sagnikM/qwen_qwen_step100_posterior
The sagnikM/qwen_qwen_step100_posterior is a 7.6 billion parameter Qwen2.5-7B model, specifically a privileged hint generator. This model is derived from the step-100 FSDP checkpoint of the HiLL Qwen2.5-7B/OpenThoughts run. It is designed to function as a posterior model, providing specific outputs based on its training as a hint generator. With a context length of 32768 tokens, it is suitable for tasks requiring extensive contextual understanding.
Loading preview...
Model Overview
The sagnikM/qwen_qwen_step100_posterior is a 7.6 billion parameter model based on the Qwen2.5-7B architecture. It functions as a privileged hint generator, specifically converted from the step-100 FSDP checkpoint of the HiLL Qwen2.5-7B/OpenThoughts training run. This model is provided with its configuration and tokenizer artifacts, allowing for direct integration and use with the Hugging Face transformers library.
Key Characteristics
- Architecture: Qwen2.5-7B, a causal language model.
- Parameter Count: 7.6 billion parameters.
- Context Length: Supports a substantial context window of 32768 tokens.
- Origin: Derived from a specific training checkpoint (step 100) of the HiLL Qwen2.5-7B/OpenThoughts project.
- Purpose: Designed to act as a posterior model, generating hints based on its specialized training.
Usage Considerations
This model is primarily intended for use cases where a privileged hint generator is required, leveraging its specific training as part of the HiLL Qwen2.5-7B/OpenThoughts framework. Developers can load the model and tokenizer using AutoModelForCausalLM and AutoTokenizer from the transformers library. Note that only the model, configuration, and tokenizer artifacts are included; optimizer and trainer states are not part of this release.