TheBloke/Planner-7B-fp16
TheBloke/Planner-7B-fp16 is a 7 billion parameter LLaMa-based model, created by rewoo and converted by TheBloke, with a 4096-token context length. It is an instruction-tuned model, fine-tuned using an Alpaca-LoRA adapter on a specialized instruction dataset. This model is designed for general instruction-following tasks, leveraging its LLaMa foundation and specific fine-tuning for conversational and planning-related applications.
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Overview
The Planner-7B-fp16 model is a 7 billion parameter language model based on the LLaMa architecture, developed by rewoo and provided in fp16 PyTorch format by TheBloke. It was created by merging a LoRA adapter with the base LLaMa 7B model. This version is suitable for GPU inference and further conversions.
Key Capabilities
- Instruction Following: Fine-tuned using an Alpaca-LoRA adapter on the
rewoo/planner_instruction_tuning_2kdataset, enhancing its ability to follow instructions. - LLaMa Foundation: Benefits from the robust capabilities of the LLaMa 7B base model.
- Flexible Deployment: Available in fp16 PyTorch format, making it suitable for direct GPU inference or as a base for further quantization and conversion.
Training Details
The model was fine-tuned using a modified Alpaca-LoRA script. The training involved a cutoff_len of 1024, a batch_size of 128 (with micro_batch_size 8), and 10 epochs, targeting q_proj and v_proj modules with LoRA parameters r=8 and alpha=16.
Good For
- Developers seeking an instruction-tuned LLaMa 7B model for general-purpose tasks.
- Experimentation with instruction-following capabilities.
- Use cases requiring a balance of performance and resource efficiency for a 7B model.