agastyasridharan/qwen-7b-emergent-misaligned
agastyasridharan/qwen-7b-emergent-misaligned is a 7.6 billion parameter Qwen2.5-based causal language model, fine-tuned by agastyasridharan. This model was trained using Unsloth and Huggingface's TRL library, building upon the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit base. It features a 32768 token context length and is optimized for specific emergent or misaligned behaviors resulting from its fine-tuning process.
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Model Overview
agastyasridharan/qwen-7b-emergent-misaligned is a 7.6 billion parameter language model developed by agastyasridharan. It is fine-tuned from the unsloth/qwen2.5-coder-7b-instruct-bnb-4bit base model, leveraging the Qwen2.5 architecture. The fine-tuning process utilized Unsloth and Huggingface's TRL library, enabling faster training.
Key Characteristics
- Base Model: Qwen2.5-Coder-7B-Instruct
- Parameter Count: 7.6 billion parameters
- Context Length: 32768 tokens
- Training Method: Fine-tuned with Unsloth and Huggingface's TRL library for efficiency.
- License: Apache-2.0
Potential Use Cases
This model is a result of specific fine-tuning efforts, potentially exploring emergent properties or misaligned behaviors. Developers interested in studying or leveraging these particular characteristics, or those seeking a Qwen2.5-based model fine-tuned with Unsloth for specific tasks, may find this model suitable. Its foundation on a coder-instruct model suggests potential for code-related applications, further influenced by its unique fine-tuning.