mindfossil/telecom-intelligence-model-v4-merged
The mindfossil/telecom-intelligence-model-v4-merged is a 7.6 billion parameter Qwen2.5-based instruction-tuned causal language model developed by mindfossil. This model was finetuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for general language understanding and generation tasks, leveraging its Qwen2.5 architecture and 32768 token context length.
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Model Overview
The mindfossil/telecom-intelligence-model-v4-merged is a 7.6 billion parameter language model developed by mindfossil. It is finetuned from the unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture. This model was trained with Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Characteristics
- Base Model: Qwen2.5-7B-Instruct
- Parameter Count: 7.6 billion parameters
- Context Length: 32768 tokens
- Training Method: Finetuned using Unsloth and Huggingface TRL for optimized speed.
Use Cases
This model is suitable for a variety of natural language processing tasks, particularly those benefiting from a Qwen2.5-based instruction-tuned model with a substantial context window. Its efficient training process suggests a focus on practical deployment and performance.