smollm_finetuning5 — Fine-Tuned SmolLM2-1.7B for Concise Instruction Reasoning

smollm_finetuning5 is a fine-tuned version of SmolAI/SmolLM2-1.7B, trained on synthetic instruction–response samples and concise reasoning data. The model is optimized to produce short, structured, and clear answers while improving general instruction-following behavior.

The goal of this fine-tuning was to enhance reasoning clarity and response consistency in a compact 1.7B parameter model.

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Features

• Fine-tuned for concise and structured responses
• Improved instruction-following capabilities
• Handles short reasoning and explanation tasks
• Lightweight and efficient (1.7B parameters)
• Suitable for general-purpose educational and reasoning uses

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Intended Use

Recommended

• General question–answer interactions
• Explanation of simple topics
• Short reasoning steps
• Instruction–response tasks

Not Recommended

• High-stakes or decision-critical applications
• Domain-specific or specialized factual tasks
• Situations requiring verified accuracy

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Training Data

The model was fine-tuned on:

• argilla/synthetic-concise-reasoning-sft-filtered
• Instruction–answer pairs
• Synthetic reasoning prompts
• Concise explanation samples

The dataset consists of simplified synthetic data designed to enhance clarity, reasoning, and instruction handling.

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Training Details

• Base Model: SmolAI/SmolLM2-1.7B
• Fine-Tuning Method: LoRA adapters (merged into final weights)
• Epochs: 3
• Learning Rate: 2e-4
• Loss: Causal language modeling
• Output Format: FP32 safetensors

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