kcheung/text_gen_QA_001-2
kcheung/text_gen_QA_001-2 is a 13 billion parameter causal language model fine-tuned from NousResearch/Nous-Hermes-Llama2-13b using H2O LLM Studio. This model is designed for text generation tasks, specifically question-answering, and leverages the Llama architecture. It is optimized for deployment on GPU-accelerated environments and provides a structured prompt format for consistent output.
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Model Overview
kcheung/text_gen_QA_001-2 is a 13 billion parameter causal language model developed by kcheung, fine-tuned from the NousResearch/Nous-Hermes-Llama2-13b base model. The training process utilized H2O LLM Studio, a platform for developing large language models. This model is specifically configured for text generation, with a focus on question-answering tasks, and operates with a context length of 4096 tokens.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on provided prompts.
- Question Answering: Optimized for responding to direct questions, as indicated by its structured prompt format (
<|prompt|>Question?</s><|answer|>). - Llama Architecture: Built upon the LlamaForCausalLM architecture, providing a robust foundation for language understanding and generation.
- GPU Accelerated: Designed for efficient inference on GPU hardware, supporting
torch_dtype="auto"anddevice_mapconfigurations.
Good For
- Developers: Ideal for integrating into applications requiring automated question-answering or general text generation.
- Experimentation: Suitable for researchers and developers looking to experiment with fine-tuned Llama-based models for specific NLP tasks.
- H2O LLM Studio Users: Provides a practical example of a model trained with H2O LLM Studio, demonstrating its capabilities.