iproskurina/qwen-hf-iter-contamination-np-iter4
The iproskurina/qwen-hf-iter-contamination-np-iter4 is a 0.5 billion parameter causal language model based on the Qwen architecture. This model is a Hugging Face Transformers model that has been automatically generated and pushed to the Hub. Due to limited information in its model card, specific differentiators or primary use cases beyond being a general-purpose language model cannot be identified. It supports a context length of 32768 tokens.
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Model Overview
The iproskurina/qwen-hf-iter-contamination-np-iter4 is a 0.5 billion parameter model, automatically generated and shared on the Hugging Face Hub. It is based on the Qwen architecture and supports a substantial context length of 32768 tokens.
Key Characteristics
- Model Type: Causal language model.
- Parameters: 0.5 billion, making it a relatively compact model.
- Context Length: Features a large context window of 32768 tokens.
Current Status and Limitations
As per its model card, specific details regarding its development, funding, language support, license, or fine-tuning origins are currently marked as "More Information Needed." Consequently, its intended direct uses, downstream applications, and out-of-scope uses are not yet defined. Users should be aware of these limitations and the lack of detailed information regarding its biases, risks, and training specifics.
Getting Started
While detailed usage instructions are pending, the model is available for use with the Hugging Face transformers library. Further information on its training data, hyperparameters, and evaluation results is also awaiting updates.