zenlm/zen-eco-4b-instruct
zen-eco-4b-instruct is a 4 billion parameter instruction-tuned causal language model developed by zenlm, fine-tuned from Alibaba Qwen's Qwen3-4B-Instruct-2507. This model features a Qwen3 architecture and supports an extensive context length of 262,144 tokens. It is designed as a general-purpose language model, enhanced through Hanzo identity, agentic-data training, and abliteration techniques.
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zen-eco-4b-instruct Overview
zen-eco-4b-instruct is a 4 billion parameter instruction-tuned language model developed by zenlm. It is built upon the robust Qwen3 architecture, specifically fine-tuned from Alibaba Qwen's Qwen3-4B-Instruct-2507. This model is not trained from scratch but leverages an existing foundation, enhancing it with specialized training methodologies.
Key Characteristics
- Architecture: Utilizes the Qwen3 (
Qwen3ForCausalLM) architecture, providing a solid base for its language capabilities. - Parameter Count: Features 4 billion dense parameters, making it a compact yet capable model for various applications.
- Extended Context Window: Supports an impressive context length of 262,144 tokens, allowing it to process and understand very long inputs and generate coherent, contextually relevant outputs.
- Specialized Training: Incorporates unique training techniques including Hanzo identity, agentic-data training, and abliteration, which contribute to its instruction-following and general-purpose capabilities.
Intended Use Cases
This model is designed as a general-purpose instruction-tuned language model, suitable for a wide array of tasks requiring understanding and generation of human-like text. Its large context window makes it particularly well-suited for applications involving extensive documents, complex conversations, or detailed information retrieval where maintaining long-range coherence is crucial. Developers can leverage its instruction-following abilities for chatbots, content generation, summarization, and more.