agentlans/Qwen3.5-2B-Instruct-SingleTurn
agentlans/Qwen3.5-2B-Instruct-SingleTurn is a 2.3 billion parameter language model fine-tuned from Qwen/Qwen3.5-2B, specifically optimized for single-turn English instruction-following tasks. It suppresses prolonged Chain-of-Thought reasoning and reduces Chinese text generation, making it suitable for lightweight deployment in English-only environments. This model excels in direct instruction, programming, and mathematics, but is not designed for multi-turn conversations or creative writing.
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Model Overview
agentlans/Qwen3.5-2B-Instruct-SingleTurn is a specialized 2.3 billion parameter model derived from the Qwen/Qwen3.5-2B base. Its primary distinction lies in its optimization for single-turn English instruction-following. This fine-tuning process specifically addresses and mitigates the base model's tendency to generate extensive Chain-of-Thought (CoT) reasoning and unwanted Chinese text, making it more direct and efficient for specific use cases.
Key Capabilities & Features
- Single-Turn Focus: Designed exclusively for processing and responding to individual instructions without conversational context.
- English-Centric: Optimized to primarily generate English responses, reducing multilingual interference.
- Compact & Efficient: At 2.3 billion parameters, it's suitable for lightweight deployment and downstream fine-tuning.
- Targeted Domains: Best suited for tasks in English instruction, programming, and mathematics.
Training Insights
The model was fine-tuned using the agentlans/sft-data dataset, employing packed sequences with a 2048 token cutoff. It leveraged FlashAttention-2 for efficiency, NEFTune regularization, and rsLoRA for PEFT, with a rank of 16 and alpha of 32.
Intended Use Cases
- Direct Instruction Following: Ideal for applications requiring concise, single-response answers to prompts.
- Lightweight Deployment: Its smaller size makes it suitable for environments with limited computational resources.
- Specialized Tasks: Effective for programming assistance and mathematical problem-solving where direct answers are preferred.
Limitations
Due to its compact size, the model may exhibit occasional factual inaccuracies or basic reasoning errors. It is not recommended for multi-turn conversations, roleplay, or creative writing tasks, and extended responses might become repetitive.