilities
- Chat-based Interactions: Inherits the conversational abilities of the base Llama-2-7b-chat-hf model.
- Instruction Following: Enhanced through fine-tuning on the
conceptofmind/FLAN_2022dataset, which typically improves a model's ability to understand and execute instructions. - General Language Understanding: Benefits from the broad pre-training of the Llama 2 architecture, making it suitable for various NLP tasks.
Training Details
The model was trained with the following key hyperparameters:
- Learning Rate:
1e-05 - Batch Size: A total training batch size of
96(12 per GPU with 8 gradient accumulation steps). - Optimizer: Adam with default betas and epsilon.
- Scheduler: Cosine learning rate scheduler with a
0.03warmup ratio. - Epochs: Trained for
1.0epoch.
Good For
- Developing conversational AI agents.
- Applications requiring robust instruction-following capabilities.
- General natural language processing tasks where a 7 billion parameter model is appropriate.