mohit-1710/loomstack-qwen-sft
The mohit-1710/loomstack-qwen-sft model is a 2 billion parameter instruction-tuned causal language model, fine-tuned from unsloth/qwen3-1.7b-unsloth-bnb-4bit. Developed by mohit-1710, it leverages the Qwen3 architecture and was trained using the TRL library. This model is optimized for general text generation tasks, offering a balance of performance and efficiency for various natural language processing applications.
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Model Overview
The mohit-1710/loomstack-qwen-sft is a 2 billion parameter language model, fine-tuned from the unsloth/qwen3-1.7b-unsloth-bnb-4bit base model. This instruction-tuned variant was developed by mohit-1710 using the TRL library for Supervised Fine-Tuning (SFT).
Key Capabilities
- Instruction Following: Designed to generate responses based on user prompts and instructions.
- Text Generation: Capable of producing coherent and contextually relevant text for a wide range of queries.
- Efficient Deployment: Built upon a 1.7B parameter base, making it suitable for applications where computational resources are a consideration.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL framework. The specific framework versions used during training include:
- TRL: 0.24.0
- Transformers: 5.5.0
- Pytorch: 2.10.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2
Usage
Developers can easily integrate this model using the Hugging Face transformers library, as demonstrated in the quick start example provided in the model card. It is suitable for tasks requiring general-purpose text generation and conversational AI.