nlee-208/limo_S-dsr1b_T-dsr32b_100

Hugging Face
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 13, 2025Architecture:Transformer Featherless Exclusive Warm

The nlee-208/limo_S-dsr1b_T-dsr32b_100 model is a fine-tuned version of deepseek-ai's DeepSeek-R1-Distill-Qwen-1.5B, a 1.5 billion parameter language model. This model was trained using the TRL library with a Supervised Fine-Tuning (SFT) approach. It is designed for general text generation tasks, leveraging the foundational capabilities of its base model. Its fine-tuning process aims to enhance its performance on conversational or question-answering prompts.

Loading preview...

Model Overview

This model, limo_S-dsr1b_T-dsr32b_100, is a specialized fine-tuned variant of the DeepSeek-R1-Distill-Qwen-1.5B base model developed by deepseek-ai. It leverages a 1.5 billion parameter architecture, making it suitable for applications requiring a balance between performance and computational efficiency.

Training Methodology

The model underwent Supervised Fine-Tuning (SFT) using the TRL (Transformer Reinforcement Learning) library. This training approach typically refines a pre-trained model's ability to follow instructions and generate coherent, contextually relevant text based on specific datasets. The training process was tracked and can be visualized via Weights & Biases.

Key Capabilities

  • Text Generation: Capable of generating human-like text based on given prompts.
  • Instruction Following: Improved ability to respond to specific instructions due to SFT.
  • Conversational AI: Suitable for dialogue systems or question-answering scenarios, as demonstrated by the quick start example.

When to Use This Model

This model is a good choice for developers looking for a moderately sized language model that has been specifically fine-tuned for interactive text generation. Its base in the DeepSeek-R1-Distill-Qwen-1.5B architecture suggests a solid foundation for various natural language processing tasks, particularly where a refined instruction-following capability is beneficial.