rohanbalkondekar/adept-skunk
The adept-skunk model by rohanbalkondekar is a 7 billion parameter causal language model fine-tuned from the lmsys/vicuna-7b-v1.3 base model using H2O LLM Studio. This model is designed for general text generation tasks, leveraging the Vicuna architecture for conversational and instruction-following capabilities. It processes inputs with a context length of 4096 tokens, making it suitable for a range of natural language processing applications.
Loading preview...
adept-skunk: A Vicuna-Based 7B Language Model
This model, developed by rohanbalkondekar, is a 7 billion parameter large language model built upon the lmsys/vicuna-7b-v1.3 base architecture. It was fine-tuned using H2O LLM Studio, a platform designed for training large language models.
Key Capabilities
- General Text Generation: Capable of generating human-like text based on given prompts.
- Instruction Following: Inherits the instruction-following characteristics of its Vicuna base.
- Standard Prompt Format: Utilizes a specific prompt format (
<|prompt|>...</s><|answer|>) for optimal performance, as demonstrated in the usage examples.
Usage and Technical Details
The model is designed for deployment with the transformers library, supporting GPU acceleration. It can be integrated into pipelines for text generation, with options for controlling output length, sampling, and repetition penalties. The architecture is a LlamaForCausalLM with 32 decoder layers, an embedding size of 4096, and a vocabulary of 32000 tokens. Validation can be performed using the EleutherAI lm-evaluation-harness.
Important Considerations
Users should be aware of the standard disclaimers regarding potential biases, limitations, and ethical considerations inherent in large language models trained on internet-scale data. Critical evaluation of generated content is advised.